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Comparative Study BMJ. 2026 Sep 18:394:e100624. doi: 10.1136/bmj-2026-100624.

Survival benefit of deceased donor kidney transplantation versus continued dialysis: international target trial emulation

Rachel Hellemans, Nicholas C Chesnaye, Anneke Kramer, Edouard L Fu, Miha Arnol, Anders Åsberg, Claus Bistrup, Pietro Manuel Ferraro, Kristian Heldal, Jaakko Helve, Camille Legeai, James Medcalf, Dorothea Nitsch, Laia Oliveras, María José Pérez-Sáez, Belén Ponte, Emilie Savoye, Mårten Segelmark, Shaminie Shanmugaranjan, Søren S Sørensen, Roser Torra, Alberto Ortiz, Kitty J Jager, Vianda S Stel 

PMID: 42759976

DOI: 10.1136/bmj-2026-100624

Introduction

Consider a conversation at a transplant clinic. A 70-year-old patient with diabetes who is receiving dialysis has been offered a kidney from a deceased older donor. After hearing about the operation, medications, and follow-up care, the patient asks, “Will this kidney help me live longer?”

There is a lot riding on that little word “this”. The patient wants to understand the likely benefit of kidney transplantation, given their age and medical history. Individualized medical recommendations are the goal, but they are often difficult in the context of evidence based medicine, where study populations often don’t reflect the patient in front of you.

The evidence supporting transplantation, in most patients with ESRD, is substantial. A 2022 systematic review by Chaudhry et al. (BMJ 2022), with 48 observational studies (but no RCTs) and 1,245,850 patients found a long-term survival advantage associated with transplantation in 44 studies (92%). Across the 18 studies included in the meta-analysis, the pooled hazard ratio for mortality was 0.45 (95% CI 0.39–0.54). However, results varied markedly between studies (I²=95.3%), and 11 studies identified subgroups without a statistically significant benefit, implying that there could be “some subgroups that may lack a survival benefit.” 

The transplant population has also been getting older. In an ERA Registry study of 99,543 first kidney transplant recipients across 12 European countries, the proportion aged 65 or older increased from 18% in 2010 to 28% in 2019. That analysis included both living and deceased donor transplantation (Boenink et al, NDT 2023). The donor population was changing as well: among 24,177 deceased donor transplants in seven European countries or regions, the median donor age increased from 50 to 55 years between 2005 and 2015, while at the same time the proportion involving donation after circulatory death increased from 19% to 37%. These trends make the combination of recipient characteristics and donor characteristics increasingly relevant to the survival discussion (Pippias et al., NDT 2021).

Dialysis outcomes have also improved in recent years. In DOPPS, international data covering 1999–2015, adjusted modelled survival time increased by 13% per decade in four European countries, 12% in Japan, and 47% in the United States. These estimates suggest that hemodialysis patients were living longer than those treated in earlier eras, reinforcing the need to revisit transplant-versus-dialysis comparisons based on older data (McCullough et al., AJKD 2025). However, these advantages don’t necessarily extend to the extreme elderly. Another recent study showed that in patients 75 years and older, on average, only lived an additional 77 days versus those who chose conservative (non-dialysis) management of advanced kidney disease (Chou et al, Kidney Med, 2022). 

There remains uncertainty about the size and timing of the benefit for particular recipient-donor combinations. How does the comparison change with advancing age, diabetes, cardiovascular disease, donor quality, and donation after brain versus circulatory death? Answering these questions needs thoughtful study design. Patients have to survive long enough to receive a transplant; an analysis that mistakenly credits their earlier dialysis survival to transplantation can introduce immortal time bias. Target trial emulation helps address this problem by first specifying the hypothetical randomised trial and then aligning eligibility, treatment assignment, and follow-up in the observational data. In this way, it applies the logic of a clinical trial to registry research, while still depending on how completely the available data captures clinically important differences between patients (Hernán and Robins, American Journal of Epidemiology, 2016; Fu et al., BMJ 2026).

In a sea of data supporting the survival benefits of kidney transplantation, clinicians still need clearer directions when navigating decisions for higher-risk patients. Hellemans and colleagues, using international registry data, set out to address that uncertainty by examining recipient age, comorbidities, and donor characteristics together. Their question was specific: for a patient with a specific risk profile, receiving a specific type of donor kidney, how much survival benefit could transplantation offer compared with continuing dialysis?

The Study

Methods

Data collection

The study used donor and transplant-related data from the European Renal Association (ERA) Registry, comprising five national registries: Catalonia (Spain), Denmark, France, Norway, and England/Wales/Northern Ireland (UK).

Recipient variables were: age, sex, cause of kidney failure, dates of waitlisting, dialysis initiation, kidney transplantation, and date and cause of death. 

Diabetes was defined as diabetes either as a primary renal disease or as a comorbidity. Cardiovascular disease was defined as a history of ischaemic heart disease, peripheral vascular disease, or cerebrovascular disease. Both were recorded at waitlisting only. Donors were classified as expanded criteria donors (ECD) if aged ≥60 years, or 50–59 years with at least two of the following: arterial hypertension, cerebrovascular cause of death, or pre-retrieval serum creatinine >1.5 mg/dL before retrieval. All others were standard criteria donors (SCD). 

The retrieval type was donation after brain death (DBD) or donation after circulatory death (DCD). Variables required to calculate the Kidney Donor Risk Index (KDRI) were collected. KDRI refers to the relative risk of kidney graft failure for a specific donor compared to the median (average) donor. Calculation includes age, height, weight, hypertension and diabetes history. Denmark and Norway were excluded from all DCD analyses because DCD transplants were absent or rare.

Target trial specification and emulation

Target trial emulation applies the design of a randomized trial to observational data. The investigators first write the protocol of the trial they would ideally conduct, then reproduce each element of it with registry data (Hernán and Robins, Am J Epidemiol, 2016; Fu et al, BMJ, 2026). The ideal trial here would randomize waitlisted patients on dialysis. Each time a kidney became available the authors would then compare survival depending upon whether the patient proceeded to transplantation or continued dialysis. The table below sets the target trial against its emulation; each row in which the two differ is a potential source of bias.

The question. Among patients who have spent the same time on waitlists while on dialysis, does receiving a deceased donor kidney this month, rather than remaining on dialysis, change survival? 

Step 1- entry. Patients enter the dataset when they are both waitlisted and receiving dialysis. Time is counted in months from that point. 

Step 2- one trial per month. For each of the 60 months after waitlisting, a new trial is started among patients still eligible: alive, waitlisted, on dialysis, and not yet transplanted. Patients were transplanted during that month from the transplant arm; all other eligible patients form the continued dialysis arm. A patient re-enters every monthly trial until transplantation, death, or censoring. A patient transplanted in month 11 therefore contributed ten observations to the dialysis arm (months 1-10) and one to the transplant arm. The 60 trials were then pooled for analysis; the 64,036 patients generated 1,491,537 person-trials.

Step 3- a common time zero. In each trial, eligibility is assessed, treatment is assigned, and follow-up starts at the same moment. This prevents three time-related biases that affect conventional comparison of transplanted and non-transplanted patients and gives a fourth property:

• No immortality bias. If follow-up started at waitlisting and patients were classified by eventual transplantation, the wait would be counted as a transplant follow-up, although a patient cannot die during it and still be counted as a recipient. With a median wait of 441 days (IQR 181-853), recipients are not credited with the survival they accrued while waiting. 

• No lead time bias. If the arms started follow-up at different points in the disease course, survival would differ for that reason alone. In the emulation, both arms start at the same point after waitlisting.

• No depletion-of-susceptible bias. Recipients are, by definition, survivors of the wait, whereas the frailest candidates die early on dialysis. In the emulation, only patients alive at month X are compared, so both arms have survived equally long.

• Exact matching on waiting time. Patients in the two arms have identical time on the waiting list. 

The design does not address unmeasured confounding due to the fact that healthier candidates remain more likely to be transplanted. 

Step 4- emulating randomization. Assignment was not random. Receipt of a kidney depends on allocation rules, blood group, sensitization, and clinical judgment. Treatment was therefore taken as observed (“pseudo-randomization”), and differences in measured baseline characteristics between arms were removed by weighting. Differences in unmeasured characteristics, such as frailty, are not removed.

Step 5- one emulation per recipient-donor combination. The whole procedure was repeated for each of four donor categories (SCD/DBD, SCD/DCD, ECD/DBD, ECD/DCD) in each of four patient groups (all patients; diabetes; CVD; neither), giving (four by four) 16 separate target trials. Each compares recipients of one donor category with dialysis patients from the same patient group. Because each trial has its own comparator, results cannot be compared directly between donor categories. Further emulation stratified donors by KDRI and by donor age.

 Reporting. The study followed the Transparent Reporting of Observational Studies Emulating a Target Trial (TARGET) guideline.

Study Population

Inclusion Criteria:

• Adults ≥20 years 

• Receiving dialysis and waitlisted for a first deceased donor kidney-only transplant between 1 January 2000 and 31 December 2019.

Exclusion Criteria: 

• Preemptive kidney transplant

• On the waiting list for more than 10 years without receiving a kidney transplant

• Started dialysis more than 10 years before waitlisting

• Permanently delisted

• Missing diabetes data

• For the analyses stratified by cardiovascular disease status, patients with missing data on cardiovascular disease. 

Treatment strategies and treatment assignment

Transplantation was a point treatment strategy, a one-time intervention, with the transplant received at the start of that monthly trial. Continued dialysis was a sustained treatment strategy, requiring the patients to be on dialysis at the start of the trial and remain on it throughout follow-up. 

This setup was an observational analogue of a per-protocol comparison, estimating the survival outcomes associated with transplant, including perioperative and early post-transplant risks, while isolating the effect of continued dialysis in the comparison arm.

Follow-up 

Follow-up started at treatment assignment and ended at death, recovery of kidney function, or loss to follow-up, or administrative censoring, whichever occurred first.  Administrative censoring occurred 5 years after waitlisting (dialysis arm) or 5 years after transplantation (transplant arm), and in all patients on December 31, 2019.

The two groups were handled differently if the treatment changed. In the transplant group, patients remained under follow-up even if their graft later failed and they returned to dialysis, fitting the point treatment strategy. In the dialysis group, patients were censored if they received a deceased or living donor transplant, as continuing dialysis was a sustained treatment strategy.

Statistical Analysis

Matching. Because a new trial started each month, transplanted patients were compared with patients who had spent the same time on the waiting list while on dialysis, giving exact matching on waiting time at assignment.

Confounding. Inverse probability of treatment weights (IPTW) were estimated by logistic regression of treatment on age at trial start, sex, primary renal disease, calendar year of waitlisting, time on dialysis before waitlisting, diabetes, and country. Continuous variables were modeled with restricted cubic splines. Covariate balance before and after weighting was assessed with absolute standardized mean differences, with 0.1 as the threshold for acceptable balance. CVD was not included in the main models; a sensitivity analysis added it in patients with available data. 

Informative censoring. Censoring of dialysis patients at transplantation may be informative, because transplantation depends on health status. Inverse probability of censoring weights (IPCW) were estimated by logistic regression of censoring on the same baseline covariates. 

Weights. IPTW and IPCW were stabilized using the marginal probabilities of transplant or censoring, multiplied into a single weight, and truncated at the 99th centile to limit the influence of extreme values. 

Survival model. A weighted flexible parametric (Royston-Parmar) survival model was fitted, with a restricted cubic spline for the baseline hazard and time-varying coefficient for treatment, allowing the hazard ratio to change over follow-up and capture early post-transplant mortality.  Age at trial start was also modelled with a restricted cubic spline and an age x treatment interaction, allowing the treatment effect to vary non-linearly with age. The model yielded marginal 5-year survival probabilities, time-varying hazard ratios, and restricted mean survival time (RMST) at 5 years, the mean time alive during the first 5 years. Results are reported at 5-year age intervals.  Confidence intervals for all survival estimates were based on the 2.5th and 97.5th centiles from the distribution of 1000 parametric bootstrap resamples. 

Subgroup analyses were restricted to adults aged 40 or older because there were relatively few younger transplant recipients.

Sensitivity analyses. Crude (unweighted) analyses of waitlisting in 2000-2009 vs 2010-2019 (pre-2010 DCD analyses include UK patients only); men vs women; patients with both diabetes and CVD; and waitlist duration up to 1, 1-2, and 2-5 years; additional adjustment for CVD.

Results

Baseline Demographics:

Of the 64,036 patients included in the trial (mean age 55, with 64.1% male), most of the participants were from the UK and France, with the leading causes of renal failure being glomerulonephritis (20.9%) and diabetes (18.3%).  

Within 5 years, 61.3% received a deceased-donor kidney transplant after a median waiting period of 441 days: SCD/DBD 43.1%, SCD/DCD 11%, ECD/DBD 37.7%, ECD/DCD 8.2%. Most elderly recipients received ECD transplants; among the 1580 recipients ≥75 years, 1264 received ECD/DBD, 184 ECD/DCD, and 132 SCD kidneys; the median KDRI was 2.6 (Tables S5, S7).

Transplantation was associated with a survival advantage across many recipient ages and donor categories, but the picture became less certain in some older patient groups. 

SCD/DBD

Standard criteria donor kidney transplantation was associated with a significant five-year survival benefit over the dialysis across all recipient ages. Even at the age 75, transplantation significantly reduced mortality risk (HR 0.68; 95% CI 0.59–0.79). This benefit persisted among recipients with diabetes (HR 0.58; 95% CI 0.39–0.85). In patients with cardiovascular disease, a similar trend was observed towards the benefit, though the hazard ratio did not reach statistical significance (HR 0.71; 95% CI 0.47–1.06)

SCD/DCD

Standard criteria donor kidney transplantation after circulatory death was associated with improved survival over continued dialysis across recipient ages. At age 75, mortality was 39% lower (HR 0.61, 95% CI 0.44–0.84), and five-year survival probability was 17% higher (95% CI 10%–23%): 70% (95% CI 64%–76%) versus 53% (95% CI 53%–54%). Results for recipients aged 75 with diabetes (HR 0.69, 95% CI 0.31–1.40) or cardiovascular disease (HR 0.75, 95% CI 0.34–1.75), point estimates suggested a survival benefit, but the findings were inconclusive because of small subgroup numbers (n=45) and confidence intervals that crossed the null value of 1.

ECD/DBD

Expanded criteria donor kidney transplantation after brain death was associated with improved survival over continued dialysis, although the benefit decreased and statistical uncertainty increased with age. At age 75, mortality was 26% lower (HR 0.74, 95% CI 0.66–0.86), and five-year survival probability was 12% higher (95% CI 9%–15%): 66% (95% CI 63%–68%) versus 53% (95% CI 53%–54%). The benefit remained significant in recipients with diabetes (HR 0.70, 95% CI 0.55–0.86), but was inconclusive in those with cardiovascular disease (HR 0.82, 95% CI 0.65–1.03), where CI crossed 1.

ECD/DCD

For expanded criteria donor kidney transplantation after circulatory death, the survival benefit declined more sharply, and statistical uncertainty increased with age. At age 75, five-year survival did not differ significantly from that with continued dialysis: 59% (95% CI 51%–66%) versus 55% (95% CI 54%–55%). The mortality estimate was close to the null value of 1 (HR 0.97, 95% CI 0.70–1.33). These findings were consistent across diabetes and cardiovascular disease subgroups.

Older candidates with cardiovascular disease, therefore, emerge as a group in whom the survival advantage warrants particularly careful interpretation. These estimates do not demonstrate consistently poor transplant outcomes; several point estimates favour transplantation, while their confidence intervals remain compatible with both benefit and harm. Therefore, any survival advantage depends on the recipient’s clinical profile and the donor kidney being considered.

Donor Age

The transplant survival benefit decreased with advancing donor age across all recipient categories, with transplant and dialysis survival curves converging for donor ages above 70 years overall and from 60 years among recipients with cardiovascular disease.

Paradox: Risk vs Gain- The Early Post-Transplant Mortality

During the first months after transplantation, mortality risk was higher than with continued dialysis, particularly in older recipients and those receiving expanded criteria donor kidneys. At age 75, peak early post-transplant mortality hazard ratios, derived from modelled curves, were 2.0 for SCD/DBD, 1.7 for SCD/DCD, 2.1 for ECD/DBD, and 3.0 for ECD/DCD.

Time gained: months of life gained within 5 years (RMST)

At age 75, compared with continued dialysis, the adjusted average survival time gained over five years was 2.7 months (95% CI 1.6–3.8) for standard criteria donors after brain death, 4.1 months (95% CI 1.7–6.0) for standard criteria donors after circulatory death, and 1.8 months (95% CI 0.8–2.8) for expanded criteria donors after brain death. Expanded-criteria donors after circulatory death showed no clear advantage (−1.4 months; 95% CI, −4.4 to 1.2).

KDRI

The transplant survival benefit decreased, and became non-significant in the oldest recipients, with kidneys in the highest KDRI quintile; in all patients, the HR at recipient age was 0.76 (0.55-1.04) for the fourth of five quintiles (Fig S4). In patients with CVD, no KDRI fifth quintile reached significance at age 75; HRs rose from 0.63 (0.33-1.14) in the lowest to 0.97 (0.63-2.45) in the highest fifth. With donors aged ≥ 70, the HR was 0.81 (0.66-0.99) at recipient age 75 and 1.13 (0.82-1.57) at 80; in patients with diabetes, HRs at 75 ranged from 0.61 to 0.84 across donor age groups, without a consistent trend (Fig S5, Table S12). 

Sensitivity analyses

Findings were consistent across transplant periods (2000–09 versus 2010–19) and largely comparable between men and women. Longer waitlist duration was associated with a modestly greater transplant survival benefit, which could potentially reflect poorer survival on dialysis. Adjustment for cardiovascular disease had little effect on the results.

Discussion

Deceased donor kidney transplantation (DDKT) generally is associated with survival advantage over dialysis in the majority of clinical scenarios, but the trial reveals a critical gradient, the benefit diminishes as the donor quality declines and recipient age increases. Standard criteria donor transplantation, including either brain death or circulatory death, consistently confers a 5-year survival benefit across all age groups; for SCD/DCD kidneys in the oldest patients with diabetes or CVD, estimates were imprecise because such transplants were rare. Conversely, the survival advantage of expanded criteria donor declines progressively with advancing recipient age, most steeply for ECD/DCD kidneys. 

The over-75 tail

Loss of benefit follows a combination of recipient age, cardiovascular disease, and donor quality, not an age threshold. At 75, no benefit could be shown for ECD/DCD kidneys (HR 0.97, 0.70-1.33), for ECD/DBD kidneys in CVD (0.82, 0.65-1.03), or for any KDRI fifth in patients with CVD. With donors aged 70 or older, the benefit seen at recipient age 75 (0.81, 0.66-0.99) was absent at 80 (1.13, 0.82-1.57). The ECD/DCD interval is compatible with 30% lower or 33% higher mortality: it shows no detectable benefit, no absence of benefit. 

These tail estimates are hypothesis-generating. Recipients aged 75 or older made up 4% of transplants (1,580 of 39,244); only 184 received ECD/DCD kidneys, with about ten deaths in each comorbidity subgroup, so homogeneity across subgroups is not demonstrated. Estimates at 75-85 years come from an age spline that borrows information from younger recipients. Selection is strongest at these ages and acts through unrecorded factors such as frailty, which doubles post-transplant mortality (McAdams-DeMarco MA et al, Am J Transplant, 2015). The ECD/DBD estimate at 75, based on 1,264 recipients, is more robust (HR 0.74, 0.66-0.86).

Early mortality explains why gains in time are small. Peak early hazards at 75 were 1.7 to 3 times those on dialysis (Fig S7), so a 15-point gain in 5-year survival with SCD/DBD kidneys equals 2.7 months of life. With 5-year dialysis survival of 54% at 75, the 5-year window covers much of the remaining life expectancy: differences of -1.4 to + 4.1 months are close to the whole survival effect. Outcomes that matter more at this age, such as hospital admissions, infections, functional decline, time at home and quality of life, were not measured. 

Comparison with other studies
This is the largest trial emulation of the question. Earlier emulation from France (7-month gain in life expectancy at 10 years; Lenain R et al, Epidemiology, 2021), Austria (benefit at all ages; Strohmaier S et al, JAMA Netw Open, 2022), and the US (5-year benefit at ≥ 75 years; Leeaphorn N et al, AJKD, 2026) did not stratify by donor quality. An Australian study found a 10-month gain over 10 years with the lowest-quality 10% of kidneys in patients aged ≥60, but pooled all older patients (Zhu L et al, JAMA Intern Med, 2025). Gill et al. found long-term benefit even with ECD kidneys in 25,468 US candidates aged over 65 years, but measured benefit from listing and could not separate DCD donors (Gill JS et al, Am J Transplant, 2013). Studies that ignored immortal time overestimated the benefit (Chaudhry et al, BMJ, 2022).

Strengths

The major strength is that this was a multinational study including a vast dataset of 64000 patients over 5 European registries. The design removes immortal time, lead time, and depletion-of-susceptibles bias and matches arms exactly on waiting time. Results are stratified by single year of age, comorbidity, donor quality, and retrieval type. Reporting follows TARGET, and the code is public. 

Limitations acknowledged by the authors

 Confounders were measured at baseline only; frailty, temporary inactivation, hospital admissions, and evolving CV risk were not captured, and residual confounding probably overestimates benefit. Temporary delisting was not accounted for. Donor categories are coarse, and HLA sensitisation, preservation techniques, and immunosuppression were unavailable. Some combinations were rare. Follow-up was limited to 5 years without multistate modelling. Results may vary by period, region, and centre, and apply only to waitlisted patients. Donor types cannot be compared with each other, because each has its own comparator.

Critical appraisal

First, the contrast in outcomes is measured in transplantation versus never being transplanted (Table S1). The editorial describes it as accepting an organ at a decision point (Kaewput and Patel, BMJ, 2026), but declining an offer to wait for a better one. This scenario describes a completely different strategy, estimated by neither (Husain SA et al, JAMA Netw Open, 2019| Jay CL et al, Transplantation, 2017). 

Second, confounding was incompletely controlled. After weighting, age and country remained imbalanced in several subgroups, and transplant-arm weights reached 21.7-34/7 before truncation (Fig. S3). DCD transplants were 82-87% British (Table S4A), so the DBD-DCD contrast partly reflects countries. CVD, missing 19.5%, was not in the main models. An unmeasured confounder with a risk ratio of about 1.5-1.6 could explain the lower confidence limits of the SCD and ECD/DBD benefits (E-values; reviewers’ calculation, VanderWeele TJ, Ding P, Ann Intern Med, 2017). For marginal kidneys, bias could also run against transplantation if they are offered to candidates with a poor waitlist prognosis.

Third, the “threefold” early risk is the peak of a modelled curve without a confidence interval. Several hundred interval estimates are reported, and variance estimation across 1.49 million correlated person-trials is not described. 

Fourth, the supplement contains errors. The table S11 repeats the fourth KDRI fifth as the fifth, the ECD/DBD HR is 0.74 in the text and 0.76 in table S8, and the methods and legends name different calendar-year confounders. 

Overall, the direction and gradient of the findings are credible; the ECD/DCD result at ≥ 75 years should be reported as uncertain. 

Implications for practice

Older candidates do receive deceased donor kidneys: all 1580 recipients aged ≥ 75 years here are considered “old”, and the Eurotransplant Senior Program allocates kidneys from donors ≥ 65 to recipients ≥ 65 (Frei U et al, Am J Transplant, 2008). But longevity matching keeps the best kidneys from them: only 132 (8.4%) recipients aged ≥ 75 received SCD kidneys. In the US, about half of candidates aged ≥ 70 were projected to die before receiving a deceased donor kidney (Schold JD et al, CJASN, 2009). The realistic choice is a living donor kidney, with outcomes comparable to or better than SCD transplantation (Patel K et al, Transpl Int, 2024| Toapanta N et al, Transpl Int, 2024), or a lower-quality deceased donor kidney versus continued waiting (Jay CL et al, Transplantation, 2017| Wey A et al, Am J Transplant, 2018). Age alone should not decide eligibility, as chronologic age and physiologic age can vary significantly (Kaewput and Patel, BMJ, 2026| Chadban SJ et al, Transplantation, 2020).

For the 75-year-old with diabetes offered an ECD/DCD kidney: mortality is higher in the first months, and 5-year survival is similar to dialysis, with uncertainty in both directions. A better deceased donor kidney or a living donor kidney offers a clearer benefit. Finally, quality of life, which improves after transplantation even in older recipients (Tsarpali V et al, Kidney Med, 2021| deBoer SE et al, Transpl Int, 2024) but wasn’t measured here, should carry substantial weight and merit significant discussion. 

Conclusion

Deceased donor kidney transplantation was associated with a survival benefit compared with continued dialysis in most settings. However, the survival benefit associated with ECD transplantation decreased with advancing recipient age; for ECD/DCD kidneys at 75 or older it could not be demonstrated, and the estimate is imprecise. Where survival gain is small or uncertain, the decision rests on the patient’s priorities. 

Summary by

Urvashi Khan,

Associate Consultant (Nephrology),

Max Superspeciality Hospital,Noida,India

S.M.Assad,

Assistant Professor (Nephrology)

Christian Medical College,Vellore,India

Reviewed by

Cristina Popa, Brian Rifkin, Akshaya Jayachandran

Header Image created by AI, based on prompts by S.M.Assad

Increasing plasma sodium with Tolvaptan under regulated water intake: comparison with hypertonic saline

Nabil William Sweis, Jennifer Tuazon, Yusra Cheema, Robert Rosa, Daniel Batlle, Gary Robertson

Why is the present study needed? 

Hyponatremia is highly prevalent and affects approximately 35% of hospitalized patients (Adrogue HJ et al, JAMA, 2022). Although there is still a great amount of debate, most clinicians believe the rate of correction is the primary issue in the treatment of hyponatremia. Slower correction increases the risk of brain herniation, while faster correction increases the risk of osmotic demyelination (ODS) in high risk persons. The serum osmolality is tightly regulated between 275 and 295 mOsm/kg by vasopressin, which acts on the V2 receptor in the collecting duct. This mechanism triggers the transport of aquaporins from the basolateral to the apical membrane, thereby increasing water absorption and concentrating urine. (Martinez-Sanchez FD, et al. World J Nephrol, 2026) .

Vaptans increase serum sodium by blocking V₂ receptors, which causes aquaresis and improves serum sodium levels in a more physiological way; however, some articles report a risk of ODS with vaptans (Morris JH, et al. AJKD, 2018; Scalla AP, et al. J of Pharm Pract, 2024). A meta-analysis with 1840 patients with 3 RCTs and 2 cohorts, claimed a higher rate of correction with the vaptans compared to placebo and fluid restriction with an OR of 5.72 for overcorrection, with a pooled overcorrection incidence of 13.1% vs 3.3%, yet zero observed ODS events. (Krisanapan P et al. J Clin Med, 2023). Can we control the rate of correction with vaptans and use them to correct hyponatremia while avoiding overcorrection?

How was the study done?

The study included healthy volunteers by way of advertisement and was conducted at Northwestern Memorial Hospital in Chicago. Out of 17 volunteers, the first 6 received 60 mg of tolvaptan, and of the next 11 volunteers, 6 received 30 mg and completed the study; the remaining 5 were disqualified due to difficulty in voiding, dizziness while voiding, and alcoholic intoxication. The study was funded by Otsuka American Pharmaceuticals, the manufacturer of vaptans.

The selected subjects were admitted to the hospital at 7:00 am; their vitals, height, weight, and time of recent void were recorded. At 9:00 am, they were instructed to void into a preweighed plastic container, and urine volume was determined. The sample was measured for urine osmolality, and blood was collected for plasma osmolality and serum sodium. Another sample was stored at -20°C for vasopressin measurement by radioimmunoassay. After collecting basal data, the first six participants received 60mg of Tolvaptan and the next received 30 mg of tolvaptan they were instructed to take tolvaptan with 150 ml of water. Every hour, weight, urine osmolality, plasma osmolality, serum sodium, and vasopressin samples were collected. They were given room-temperature water to drink equal to the amount of urine volume (UV) in the previous hour, minus 5 ml/kg of body weight (underreplaced). After the 6 hours of regulated water intake, the subjects were advised to eat, drink, exercise, and sleep as they wished until the next day at 7 AM. The same measurements were taken at 2, 2, 4, and 8 hours.

Hypertonic saline infusion: After completing the tolvaptan study, the same participants were admitted 2 weeks later, and the same basic measures were taken as in the tolvaptan study. They were infused with hypertonic saline at 0.033 ml/kg body weight (approx. 2 ml/kg/h cumulative) over six hours with complete water restriction, and the same measurements were taken for the next 4 hours.

What did they find? 

Tolvaptan 60mg group: 

Initial 6 hrs of the regulated water intake phase:

• Urine osmolality, urine volume, and free water clearance were monitored hourly.

• Urine osmolality decreased from baseline 478 to 59 at the 3rd hour and gradually increased to 154 at the 6th hour.

• Urine volume increased from 72 to 749 at the 3rd hour, then gradually decreased to 451 by the end of the 6th hour.

Thirst: Thirst increased progressively from basal to the 6th hour of regulated water intake On a scale of 0 to 10, the individual ratings (mean ± SD) rose from a basal level of 2.0 ± 1.5 to a maximum of 5.8 ± 2.9 after 6 h of regulated water intake, and mean thirst rates were correlated with the plasma osmolality during the first 6 h of regulated water intake. (R = 0.8687, P < 0.05)

Water intake: At zero hour, all six consumed 150 ml of water, and after that, they consumed an amount equal to UOP for that hour, lessened by 5 ml/kg as calculated by the nurse in charge (controlled water intake).

Body weight: Since the amount of water intake was smaller than the amount excreted, the participants went into a negative water balance, which was also reflected in their body weight, decreasing from 71.8 ± 13.5 kg to 69.8 ± 13.2 kg after 6 h of regulated water intake. When adjusted for differences in body weights, individual losses over the 6 h of regulated intake were very similar, ranging from 2.7 to 3.4% of basal body weight. 

.

Plasma osmolality and sodium: The decrease in body weight is closely correlated with the increase in plasma osmolality and plasma sodium during regulated water intake. Osmolality increased from 287 ± 1.6 to 300 ± 3.0 mosmol/kgH₂O, and sodium concentration increased from 138 ± 0.6 to 144 ± 2.0 mEq/L, respectively.

Vasopressin (AVP): The progressive rise in plasma osmolality and plasma sodium resulted in a progressive rise in vasopressin, and the osmotic threshold for vasopressin release was similar, ranging from 286 to 290 mosm/l.

Ad libitum water intake phase (1500 h - 700 h): 

After 6 h of regulated water intake, all volunteers were permitted to eat and drink at will; this phase was divided into two parts, the first 8 h part and the second 8 h part.

First 8 h part: Urine osmolality and urine volume were maintained at the same level. But body weight increased, and plasma osmolality, sodium, and vasopressin declined about halfway back to basal pretreatment levels. 

Last 8 h part: The subjects slept, water intake decreased dramatically, urine osmolality increased, and urine output decreased; however, plasma osmolality and plasma sodium continued to fall but remained above basal levels, and vasopressin rose slightly but remained well above basal levels the next morning.

Tolvaptan 30 mg group: Despite comparable peak PNa (~144 mEq/L in both groups), the percentage rise in POsm/PNa was smaller with 30 mg (3.3-3.6%) than 60 mg (4.1-4.3%), even though weight loss relative to body mass was nearly identical between groups (~2.8% in both). Using the paper’s own free-water model (TBW-57% of body weight), the observed weight loss in both groups should have produced a ~4.9% rise in POsm/PNa; the actual rise fell short in both, and more so in the 30 mg group.  

Figure 5. Urine osmolality and volume, water intake, and plasma osmolality and sodium following Tolvaptan (30 mg) from Sweis N, et al. Am J Physiol Renal Physiol, 2026

3% Hypertonic saline infusion:

After 10-14 days following the tolvaptan study, the same subjects were infused with IV hypertonic saline (3%) for 6 h at a rate of 0.033 ml/kg/hr; during the infusion they were not allowed to drink, and the same variables were measured.

Urine osmolality and body weight increased, and urine volume also increased, but not to the same level as after tolvaptan. Plasma osmolality, plasma sodium, and thirst also rose similarly to tolvaptan. Even vasopressin levels rose, but not to the same extent as in the tolvaptan study. 

After hypertonic infusion stoppage, the individuals were allowed to eat and drink at will for 4 hours. At that time, urine osmolality and volume did not change, but plasma osmolality and plasma sodium were decreased by half of the values during the infusion. Vasopressin levels decreased to baseline levels.

Comparison of the Effects of Tolvaptan and Hypertonic (3%) Saline Infusion

The slopes of changes in plasma sodium and plasma osmolality after each dose of Tolvaptan (60 or 30 mg) are 2.1 to 2.4, while those after hypertonic (3%) saline infusion are 1.8 to 2.6, and both are similar. However, the slope of the rise in plasma AVP after Tolvaptan 30 and 60 mg is twice that of the rise with 3% saline.

There was a high correlation between plasma osmolality and plasma AVP levels for all three treatments: tolvaptan 60 mg, tolvaptan 30 mg, and 3% NS in all 3 groups, with an r value above 0.9 and a p value < 0.001, how ever in each of the 6 subjects in whom plasma AVP was measured, it rose in close correlation with plasma osmolality during treatment with tolvaptan when fluid intake was regulated (r=0.9736) as well as when it was not (r=0.9360). A similar correlation was observed during the infusion of hypertonic saline when fluid intake was not permitted (r=0.9637) as well as when it was (r=0.9360). However, the slopes of the relationships between plasma AVP and plasma osmolality differed appreciably not only as a result of the two different treatments but also between subjects during each treatment.

Critical Review

How can this study impact our practice? Can we use tolvaptan in chronic hyponatremia without the fear of ODS?

Although a recent meta-analysis (Ayus J, et al. JAMA Intern Med, 2025) suggested that slow and very slow sodium correction are associated with increased risk of mortality and longer length of hospital stay compared to rapid correction, guidelines have not changed regarding the rate of correction, as the meta-analysis was based on observational cohorts and not RCTs. 

The present study supports the hypothesis that the rise in plasma osmolality and sodium under regulated water intake can be controlled with tolvaptan intake. Though tolvaptan produced thirst, it did not correlate with the magnitude of increase in urine output, but showed a strong correlation between body weight decline and rise in plasma osmolality and sodium where water intake was not regulated by thirst but by under-replaced urine output.

 In the initial 8 hrs of unrestricted water intake, plasma osmolality and sodium continued to decline about halfway back to basal pretreatment levels, even though they continued to excrete abnormally large volumes of dilute urine, suggesting that vaptans with normal water intake may not increase plasma sodium levels. In the next 8 hrs, when the subjects were sleeping, their intake was reduced, and the urine osmolality increased. 

Strengths: 

The study explained the impact of vaptans with regulated fluid intake and when given ad libitum fluid intake. The study showed that regulated fluid intake in healthy males resulted in a similar correction of plasma osmolality as per the guidelines. 

Limitations: 

1) The study was performed only in males. 

2) The study was conducted only in healthy subjects whose kidneys and water regulatory systems would not allow them to experience serious side effects, but how practical is it for patients with already damaged water regulatory systems? This is especially true for patients who are more prone to ODS during correction, such as those with chronic alcoholism, malnutrition, or hypokalemia. 

3) This treatment cannot be given on an outpatient basis because hourly measurement of urine output and adequately replacing that output is practically impossible, so it requires ICU management. 

4) It was not a safety study: no ODS-relevant endpoints were assessed, and the absence of adverse events beyond thirst does not constitute a safety evaluation. 

5) Duration mismatch with clinical practice: the regulated phase was 6 h; the Na rise it produced (5-6 mEq/l) largely reversed overnight once ad lib intake resumed. Real hyponatremia correction is a multi-day process. What’s untested is whether a fixed per-hour deficit sustained over 24-48 h (vs a single 6 h burst) produces the same predictability or compounds error.

6) The unresolved TBW-model shortfall (predicted 4.9% rise vs. observed 3.3-4.4%) means the underlying free-water deficit arithmetic (the basis of the 5 mL/kg/h prescription itself) is an approximation, not a validated dosing formula. 

Conclusions:

 The authors have demonstrated that tolvaptan, when combined with regulated water intake, has a rate of sodium rise similar to 3% NS infusion in normal healthy males. Further studies in a broader population of hospitalized patients with hyponatremia are needed to prove the efficacy of tolvaptan at the bedside and to support its wider use in hyponatremia.

Summary by

Sai Vani Yellampalli

Reviewed by

Brian Rifkin, Cristina Popa

#NephJC Ten POsts Chat

Tuesday, September 29th 2026, 9 pm Eastern

Kidney International Reports, 2026 

Randomized Controlled Trial of Corticosteroid Tapering in Steroid-Sensitive Podocytopathy 

Arun Prabhahar, Joyita Bharati, Tamanna Sharma, Vivek Sood, Pavitra Manu Dogra, Thakur Sain, Vinod Kumar, Ritambhra Nada, Smita Subhash Divyaveer, Manish Rathi, Harbir Singh Kohli, Vivekanand Jha, and Raja Ramachandran
doi: 10.1016/j.ekir.2026.107004 

Introduction

The fewer steroids, the better. This has been the catchphrase in glomerulonephritis in the past decade, with the caveat that we are not paying for it with more relapses. Adults with steroid-sensitive podocytopathy can respond to glucocorticoids, but relapse is common, and repeated courses can quickly add up to a substantial steroid burden. That matters because prolonged glucocorticoid therapy brings familiar baggage: metabolic complications, weight gain, risk of infection, bone disease, and other dose-related toxicities. The catch, of course, is that steroids work as a systemic, but non-specific, modifier of immunity and inflammation. Once remission is achieved, the practical question becomes: over what period should we taper steroids before enough is enough?

For a question we face so often in patients with nephrotic syndrome, the evidence in adults is surprisingly thin. Relapse rate in adult MCD has been reported between 61- 73% (Fenton et al, BMC Nephrology 2018; Waldman et al, CJASN 2007), with 28% frequently relapsing in the Waldman Cohort. Data from the Toronto Glomerulonephritis Registry (Troyanov et al, KI 2023) showed a 57% relapse rate among patients with FSGS and hypoalbuminemia, with a median time to relapse of 7 months. 

KDIGO Guidelines recommend high-dose glucocorticoids as first-line therapy for adult MCD (1C), and suggested tapering after remission for up to about 24 weeks of total glucocorticoids exposure. For primary FSGS, glucocorticoids are recommended first line (1D), with a Practice Point suggesting a total duration of ≥6 months in responders. Infrequent MCD relapses and relapses of previously steroid-sensitive FSGS can be retreated with a shorter glucocorticoid course (tapering over 1-2 months). Much of the current practice is shaped by observational experience rather than randomized data.

Studies in children have given us some clues as to how to effectively decrease steroid exposure in nephrotic syndrome (one of the few instances where the peds literature surpasses the adult side). Randomized trials (Sinha et al, KI 2015 and Yoshikawa et al, KI 2015; Webb et al, Health Tech Assess 2019  | NephJC summary) ) found no clear advantage in extending the initial steroid treatment compared with shorter courses. Whether we can carry that lesson into adult practice has yet to be supported by RCT data.

NephJC has wrestled with the  question of steroid minimisation in adults before while discussing tacrolimus as first line therapy in adult MCD (Thomas et al, CJASN 2020| NephJC summary). Despite all the hullabaloo around steroids, they remain the first line therapy for both childhood and adult MCD and FSGS considering their cost, availability and the vast literature supporting their use.

Prabhahar et al set out to address the evidence gap regarding length of steroid taper in adults with podocytopathy (MCD/FSGS). In the current study adults with biopsy-proven steroid-sensitive MCD or FSGS who had already achieved remission with prednisolone were randomized to either a 2-month or 4-month taper. The trial was not asking whether the shorter taper was better, but whether it was noninferior at preventing relapses. If the taper could be cut in half without meaningfully increasing relapses, then the steroid-sparing payoff could be substantial. The question they addressed: can we taper steroids sooner without paying for it later?

The Study

Methods

Study Design

The START (Steroid Tapering protocol in Adult Nephrotic Syndrome due to Podocytopathy) was a randomized, parallel-group, active-controlled, open-label noninferiority trial conducted at two centers in India. Enrollment took place from May 2021 to May 2023, with follow-up continuing through December 2025. Participants were centrally randomized using a computer-generated sequence, with allocation concealed. Neither participants nor treating nephrologists were blinded to treatment assignment; however, the person generating the randomization sequence was not involved in patient care or outcome assessment.

Study Population

Inclusion Criteria:

• Adults >18 years

• Biopsy-proven MCD or FSGS

• Newly diagnosed or infrequently relapsing nephrotic syndrome

• Achieved remission with oral prednisolone alone, defined as proteinuria <0.5 g/day at randomization

Exclusion Criteria: 

• Hepatitis B, hepatitis C or HIV infection

• Corticosteroid exposure within the previous 12 months or other immunosuppressive therapy within the previous 12 months.

• Persistent kidney dysfunction despite proteinuria remission

• Diabetes mellitus

• Active urinary sediment (>5 RBC/hpf) or ANA positivity 

• Suspected secondary cause of nephrotic syndrome

• Steroid intolerance

• Abnormal liver function tests

Intervention

Patients initially treated with oral prednisolone 1 mg/kg/day for up to 8 weeks were randomized after remission to the following arms

• Short-course arm: prednisolone tapered over 2 months

• Standard-course arm: prednisolone tapered over 4 months

Both tapering protocols used stepwise dose reductions based on the starting prednisolone dose, with dose changes every 7 days in the 2 month arm and every 14 days in the 4 month arm.

All participants also received an ACE inhibitor or ARB (Did they really need that?), along with calcium and vitamin D, until completion of steroid therapy.

After steroid completion, participants were followed regularly through 12 months (monthly for 2 months, then with decreasing frequency of follow up till month 12) and then every 6 months for long term follow up. Serial assessment of 24-hour urine protein, serum creatinine, and serum albumin was done in all patients.

Outcomes

The primary outcome was refreshingly simple: relapse free-remission at 12 months after randomization. The study-specific definition of remission at randomization was proteinuria <0.5g/day (slightly higher than the KDIGO definition of 0.3 mg/day), and relapse was defined as recurrence of nephrotic syndrome after remission.

The secondary outcomes examined some interesting endpoints as well:

• Time to first relapse

• Relapse-free remission by MCD vs FSGS

• Relapse-free remission in treatment-naive vs infrequently relapsing patients

• Relapse-free remission at last follow-up

• Cumulative prednisolone exposure

• Safety and adverse events

Sample Size

The investigators assumed that 70% of patients in both groups would remain relapse-free at 12 months. Because this was a noninferiority trial, they also had to decide how much worse the shorter taper could be before it would no longer be considered an acceptable alternative. They set that margin at 25 percentage points meaning that the short taper could not be more than 25 percentage points worse than the standard taper. The authors based this on the lack of prior adult randomized data, the high relapse rates reported even with prolonged steroid courses in MCD and steroid-responsive FSGS, and the potential benefit of roughly 50% less steroid exposure. For more on non-inferiority trials, see this NephTrials summary.

With these assumptions, 106 patients were needed to provide 80% power using a one sided alpha of 0.025; allowing for a 5% attrition increased the target enrollment to 114 patients. 

Statistical Analysis

The primary analysis was intention-to-treat (ITT), supported by a per-protocol (PP) analysis. Noninferiority had to be demonstrated in both. Noninferiority was met if the lower bound of the one-sided 97.5% confidence interval for the difference in relapse-free remission remained above -25 percentage points. A sensitivity analysis treated missing 12-month outcomes as relapses, and all other outcomes were considered exploratory. Relapse-free remission according to histology (MCD vs FSGS) and clinical phenotype (treatment-naive vs infrequently relapsing) was included among the secondary outcomes. Separately, the broader subgroup analysis (shown in Figure 3), including sex, age, histology, and phenotype, was described as nonprespecified and should therefore be considered exploratory.

Funding

The study was funded by the Indian Council of Medical Research, New Delhi. The manuscript does not describe a specific role for the funder in study design, data analysis, interpretation, or manuscript preparation. Vivekanand Jha reported consulting fees and honoraria from several pharmaceutical companies, with payments made to his institution; all other authors declared no competing interests.

Results

Of 118 patients with MCD or FSGS, 94 achieved complete remission within 8 weeks and were randomized to short- or standard-course taper. The target sample size of 114 could not be met in the specified time interval.

Eighty-eight patients completed follow-up, with only six lost to follow-up. All randomized patients were included in the ITT analysis, while 80 were included in the PP analysis.

There is an error in the consort diagram above (did you spot it?), the 24 excluded patients should appear above the 94 remission patients who were then randomized.

The study population was relatively young (mean age 31.9 years; 60.6% male), with MCD and FSGS almost equally represented (51.1% vs. 48.9%). The standard treatment group had a slightly higher proportion of patients with FSGS and treatment naive participants compared to the short course group. Just over half the patients (55.3%) were treatment-naïve. Median steroid exposure before randomization was comparable. At presentation, median proteinuria was 6.0 g/day, decreasing to 0.24 g/day at randomization, with serum albumin increasing from 2.1 to 3.7 g/dL. Baseline characteristics were comparable between treatment groups.

Primary outcome

At 12 months, relapse-free remission was 63.3% with the short-course vs. 57.8% with the standard-course regimen in the ITT analysis. The risk difference was 5.5% (lower CI bound −14.3%), remaining above the prespecified −25% noninferiority margin (P=0.001). Noninferiority was also confirmed in the PP analysis (66.7% vs. 68.6%; risk difference −1.9%, lower CI bound −22.6%; P=0.014).

Secondary Outcomes 

Regarding the type of disease, outcomes differed numerically between MCD and FSGS. In patients with MCD, relapse-free remission was more frequent with the short-course regimen (75% vs. 55%; risk difference +20.0%), whereas in FSGS, it was less frequent (47.6% vs. 60%; risk difference −12.4%). However, neither comparison reached statistical significance. 

Relapse-free remission at 12 months was similar between regimens in both treatment-naïve patients (60.7% vs. 54.2%; P=0.63) and those with relapsing disease (66.7% vs. 62%; P=0.75).

At 36 months, relapse-free remission remained comparable between the short- and standard-course groups (38.8% vs. 44.4%; P=0.58), with no significant difference in time to relapse. However, the short-course regimen substantially reduced cumulative prednisolone exposure, with a median post-randomization dose of 1750 vs. 3430 mg in the ITT analysis (P<0.001). This reduction was also observed in the PP analysis (2310 vs. 4620 mg; P<0.001).

No significant differences in treatment efficacy were observed according to underlying disease (MCD 51.1%; FSGS 48.9%) or disease phenotype (treatment-naïve 55.3%; infrequently relapsing disease 44.7%). 

Safety profile

Adverse events were significantly less frequent with the short-course regimen (24.5% vs. 53.3%; RR 0.46, 95% CI 0.26–0.81; P=0.003), with similar findings in the PP analysis.

Steroid-related adverse events were consistently more frequent with standard therapy, including dysglycemia (8.9% vs. 0%), cosmetic effects (15.5% vs. 4.1%), and arthralgia/myalgia (13.3% vs. 2.0%). Serious adverse events, as defined by the study protocol, were numerically less frequent with the short course (8.2% vs. 17.8%). However, the SAE definition also included events necessitating treatment interruption or protocol deviation, which is broader than conventional SAE definitions and may have contributed to the apparent difference between groups. 

Two serious infections requiring hospitalization occurred in the short-course group (1 case of severe COVID-19 infection and 1 case of renal abscess with pyelonephritis). In the standard-course group, serious adverse events included steroid-induced myopathy/arthralgia requiring protocol deviation (n=3), psychiatric disturbances (n=2),hemorrhoidal bleeding requiring blood transfusion (n=1), and avascular necrosis of the hip (n=1).

Discussion

The optimal duration of corticosteroid therapy in adult steroid-sensitive nephrotic syndrome remains uncertain. Current KDIGO guidance (KI 2021) recommends glucocorticoids as first-line therapy for adult MCD/primary FSGS, with tapering starting 2 weeks after complete remission, with a total exposure steroid exposure period of 6 months.  These are Practice Points based on small observational studies or extrapolated from childhood NS, largely reflecting limited adult evidence and real concerns regarding relapse with shorter treatment courses.

Attempts to reduce corticosteroid exposure in adults have produced mixed results. In a prospective study of 35 adults with steroid-sensitive MCD, a 2-month steroid regimen significantly reduced cumulative corticosteroid exposure compared with conventional treatment (Ozeki T et al, Am J Nephrol 2019). However, the shorter regimen was associated with an earlier occurrence of relapse (adjusted HR 2.45, CI 1.51-3.91), although the risk of frequent relapse was not significantly different. Importantly, this study was an observational study with a historical control rather than a randomized trial.

More recently, the ADAPT randomized noninferiority trial explored a steroid-minimizing strategy in adult MCD, this time by reducing the corticosteroid dose rather than treatment duration, comparing prednisolone 0.5 mg/kg/day plus alfacalcidol with conventional prednisolone 1 mg/kg/day (Kristensen et al, Kidney Int 2026). Remission (88% vs. 91%) and relapse rates (35% vs. 32%) were similar between groups, with no significant differences in time to remission or relapse. The lower-dose strategy substantially reduced cumulative prednisolone exposure (3.7 vs. 8.0 g) and glucocorticoid toxicity. However, formal noninferiority was not established. 

Against this background, the present trial adds randomized evidence for adults with podocytopathies. Unlike previous studies focused predominantly on MCD, it included both MCD and steroid-sensitive FSGS, directly evaluating whether shortening the taper, after remission has already been achieved, could preserve relapse-free remission. Noninferiority at 12 months was demonstrated in both ITT and PP analyses, while cumulative prednisolone exposure was approximately halved.

The clinical trade-off therefore appears favorable: similar relapse control with substantially less corticosteroid exposure. This was further reflected in the safety findings, with adverse events occurring more than twice as often in the slow taper group. The reduction was particularly apparent for typical steroid-related metabolic, cosmetic, and musculoskeletal complications.

Protocol deviations were also more frequent in the standard-course group (6 vs. 2), with most deviations in this group related to steroid-associated adverse events, further supporting the tolerability advantage of reducing corticosteroid exposure. However, the reduction in overall adverse events should not be interpreted as evidence that the shorter taper reduces serious infections, hospitalization, or long-term complications. Notably, the two serious infections requiring hospitalization occurred in the short-course group. 

The findings are also reassuring over longer follow-up. At 36 months, relapse-free remission remained similar between groups, strengthening the conclusion that shortening steroid therapy did not simply postpone relapses beyond the 12-month primary endpoint. Notably, the persistence of comparable outcomes at 36 months provides important longer-term support for the durability of the short-course strategy. Nevertheless, fewer than half of patients in either group remained relapse-free, highlighting that long-term relapse remains an important clinical challenge. This highlights that long-term relapse remains an important unresolved issue irrespective of taper duration, but that simply prolonging corticosteroid exposure may not substantially modify the underlying tendency to relapse. 

Limitations

So, should we just taper  steroids quickly for adult MCD/FSGS?! Well, there are important reasons for caution. The study was smaller than planned, enrolling 94 of the targeted 114 participants, which reduced the statistical power to approximately 75% and resulted in relatively wide confidence intervals. The −25% noninferiority margin was also wide, meaning that noninferiority should not be interpreted as equivalence and a clinically meaningful difference cannot be completely excluded. Although the point estimate favored the shorter taper, the lower confidence interval bound of −14.3% means that the results remain compatible with approximately 14 additional relapses per 100 patients treated with the shorter regimen. Whether this potential loss of efficacy represents an acceptable trade-off for substantially lower corticosteroid exposure may differ between patients, particularly when previous relapses have been severe or complicated by acute kidney injury, infection, or thrombosis. Additionally, the relapse free remission was much lower than the assumed 70% used to calculate sample size, further questioning the evidence this sample size could generate.

The study was open-label and conducted at only two centers, potentially limiting generalizability. The findings, therefore, cannot be directly extrapolated to older or more comorbid patients, steroid-resistant FSGS, or patients in whom induction therapy is limited by toxicity.  

Importantly, the trial was not powered to detect histology-specific differences. At baseline, a higher percentage of patients in the short course group were MCD rather than FSGS. Additionally, microscopic hematuria, which is more common in FSGS compared to MCD, and a predictor of proteinuria remission (Yoshida et al, Kidney Medicine 2026), was excluded in the present study. Interpretation of the FSGS subgroup is further complicated by the heterogeneity of FSGS itself. FSGS represents a histological pattern encompassing primary, genetic, and secondary forms of podocyte injury. Although nephrotic presentation and steroid responsiveness increase the likelihood of primary FSGS, electron microscopy was not mandatory and systematic genetic testing was not reported, leaving some uncertainty regarding the classification of the FSGS population. 

Although relapse-free remission numerically favored the short-course regimen in MCD and the standard-course regimen in FSGS, these subgroup findings should be considered hypothesis-generating rather than definitive. This is particularly relevant for FSGS, where evidence supporting shorter steroid courses remains much more limited and current KDIGO guidance does not support extrapolating shorter corticosteroid regimens from MCD to FSGS. 

Whether steroid-sensitive MCD and FSGS should therefore be managed with the same corticosteroid tapering strategy remains an important unanswered question and warrants adequately powered, histology-specific studies. 

Conclusion

START shifts the balance toward shorter steroid exposure, with a 2-month taper meeting the prespecified noninferiority criterion while substantially reducing cumulative prednisolone exposure. The inability to meet sample size, wide noninferiority margin and uncertainty around FSGS keep this from being a one size fits all taper. 

Summary by

Andreea Manolea,

Nephrology resident,

Clinical Hospital "Dr. C. I. Parhon" Romania

Farah Wehbe,

Renal hypertension fellow,

University of Ottawa, Canada

Reviewed by

Pallavi Prasad, Brian Rifkin

Header Image created by AI, based on prompts by Brian Rifkin

The START trial compared 2-month and 4-month prednisolone tapers in adults with minimal change disease or FSGS. Explore the study design, findings, and clinical implications on the next #NephJC

El ensayo START comparó pautas de 2 y 4 meses de prednisolona en adultos con enfermedad de cambios mínimos o GEFS. Conoce el diseño, los hallazgos y sus implicaciones clínicas en el próximo #NephJC

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Thoughtful critical appraisal of the 26 most impactful manuscripts every year, and even more with shorts. Half of those are additionally covered by our podcast, Freely Filtered.

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A good time was had by all at the 2026 NephJC #KidneyWk Party

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September 1st, 2026, 9 pm EST

Kidney Int Rep,  2026 Jun 23;11(9):106673., doi: 10.1016/j.ekir.2026.106673.eCollection 2026 Sep.

Randomized Controlled Trial of Large Language Model–Assisted Diagnostic Accuracy in Nephrology 

Raphaël Bentegeac, Philippe Amouyel, Bastien Le Guellec, Wisit Cheungpasitport, Mehdi Maanaoui, Aghiles Hamroun

PMID: 42502668
DOI:  10.1016/j.ekir.2026.106673

Introduction

Modern Artificial Intelligence (AI) encompasses a spectrum of computational approaches, from traditional machine learning to deep neural networks capable of processing increasingly complex data (see NephMadness 2026, AI region). Among the most rapidly evolving applications of deep learning are large language models (LLMs) that are trained on vast amounts of information and can interpret, synthesize, and generate natural language.  Since the release of ChatGPT in late 2022,  medicine has been grappling with a collective identity crisis.  In the last few years, there has been an expanding body of research evaluating the performance of different LLMs across a wide range of medical tasks, from answering clinical questions and interpreting complex case vignettes to diagnostic reasoning and medical decision-making. A recent systematic review (Chen SF et al, Nat Med, 2026)  found that LLMs outperformed human comparators in 33% of studies, with better relative performance in knowledge-based assessments than in real-world clinical settings, highlighting the persistent gap between benchmark performance and clinical utility. Essentially, AI programs are very good at multiple-choice exam questions.

Nephrology has not been an exception to this trend. In an evaluation of 975 questions from the Nephrology Self-Assessment Program (NephSAP) and Kidney-Self-Assessment Program (KSAP), GPT-4 achieved 74% accuracy, although remaining below the 77% passing threshold and the average performance of nephrology examinees (Miao J et al, Clin J Am Soc Nephrol, 2024). Moreover, beyond knowledge-based assessments, the potential applications of LLMs in nephrology have rapidly increased to include patient management, clinical documentation, decision support, and medical training. Evidence supporting the safety and effectiveness of LLMs in real-world nephrology settings remains limited (Yongzheng Hu et al, Ren Fail, 2025). 

Against this vast background, the REFINe (Reasoning Enhancement With Feedback From a Generative AI in Nephrology) was conducted. Rather than asking whether a LLM could independently solve nephrology questions, REFINe addressed a more clinically relevant question: Can access to a high-reasoning LLM improve physicians diagnostic accuracy when faced with complex, open-ended nephrology cases?

REFINe tested whether AI can better diagnosticians of physicians- a more relevant question for the future of AI-assisted medicine. 

Methods

REFINe was a prospective, randomized, open-label, parallel-group superiority trial conducted entirely online between November 2025 and March 2026. English or French-speaking residents and board-certified physicians were randomized 1:1 to diagnose complex nephrology cases with or without LLM assistance. Each case evaluation was one unit of analysis. The review board of lille University Hospital approved the study. The study group planned to enroll 100 participants and recruited 97. They report no change to the criteria, the outcomes or the analyses after the trial started. 
The cases came from the Make Your Diagnosis series in Kidney International, published between August 2007 and January 2025. Two nephrologists (N.F. and A.H.) rewrote 245 of these cases. The aim was to stop the LLMs from recognizing cases used during training. Study designers changed every sentence and every number but kept the clinical logic and the final diagnosis. Ten cases were then chosen at random for the trial. 
It is worth noting what those ten cases detailed, because this shapes how we should interpret percentages in the paper. There were four glomerular and vascular cases, two tubular, interstitial and cystic disorders, two electrolyte and acid disorders, one case of acute kidney injury in critical care, and one transplant case. Seven of the ten included kidney or allograft pathology. One included urine microscopy. Two were text only. One was pediatric. This question inventory tells us more about the difficulty than any adjectives or descriptors could. 

Participants were residents or board-certified doctors who spoke English or French. They were recruited through professional societies and conferences. The main exclusion criteria was previous contact with the specific cases. The team used the Pocock and Simon minimization algorithm (80% fixed and 20% random) to balance five features: certification status, speciality, years of experience, how often the doctor used LLMs, and academic status. The process was automatic, and the allocation sequence was hidden from the study staff. This is a stronger allocation method than most similar AI studies have used. 

Each participant received the 10 cases, in random order, on a dedicated platform. They could not go back to review a previous case. In the control group, doctors gave up to three diagnoses and a confidence score, then moved directly to the next case- no AI input at any point. In the AI group, participants completed the same first step, and the platform showed them a fixed GPT-5 answer with short, structured reasoning: they could revise their diagnoses and confidence once before moving on. The AI answer came from a two step prompt with no examples given to the model. The first step returned exactly three ranked diagnoses. The second step turned them into case specific reasoning with probabilities and a short summary of the key differences. All GPT-5 answers were produced in October 2025 and then frozen (this was a fixed ranked list with probabilities, not a conversation). 

Then two board-certified nephrologists scored the main outcome. They worked independently and did not know from which group each answer was obtained. They agreed on about 97% of answers before any discussion, and the rest were solved by consensus. For emphasis, the whole result of this trial rested upon the “yes” or “no” judgments about free text answers by the adjudicators. 

The sample size came from a simulation. It assumed 50 participants per arm, 10 cases each, 50% accuracy in the control group, and 70% in the AI group. With 100 participants, the power was calculated to be above 99%. The main analysis used mixed-effects logistic regression, with a fixed effect for the AI study arm and random intercepts for the participant cases. It was adjusted for gender, country, speciality, academic status, experience, confidence in AI, how often the doctor used LLMs, and how many AI tools they used. Every randomized participant was analysed in the assigned group. The subgroup analyses were planned in advance. The analyses by number of cases completed were done afterwards. The R 4.3.2 software environment was used for data analysis, statistical modelling, and graphics.

REFINe Population

Results

The 97 participants produced 556 case evaluations. The median age was 38 yo (IQR 34 to 45). Three out of four said nephrology was their speciality, and 69% did not work in academic centres. Nine countries were represented, but 87% to 93% of participants were French, depending on the arm. The two groups were balanced at baseline. About four in ten participants had never used an LLM. 

Primary Outcome

Secondary Outcomes

Doctors only took half of the good advice!

There were 178 answers that were wrong at first where the AI suggestion prompted the correct answer. Of those, 85 (47.8%) were corrected, and just under half were not. In the AI arm, doctors changed their top-3 in 47.4% of answers (from 41.3% to 53.6%).

Harm was rare

Only 1 of 69 answers that were right at first became wrong after a bad AI suggestion (1.5%; 95% CI: 0.04 to 7.9). In this format, doctors were not simply following the machine. 

No subgroup effect reached significance (all interaction p > 0.05). The analyses by number of cases completed gave similar effect sizes, but they also show something the main paper does not discuss.

One pattern stands out despite the null result: monthly LLM users showed by far the largest AI benefit (OR 10.51, 95% CI 3.18-24.67), well above never users or daily users, and the LLM-usage interaction (p=0.008) was the closest to significance of any subgroup tested (supp figure 3).

The adjusted odds ratios were 4.3 (1.99 to 9.28) for doctors who completed at least five cases, and 3.94 (1.70 to 9.13) for those who completed all ten. 

Before the trial, the team tested eleven models on all 245 cases. This took about three hours and cost around EUR 300. GPT-5 was the best performer in the pre-trial benchmark (77.1%), so it was the model used in the AI arm.

DeepSeek V3.2 and MedGemma 27B were tested with text-only inputs because those setups could not accept images. Their scores therefore, mix model ability with a missing input. 

Funding: The authors received no specific funding for this work. 

Data availability statement: Participant consent did not include permission for public data sharing. The analysis code will be made publicly available in a Git repository.

Discussion

From AI versus nephrologists to AI with nephrologists

LLMs have already shown that they can readily solve board-style questions. This study has moved the field of study from multiple-choice questions to complex, integrated clinical vignettes requiring the synthesis of history, laboratory data, pathology, urine microscopy and images. This trial addresses a very practical and pressing question: Does giving clinicians access to an LLM actually improve diagnostic reasoning in nephrology? In this RCT of fixed scenarios, the answer appears to be yes. While the results seem like a slam dunk endorsement of our machine overlords (we will add your biological and technological distinctiveness to our own; resistance is futile), we need to read in between the lines. Top-3 and Top-1 diagnostic accuracy improved by around 20% with AI, and the effect was preserved among participants who completed at least 5 cases, but it could have been an even greater improvement if the physicians had trusted the AI more. 
Clinical nephrology is open-ended, and new clinical information is integrated in ways particular to our specialty. Our diagnoses rely on the incorporation of heterogeneous data types, including a urinary sediment, light microscopy and immunofluorescence pattern, blood gases, and a serology panel that half fits multiple etiologies (diseases rarely read textbooks and don’t deal in absolutes). There is rarely a diagnosis that relies solely on an individual, decisive test. Answers may depend on trajectory rather than on a point value, since the same creatinine of 2.4 mg/dL means one thing over three days and something entirely different over three years. Finally, nephrologists draw on a long tail of low-prevalence entities, precisely the region in which a probabilistic model may either shine or possibly exaggerate the exotic. 

The human - AI gap may be the real story

There were 178 occasions where the clinician's initial answer was wrong, but GPT-5 gave the correct diagnosis. Yet only 85 (47%) of those answers were eventually corrected. This is probably one of the most important findings of the study. The problem is not always whether AI can correctly determine the diagnosis, but rather whether physicians are willing and/or able to recognize their mistakes. When it comes to correct diagnosis, humans are subject to errors in logic, including anchoring, premature closing, and confirmation bias (Mutlack Z, et al. J Clin Med 2025). AI assistance brought clinicians substantially closer to the apparent performance ceiling of the model, but nowhere near all the way (maybe the real trial was about us, not LLMs). That is where this paper becomes less about how clever GPT-5 is and more about how we, as clinicians, respond when AI disagrees with us. Do we trust it, challenge it, ignore it, or simply refuse to change despite the evidence? 

Why does a tool capable of very high diagnostic accuracy not automatically transfer that accuracy to the clinician sitting in front of it? The study workflow and the AI interaction here were very artificial and may have contributed to the gap. GPT-5 produced exactly three ranked diagnoses with concise reasoning and probability estimates. The answer was pre-generated and frozen. There was no chat, no follow-up, no chance to ask, “Why not my diagnosis?” or “Which one feature in this case makes you think that?” That matters because most of us do not use AI like a static answer key. We question it, push back, ask for discriminating features, ask it to argue against itself, and sometimes keep asking until either we are convinced or the model starts hallucinating creatively enough that we close the window. So, the question is not simply whether GPT is accurate. It is how much of that accuracy can be transferred to a clinician in a safe and useful way.

What has been examined previously

Other studies with the use of LLMs and physicians diagnosing complex cases have had mixed results. Goh and colleagues found no benefit at all from giving doctors an LLM, while the model on its own beat both groups (physicians alone and AI assisted physicians) by 16 points, similar to the current study (Goh E, et al. JAMA Netw Open, 2024). Everett and colleagues did improve accuracy with collaborative workflows (Everett SS, et al. NPJ Digit Med, 2026). We know LLMs keep evolving (quickly), but human integration may take longer due to trust and fear of machine hallucinations. Presentation of source materials might help physicians be more accepting of AI prodding changes, especially if the logic is solid and the primary references could be reviewed. Again, a conversation versus a mandate seems to work better in altering a doctor’s clinical reasoning.

Many previous studies scored the quality of the reasoning against a rubric (Goh E et al, JAMA Netw Open, 2024| Everett SS et al, npj Digit Med, 2026| Qazi IA, et al. Nature Health, 2026). REFINe scored a binary “yes” or “no”: was the right diagnosis somewhere in the list. The nephrology study mixed a percentage with a completeness rating. 

We don't want doctors to blindly accept whatever an AI says. If AI gives a diagnosis that doesn't fit the patient, it should be questioned, because currently AI has its own set of logistical errors (hallucinations, prejudice due to training errors, and context loss). There’s another side to this; even if the AI is actually right, our own clinical experience and preconceived notions can make us dismiss it. The trial results show that the ability to decide when to listen to AI may be just as important as the AI's accuracy. The authors make a similar point, suggesting that AI literacy and how the interface feels to us may be important steps in human/AI integration to achieve the best results.

Considering the reverse scenario, among the responses that were initially correct, very few became wrong after viewing the AI suggestion. This means the participating clinicians didn’t simply abandon a correct diagnosis whenever the AI disagreed with them. That is reassuring, yet we should be cautious, because the study was small and the AI was presented in a very controlled format somewhat dissimilar from real world clinical reasoning.

It is important to note, the control group in this study did not have access to the usual clinical resources. That is quite different from trials in which doctors could use Google, UpToDate, or other conventional resources. In addition, LLMs are evolving at an incredible rate, making what’s true in machine logic yesterday almost obsolete in just a few weeks. We cannot simply say that REFINe proves that LLMs are better than physicians (this sounds like a headline AI would write). It just shows that adding this particular AI workflow to unaided clinical reasoning improved diagnostic performance. In clinical practice, AI is being integrated in a somewhat haphazard way, slowing potential real world improvements. However, AI will be a useful tool for clinicians who choose to enhance diagnosis and treatment strategies with an eye on confirming machine-generated recommendations.

The number we are highlighting is the distance between 57.5% (physician enhanced with AI) and 77.1% (AI alone), and the fact that half of the correct suggestions were simply not used. That gap is not an AI model problem. It is a problem about how we read, trust, and wrestle with these tools. Qazi and colleagues showed just how much this can be moved: an AI-literacy curriculum alone lifted diagnostic assistance accuracy from 42.6% to 71.4%, a 27.5 point gain - arguably a bigger lever than the model itself (Qazi IA et al, Nature Health, 2026). It is becoming quite apparent that AI literacy is no longer optional for the modern physician. Mastery of AI (a potentially daily consulted assistant) sits closer than the urine sediment and biopsy interpretation in core kidney education than most of us are comfortable admitting. Similar to the acceptance of the electronic medical record, AI will be transformational once everyone gets on board.

Strengths

Methodologically, there is quite a lot to like. Group allocation used the Pocock-Simon minimization algorithm, with 80% deterministic and 20% random assignment. This balanced certification status, specialty, years of experience, LLM usage frequency and academic status. Randomization was automated and allocation remained concealed from study personnel during enrollment. The primary analysis used mixed-effects logistic regression with random intercepts for both participant and vignette, which is important because each clinician contributed multiple cases and each vignette had a different intrinsic level of difficulty. The model also adjusted for gender, country, specialty, academic status, experience and AI-use characteristics. Although the intervention itself could never be blinded, the diagnostic outcome adjudication was. This is a stronger allocation method than most similar AI studies have used. 

Weaknesses

The sensitivity analysis is also reassuring as the benefit did not disappear when the analysis was restricted to people who completed more of the task. At the same time, 97 participants generated only 556 completed evaluations, despite being assigned up to 10 cases each, and that’s before counting the 46 signups (32% of all registrants) who never completed a single case and were excluded from analysis all together. That raises a question: were difficult cases more likely to be abandoned or did participants simply decide that ten consecutive renal diagnostic puzzles were enough nephrology for one evening? Completion was voluntary and uncompensated, so informative non-completion remains worth thinking about even though the sensitivity analyses are reassuring.

Baseline confidence was similar between control and AI groups, but confidence increased significantly after exposure to AI (p<0.001). That makes intuitive sense if someone or something has just agreed with you, produced three polished diagnoses, and assigned probabilities to them. You naturally feel more certain. But there is an important caveat here. The confidence has to move in the correct direction, not just increase. If AI makes a correct clinician more confident, that is useful; if it makes a wrong clinician more confident, that may be dangerous.

The control arm also had no access to conventional resources. That makes the trial clean experimentally, but it is not quite how nephrology is practiced. If we come across a rare genetic tubulopathy, an unusual biopsy or a toxic alcohol case, or a lot many numbers, we may look something up. That is not cheating; that is how nephrology is currently practiced. The clinically relevant future comparison is therefore probably usual resources versus usual resources plus LLM. 

Conclusion

This is the first randomized trial in nephrology to show that a structured LLM workflow improves diagnostic accuracy, with very few errors introduced by the AI. Although this study may not currently have real world implications, the growing breadth and speed of LLM evolution will make it an essential tool for all future physicians.

One last detail, buried in the disclosures: the authors used Claude Sonnet 4.5 and GPT-5 to help write and edit this manuscript. A paper about human-AI diagnostic collaboration was itself a product of human-AI collaboration, which feels about right.   

Summary by

Anca Elena Stefan
Nephrology specialist, Assistant Lecturer
Romania

Assad S M

Assistant Professor, Nephrology

Christian Medical College, India

Srinivasavaradan Govindarajan
Assistant Professor (Ped Neph)
VMMC and Safdarjung Hospital, India

Luis Daniel Ramírez-Calvillo

Nephrology Fellow
Instituto Nacional de Cardiología Ignacio Chavez, México

Reviewed by

Akshaya Jayachandran, Brian Rifkin, Cristina Popa

Special thanks to guest reviewer & author

Wisit Cheungpasitporn (The AI guy)

Header created by AI and prompts from Assad SM

Check out the REFINe VA, and don’t forget to join us on #NephJC!

Revisa el resumen visual de REFINe y no olvides acompañarnos en #NephJC

Vasopressors or Fluids in Early Septic Shock

ARISE FLUIDS investigators, the ANZICS Clinical Trials Group, and the ACEM Clinical Trials Network; Sandra L Peake, Stephen P J Macdonald, Belinda D Howe, Elissa Milford, Peter Jones, Glenn Arendts, Michael Bailey, Jonathon Burcham, Anthony Delaney, Diana Egerton-Warburton, Daniel Fatovich, John F Fraser, Alisa M Higgins, Gerben Keijzers, Andrew A Udy, Patricia Williams, Paul J Young, Rinaldo Bellomo

PMID: 42274006

DOI: 10.1056/NEJMoa2516225

Why was the study done?

Fluid resuscitation has long been a pillar of septic shock treatment. However, the evidence so far has weakened the idea for treating 30 mL/kg as a rigid universal target. At the bedside, the decision is rarely based on metrics. Fluids may restore effective circulating volume, yet beyond the point of fluid responsiveness, another bolus may provide diminishing hemodynamic benefit while worsening tissue edema, venous congestion, pulmonary edema, cardiac dysfunction, and oxygenation (Prescott HC et at, CCM 2026 |  Monnet X et al, Intensive Care Med 2025).

So, when to stop giving fluids and transition to early vasopressor support?

The “fluid wars” have challenged the assumption that more is better, but so far, less has not clearly translated into better outcomes. The closest precedent is the CLOVERS (Shapiro NI et al, NEJM 2023) and the CLASSIC trial (Meyhoff TS et al, NEJM 2022 | NephJC Summary | Freely Filtered discussion), which tested different points along the resuscitation pathway without improving mortality.  Perhaps mortality alone does not capture the entire effect of an early hemodynamic strategy? 

ARISE FLUIDS (Peake SL et al, NEJM 2026) moved the question into an ED strategy comparison: after initial fluid resuscitation has begun, can restricting additional fluid and initiating vasopressors earlier improve patient-centered outcomes? Rather than selecting mortality alone, the investigators chose days alive and out of the hospital through day 90 (DAOH-90) as the primary outcome integrating survival, hospitalization, and readmission. The hope was that this new trial would help shape recommendations towards a more individualized approach to fluid resuscitation in sepsis (Prescott HC et at, CCM 2026 |  Monnet X et al, Intensive Care Med 2025).

What they did?

• Australian Resuscitation in Sepsis Evaluation: Fluid or Vasopressors in Emergency Department (ARISE FLUIDS).

• Investigator-initiated, multicenter, open-label, randomized superiority trial conducted at 51 hospitals in Australia, New Zealand, and Ireland.

• Randomization occurred in patients presenting to the ED with suspected sepsis and persistent hypotension (systolic blood pressure <90 mm Hg or mean arterial pressure <65 mm Hg) and lactate >2.0 mmol/L while receiving the first dose of an antimicrobial agent, despite at least 1L of IV fluid.

• Important exclusions included more than 2 L of fluid before enrollment, more than 6 hours since ED presentation, more than 2 hours since the final inclusion criterion was met, hypotension attributed to another cause, an immediate need for surgery, imminent death, or a clinician determination that either strategy was unsuitable.

 These criteria selected an early, still-modifiable phase of shock rather than established refractory ICU shock.

• Patients were randomized 1:1, consent was not obtained for 37 patients; the intention-to-treat population included 963 participants: 481 assigned to the vasopressor strategy and 482 to the fluid strategy. The assigned strategy continued for at least 6 hours, and up to 24-hours, while patients remained in a critical-care area.

• Vasopressor strategy: routine resuscitation fluid was stopped, vasopressors were started immediately and titrated to the blood-pressure target, and 250-mL rescue boluses were allowed when clinically indicated.

• Fluid strategy: up to 1L was given during the first hour, followed by 500-mL boluses when hypotension or hypoperfusion persisted; vasopressors were started after the fluid target was restored or when further fluid was not considered appropriate.

Outcomes 

• Primary: 

• Days alive and out of the hospital through day 90 (DAOH-90)

• Secondary: 

• Mortality; days alive at home; and days free of mechanical ventilation, vasopressors, and acute kidney-replacement therapy. 

• Safety included pulmonary edema, vascular-access complications, ischemic events, and serious adverse events.

What do the results say?

The groups were balanced at baseline. Median age was 68 years in the vasopressor group and 69 years in the fluid group, median APACHE II score was 18 in both groups, and median lactate was approximately 3.2–3.3 mmol/L. Both groups had already received a median of 1.5 L, approximately 18 mL/kg, prior randomization. 

Did the trial separate the two strategies?

By 6 hours, the vasopressor group had received a median of 500 mL of additional fluid versus 1,500 mL in the fluid group. By 24 hours, the corresponding volumes were 1,140 mL versus 2,248 mL, a median difference of −1,108 mL (95% CI, −1,395 to −850). Vasopressors were started a median of 1 hour earlier (0.4 vs 1.4 hours), and more patients received them during the first 24 hours (86.5% vs 67.6%; difference, 18.9 percentage points; 95% CI, 13.3 to 24.5). Peripheral administration was common (71.9% vs 55.4%). 

Primary outcome: 

The median number of days alive and out of hospital at day 90 was: 76 days vs 76 days, for early vasopressors versus more fluids, respectively.

Restricting additional fluid and starting vasopressors earlier did not improve the primary patient-centered outcome. The adjusted analysis showed no clear difference for the major secondary outcomes, including mortality. 

Ninety-day mortality was 16.4% with the vasopressor strategy and 14.4% with the fluid strategy (RR, 1.14; 95% CI, 0.85 to 1.54). Mortality at 28 days, days alive at home, and days free of mechanical ventilation or acute kidney-replacement therapy were also similar. Also, vasopressor-free days were slightly fewer in the early-vasopressor group, 

Pulmonary edema occurred in 0.6% of patients assigned to early vasopressors versus 5.0% assigned to more fluid (RR, 0.12; 95% CI, 0.03 to 0.39; P<0.001). Other adverse events were uncommon and broadly similar; no ischemic events or serious adverse events were reported.

Critical view

So, the ARISE FLUIDS is not exactly a trial of “vasopressors versus fluids”. Both groups received fluids, and also received vasopressors. The trial compared the timing and relative emphasis of these two therapies, achieving a clear separation between treatment strategies in a controlled setting. This strategy did not improve the overall recovery, but it did reduce the fluid exposure. 

This finding fits into an increasingly consistent story from CLASSIC  and CLOVERS : changing fluid exposure is feasible, but simply giving less fluid has not produced a universal advantage. 

CLASSIC trial achieved 2L lower ICU fluid exposure, but 90-day mortality was essentially identical (42.3% vs 42.1%), as were days alive without life support and days alive out of hospital. CLOVERS moved the question and prioritized vasopressors over additional fluid. The restrictive group received about 2.1L less fluid, with earlier and longer pressor use, but death before discharge home by day 90 was similar (14.0% vs 14.9%; P=0.61). In ARISE FLUIDS, the fluid separation was smaller, about 1.1L less fluid, with 18.9% vasopressor use, and yet both groups showed no superiority at all.  

What about the apparent few patients with pulmonary edema in the vasopressor group? The infection source was heterogeneous, as expected in the sepsis population, and there was a modest imbalance in respiratory infections, with more cases in the fluid group. Could this influence safety outcomes? Still findings remain provocative, particularly given the lower fluid exposure. 

This may also be one of the trial's most interesting blind spots. Hemodynamic assessment was still largely driven by conventional clinical variables, and POCUS was not systematically incorporated to characterize fluid responsiveness as part of the strategy. Although CLOVERS allowed its use, it was not part of a standardized intervention. 

Conclusion

Where does this leave us? Unsurprisingly, there may not be one resuscitation strategy for every septic patient. The next step may be less about finding another universal fluid threshold and more about identifying the patient's hemodynamic phenotype in real time, integrating perfusion, fluid responsiveness, cardiac function, and venous/pulmonary congestion, to decide who needs another bolus and who needs vascular support instead.

Written by Milagros Flores

Reviewed by Brian Rifkin 

Randomized Trial of Dietary Nitrate Supplementation in CKD Pregnancy (ORCHARD-BEET)

Priscilla Smith, Danielle Ashworth, Rachel Hung, Yanzhong Wang, Andrew J. Webb, Kathryn Dalrymple, Katherine Clark, Lucy C. Chappell, Kate Wiles, Kate Bramham, on behalf of the ORCHARD Consortium

DOI: 10.1016/j.ekir.2026.106704

Why was the study needed?

CKD affects 0.2% to 6% of pregnancies (Williams D & Davison J, BMJ, 2008| Piccoli GB et al, J Clin Med, 2018), rising with diabetes, obesity, hypertension, and delayed childbearing (GBD 2015 Maternal Mortality Collaborators, Lancet, 2016). In stages G3-G5 CKD, ~46% of women lose over a quarter of pre-pregnancy eGFR or need renal replacement therapy within 12 months of delivery. (Wiles K et al, NDT, 2021). Outcomes haven’t improved in two decades (Smith PA et al, Nat Rev Nephrol, 2025), and no dedicated interventional trial has run since Kincaid-Smith’s 1995 heparin/ dipyridamole study (Nephrology, 1995)- a three-decade gap ORCHARD-BEET was designed to address (Smith P et al, KI reports, 2026). 

RAAS inhibitors and SGLT2 inhibitors, the strongest CKD progression-slowing agents, are contraindicated in pregnancy and lactation because of fetal risk and insufficient breastfeeding safety data (Wiles K et al, BMC Nephrol, 2019). Dialysis, meanwhile, is neither universally accessible nor scalable. KDIGO 2024 itself concedes that pregnancy-specific management evidence beyond monitoring remains unclear (KDIGO CKD Work Group, KI, 2024). 

Nitric oxide physiology: normal pregnancy, CKD, and preeclampsia

Normal pregnancy
Nitric oxide (NO) drives two relevant adaptations. Renally, NO-mediated afferent/efferent arteriolar dilation lowers vascular resistance and raises renal blood flow and GFR- the physiological hyperfiltration of pregnancy. In the placenta, NO drives trophoblast remodeling of spiral arteries from narrow, high-resistance vessels into wide, low-resistance ones able to meet fetal demand.

CKD
NO bioavailability falls via impaired synthesis (Wever R et al, Arterioscler Thromb Vasc Biol, 1999) and accumulated NO synthase (NOS) inhibitors- such as asymmetric dimethylarginine (ADMA) (Schmidt RJ, Kidney Int, 2000). A kidney entering pregnancy with impaired NO synthase activity cannot mount the normal hyperfiltration response, which is a key contributor to eGFR loss. 

Preeclampsia
Same deficit, different organ: reduced NO and endothelial dysfunction impair spiral artery remodeling, leaving the placenta underperfused, triggering anti-angiogenic factor release, maternal endothelial dysfunction, and fetal growth restriction- contributing to, but not strictly causing it. CKD and preeclampsia share this mechanism, explaining CKD pregnancies’ elevated preeclampsia risk.

Dietary nitrate as an NO synthase-independent bypass

Oral/ enteric bacteria reduce nitrate to nitrite, then to NO, a route Lundberg, Weitzberg and Gladwin showed works independently of the classical L-arginine-NOS route (Lundberg JO et al, Nat Rev Drug Discov, 2008), and one preferentially active in hypoxic, acidic tissue, exactly where NOS activity is the weakest. Nitrite-mediated vasorelaxation of human placental chorionic plate arteries is enhanced under hypoxia (Tropea T et al, Nitric Oxide, 2018), and low-dose nitrite restores renal tissue oxygenation and limits the creatinine rise following ischemia-reperfusion injury in animal models (Cantow K et al, Sci Rep, 2018). This gives the intervention a mechanistic rationale specific to the compromised, hypoxic tissue found in pregnancy with CKD and preeclampsia.

Beetroot juice had already shown plausible benefit and acceptable safety in hypertensive, non-CKD pregnancy (Ormesher L et al, Nitric Oxide Biol Chem, 2018), and in non-pregnant CKD cohorts (Kemmner S et al, Nitric Oxide Biol Chem, 2017| Webb AJ et al, Hypertens Dallas Tex, 2008). The effect of beetroot juice on blood pressure itself has been reported in several, albeit all small, RCTs (Gronroos et al, Nutr Metab CV, 2024), though whether this effect is mediated through nitrates or not may be debatable (Sagar et al, KI Reports 2024). 

This was a feasibility study, not an efficacy trial- designed to test whether recruitment was achievable and to gather preliminary safety/signal data for a future powered trial.

How was the trial done? 

Design

Trial-within-a-cohort (“modified Zelen”) design (Figure 1): participants first consented to the ORCHARD observational cohort and then were randomized 1:1 to dietary nitrate or standard care. Only those allocated to the nitrate were approached for a second consent to actually receive it; standard care participants were enrolled automatically. This structure, originally proposed by Zelen in 1979, reduces recruitment bias, but it weakens the crossover: even modest rates bias estimates and cut power (Homer CSE, J ADV Nurs, 2002).

Setting and population: 8 UK tertiary centers, February 2020-April 2024, with a 6-month pause for COVID-19. Eligible patients included singleton pregnancies, KDIGO-defined G2-G5 CKD, <25 weeks’ gestation, pre-pregnancy eGFR >90 ml/min/1.73 m² or pregnancy creatinine >70 μmol/l (0.79 mg/dl) without evidence of acute kidney injury.
Intervention vs comparator: Daily 70 mL “Beet-It-Sport” shot (approx. 400 mg nitrate) from consent or 12 weeks’ gestation (whichever was later) until delivery, versus standard care. The trial was open-label, without a placebo due to expense.
Follow-up and outcomes: Up to 5 visits through 6 months postpartum, assessing kidney function, proteinuria, blood pressure, and maternal/neonatal adverse events. The primary outcome was recruitment rate per site per month. 
Intention-to-treat: All standard care participants were analyzed. Only nitrate-arm participants who gave the second consent were analyzed. This departs from the classical Zelen analysis (which retains everyone originally randomized); a deviation the authors acknowledge as a limitation.
Of 119 eligible women, 108 were randomized, yet only 30/54 allocated to nitrate consented versus 53/54 retained in standard care (Figure 2)- crossover made concrete, and a reminder that the analyzed nitrate arm is self-selected, not the full allocated group.

Results

The primary outcome- recruitment feasibility- was achieved, with a mean recruitment rate of 1.01 participants per site per month (SD 0.90)-useful for planning, likely an underestimate in higher-burden settings.

The nitrate arm had numerically less chronic hypertension and markedly fewer Black participants to standard care, both established risk factors for CKD progression; despite comparable pre-pregnancy eGFR between arms, these imbalances were not statistically adjusted for in subsequent comparisons.

Kidney outcomes were directionally suggestive rather than statistically definitive, though biologically coherent. Among participants with pre-pregnancy eGFR below 45 ml/min/1.73 m², postpartum creatinine tended to be lower with dietary nitrate (176 vs 226 μmol/l at week 6, p= 0.23, 173 vs 216 at μmol/l 6 months, p=0.18), whereas in the milder CKD stratum (Figure 3c, eGFR > 45) the 2 arms’ trajectories were superimposable- consistent with the hypothesis that benefit concentrates where baseline NO deficiency is greatest.

Figure 3. (a) Median eGFR changes from prepregnancy to 6 months postpartum, by randomization eGFR group and treatment. (b) Median creatinine by intervention. (c) Median creatinine by intervention and randomization eGFR group, from Smith P et al, KI reports, 2026

There were no significant differences in delivery mode, gestational age, or birthweight centile but a numerically lower NICU/neonatal unit admission rate with nitrate use (23.3% vs 39.6%, p= 0.13) and a higher median birthweight. The authors note this magnitude of NICU admission reduction is comparable to that reported for antenatal corticosteroids in the updated Cochrane review of preterm birth (Roberts D et al, Cochrane Database Syst Rev, 2026), though this remains a descriptive comparison across studies rather than a direct statistical contrast.

Blood pressure approached but missed significance (Table 3): SBP 120.3 vs 123.2 mmHg (p= 0.064) and DBP 75.4 vs 77.7 mmHg (p= 0.051). A magnitude comparable to both the CHAP trial of mild chronic hypertension treatment in pregnancy (Tita AT et al, NEJM, 2022| NephJC summary) and non-pregnant dietary nitrate meta-analyses (Ashor AW et al, J Hypertens, 2017).

The single result reaching statistical significance appears in Table 4. Serious adverse events occurred in 23.3% of the nitrate arm vs 50.9% of standard care (OR 0.29, 95 CI 0.11-0.80), with no nitrate-attributable SAEs, no congenital abnormalities, and hyperkalemia above 6 mmol/L occurring in one participant per arm. A post hoc analysis found lower antihypertensive use with nitrate use (36.7% vs 66%, p= 0.0097). This analysis was explicitly designated post hoc by the authors and therefore is hypothesis-generating. 

Only 20.8% enjoyed the intervention-  taste, nausea, reflux, and beeturia (harmless urinary discoloration) were the main complaints- yet 62.5% would repeat it, and 70.8% would recommend it.

What are the implications?

The one significant finding- the SAE reduction- is also its most bias-prone result: open-label trials leave SAE ascertainment and decisions like antihypertensive initiation vulnerable to detection and performance bias without blinding. A nitrate-depleted placebo had worked before (Ormesher L et al, Nitric Oxide Biol Chem, 2018), so the comparator was feasible, just not funded here; its absence weakened every clinical outcome. Baseline imbalances compound this, as does the Zelen crossover itself: with nearly half the nitrate arm declining the second consent, the analyzed group is a self-selected, likely more engaged subgroup against a near-complete standard-care arm- exactly the bias methodological literature predicts, and one of the authors acknowledges. 

Generalizability is also limited: women from lower socioeconomic groups and Black ethnicity - disproportionately affected by CKD and its pregnancy complications- were underrepresented in the intervention arm, partly from COVID-related recruitment disruption at sites serving them, limiting applicability to the population with the greatest disease burden. The mechanistic picture is incomplete too: planned salivary/ tongue sampling to characterize oral nitrate-reducing capacity was abandoned for infection-control reasons- a real gap since the pathway depends on bacterial nitrate reduction, and interindividual variation in this capacity (Willmott T et al, Hypertens Dallas Tex, 2023) plausibly explains some of the blood pressure heterogeneity (Lundberg JO et al, Nat Rev Drug Discov, 2008).

Conclusion

After thirty years without a randomized trial in CKD pregnancy and a pandemic working against it throughout, ORCHARD-BEET deserves credit for being a well-conducted, transparently reported multicenter RCT in a largely abandoned field. It met its stated goal: feasibility, not efficacy, was the primary outcome, and 1.01 participants per site per month with 76.8% completion gives solid planning data for what comes next.
It was not, and was never meant to, prove efficacy. The kidney, blood pressure, and neonatal trends are encouraging and biologically coherent, especially in more advanced CKD, but stem from a modified Zelen analysis that lost nearly half its intervention arm to a second consent in an open-label trial with no placebo.
Put simply, this is a solid proof of concept. Women with CKD will enroll in interventional pregnancy trials; the intervention raised no safety concerns, and the field now has the numbers it needs to design a properly powered trial that should follow. Closing a thirty-year gap is, on its own, quite an accomplishment.

                                                                                         By Cristina Popa

Reviewed by

Brian Rifkin, Swapnil Hiremath

Nondialyzable versus Dialyzable Beta-Blockers in Hemodialysis: A Target Trial Emulation Study

Ali Etemadi, Sai Liu, Wolfgang C Winkelmayer, Maria E Montez-Rath, Tara I Chang

PMID: 42160184

DOI: 10.1681/ASN.0000001147

Why was the study done?

Beta blocker use, although well established in cardiovascular disease, has been embroiled in controversy when it comes to their protective effects in patients in CKD (Han Hyung Seung et al, NDT 2026;  Yeh Tzu-Hsuan et al, PLOS One 2022).  Cardioselectivity and dialyzability are two major factors influencing physician choice in patients on dialysis. Non-cardioselective beta blockers, like bisoprolol, have proven mortality benefit in heart failure (Hans-Dirk Düngen  et al, Euro J Heart Fail, 2011) with some evidence describing their superiority in CKD (Shireman TI et al, BMC CVD 2016; Yu Albert et al, JASN supplement 2022). However, they may predispose to hyperkalemia, especially in patients on mineralocorticoid receptor blockers (Wu PH et al, NDT  2020).  Alternatively, non-dialyzable beta blockers may provide consistent cardioprotective benefit,  they may also predispose to bradycardia and intradialytic hypotension (Yeh Tzu-Hsuan et al, PLOS One 2022).  

Unfortunately, to date, observational data comparing dialyzable vs non dialyzable beta blockers use in dialysis populations has given mixed signals of cardiovascular benefits

(Weir et al JASN 2014; Assimon et al, AJKD 2019; Wu et al, NDT 2020). Importantly, apart from dialyzability, beta blockers have other pharmacokinetic and pharmacodynamic properties such as cardioselectivity, vasodilatory capacity, lipophilia which may affect outcomes. Cardioselective betablockers have been demonstrated to be more beneficial in dialysis patients compared to non-selective beta blockers (Shireman at al, BMC CV Dis 2016; Meta-analysis-Tao et al, PloS 2022).

Due to the inconsistent data regarding the benefit of one class over another, prescription practices vary widely and are often influenced by availability, insurance coverage, familiarity, and peer usage. Considering the clinical equipoise, the high variability in practices, and the difficulty in conducting RCTs in hemodialysis population, this target trial emulation study was done to demystify this controversial area. 

How was the study done and what did it report? 

Methods

This was a target trial emulation study (Hernán  M et al, JAMA 2022) using retrospective data from USRDS, Medicare and Davita to predict the outcomes of a randomized controlled trial of dialyzable versus non dialyzable beta blockers in CKD patients initiating hemodialysis.  Retrospective data from USRDS was obtained for patients with Medicare A, B, and D from 2008-2021. Elderly patients ≥ 67 years initiating hemodialysis while on beta blockers, with at least claim for hypertension in the past, were included in the study from 41 US states .

Inclusion and exclusion criteria are detailed below:

Adapted from Supplemental table 1 from Etemadi et al, JASN 2026

Nomenclature /definitions:

• Dialyzable beta blockers: metoprolol, atenolol, bisoprolol, acebutalol

• Non dialyzable beta blockers: carvedilol, propranolol, betaxalol

• Classification of patients by type of beta blockers was based on whether they had only one active prescription supply for either a dialyzable or a nondialyzable beta blocker on index date or within a 30-day grace period prior to index date.

Outcomes:

All-cause mortality and major adverse cardiac events  (MACE-defined as stroke, myocardial infarction, or all-cause mortality) were studied as per intention to treat analyses. Negative control outcomes (unrelated outcomes like conjunctivitis, contact dermatitis, onychomycosis, contact dermatitis) were analyzed both in the instrumental variable approach and the Cox model. Data were obtained from ICD codes used in Medicare.

Statistical procedures:

Quasi-randomized treatment groups were made based on an instrumental variable (IV) approach using regional prescribing preferences. Regions with a preferential prescription were included. 

RESULTS:

A total of 161,798 patient records were screened for eligibility, of which 80,353 patients were eligible for inclusion in the emulation study (Figure 1 below). Since only patients residing in areas with a preferential prescription (ATR>0.8) were included in the instrumental variable (IV) approach, the final sample size was 40,313 (n= 19,833 in the non-dialyzable beta blocker group, n=20,480 in the dialyzable beta-blocker group). 

Baseline characteristics (Table 1 below) were balanced between the two groups -patients residing in areas with preferential dialyzable vs preferential non-dialyzable beta blocker use (SMD <0.1 for all characteristics). Although p value should not be calculated for comparing baseline characteristics in randomized trials, a SMD in target trial emulation helps to determine if the quasi-randomization has genuinely been able to create balanced groups.  When actual group of patients receiving dialyzable versus non-dialyzable beta blockers was compared (supplemental table 3), the group of patients receiving non-dialyzable beta blockers were found to have a higher prevalence of congestive heart failure, ischemic heart disease, chronic pulmonary disease, complicated hypertension and diabetes,  higher mean Elixhauser mortality score, more frequent cardiology visits, and higher baseline diuretic use (probably reflecting sicker baseline status with higher comorbidity burden).

MACE and its individual component outcomes were significantly lower in the arm with preferential non-dialyzable beta blocker use compared to dialyzable beta blocker use.

(Figure 3 below). There was no significant association of any of the negative outcomes (conjunctivitis, cerumen accumulation, onychomycosis) with beta blocker dialyzability.

In the fully adherent cohort , the outcomes showed similar trends, shown in Figure 3, except for the outcome of stroke which was not significantly associated with beta blocker dialyzability at 6 months, 1 year or 3 years. 

Overall there were 25,720 MACE over the 3-year follow-up period, with 10,933 occurring in the first 6 months. The table below shows the incidence of MACE and individual outcomes at  6 months, 1 year and 3 years. The incidence of each outcome and of the composite outcome was highest during the first 6 months after hemodialysis initiation. 

Using a primary Cox model, hazard ratios for MACE, acute MI and stroke at 6 months, 1 year and 3 years showed a significant advantage of using non-dialyzable beta blockers in HD. However, negative outcomes (conjunctivitis, cerumen accumulation, contact dermatitis , onychomycosis) were found to be associated with beta blocker dialyzability at different time points after HD initiation. (A smart way to demonstrate the advantage of using target trial emulation over the Cox model!)

DISCUSSION:

Beta blockers are a frequent antihypertensive choice in patients on hemodialysis, especially in those with cardiac disease. The data regarding the use of dialyzable versus non-dialyzable beta blockers in the dialysis population has been fraught with controversy.

This study analysed data from >40,000 patients who were initiated on dialysis to compare outcomes in regions where there was prescription preference for dialyzable vs non-dialyzable beta blockers. The results demonstrated a significant benefit of non-dialyzable beta blockers in preventing MACE and its individual components.This target trial emulation study tried to bring an end to the controversy, but can anything but an RCT be the final answer ?

 Unfortunately, large scale RCTs on beta blocker use in hemodialysis have failed to reach completion due to various reasons (see table below).The first RCT of beta blockers in dialysis patients was published as early as 2001 (Cice G et al, JACC 2001) - it demonstrated that carvedilol decreased all cause mortality in dialysis patients with dilated cardiomyopathy. Subsequent to this small study (n=117), there was one RCT of atenolol vs lisinopril in HD patients (Agarwal et al, NDT 2014; HDPAL study)  which was terminated early due to more serious adverse events including CV events, all cause mortality , hyperkalemia and hypertensive crisis in the lisinopril arm. A feasibility trial of beta blocker vs placebo use in dialysis patients

(Roberts et al, AJKD 2016; BLOCADE) was unable to recruit patients due to high baseline use of beta blockers, apprehension of nephrologists to switch patients to a non-beta blocker, inability to get informed consent and a high drop out rate (with intradialytic hypotension and bradycardia). 

The study by Etemadi et al (JASN 2026)  had the strengths of using an instrumental variable approach and a target trial emulation design with a large sample size. It was a very well designed statistical approach with clear demonstration of techniques used to check independence, relevance and exclusion restrictions. They also demonstrated comparison with a Cox model to highlight the difference in results and flaws of using a Cox regression analysis in situations where RCTs are ideal (and trial emulations a distant second choice). Analysis was done both on ITT and the adherent population to bring out the differences. 

Limitations include the inherent limitations of a retrospective cohort and the use of medicare data rather than actual outcomes. The generalizability may be limited to older patients (age >67 years) and those with medicare coverage (adherence could also be influenced by that). Incidence of bradyarrhythmias and intradialytic hypotension episodes were not available to draw any mechanistic conclusions. Lastly PD patients were not included , similar to all previous studies quoted above of beta blockers in dialysis patients. 

Conclusion: 

In this target emulation trial, MACE and its individual component outcomes were significantly lower in the arm with preferential non-dialyzable beta blocker use compared to dialyzable beta blocker use.

This study also highlights the need for adequately powered RCTs in HD patients to study the effect of beta blockers accounting for dialyzability, cardioselectivity and other physicochemical factors unique to different drugs in this class.

                                                                                         Blog by Pallavi Prasad

Associate Professor Nephrology, VMMC and Safdarjung Hospital 

New Delhi, India

Reviewed by

Brian Rifkin, Cristina Popa

JAMA. 2026 Jul 30. doi: 10.1001/jama.2026.9268. Online ahead of print.

Dapagliflozin and Acute Kidney Injury Following Cardiac Surgery: A Randomized Clinical Trial

Maartina J P Oosterom-Eijmael, Abraham H Hulst, Nelson P Monteiro de Oliveira, Ed D Niesten, Nicobert E Wietsma, Bastiaan M Gerrits, Thierry V Scohy, Thijs C D Rettig, Ferdinand T F Snellen, Magiel F Voogd, Marc B Godfried, Rients N de Boer, Jeroen Wink, Lisa M M van der Werff, Christa M Cobbaert, L Renee Ruhaak, Susanne Eberl, Benedikt Preckel, Markus W Hollmann, Jimmy Schenk, Jeroen Hermanides, Daniel H van Raalte; MERCURI-2 Study Group

PMID: 42530910

DOI: 10.1001/jama.2026.9268

Introduction

SGLT2 inhibitors (SGLT2i or Flozins) are awesome! So much so that we at NephJC call them “The Undefeated”. A miracle class of drugs that works in chronic kidney disease (CKD) with or without diabetes, heart failure with or without reduced ejection fraction, proteinuric nephropathies; however, even Superman had his limits. 

Acute kidney injury (AKI) is a significant health concern, especially among hospitalized cardiac surgery patients because it contributes to increased length of stay, morbidity, mortality, and costs (Fuhrman DY, Curr Opin Anaesthesiol 2017). The incidence of cardiac surgery-associated AKI occurs in up to 50% of cases, and 2%–5% of these patients require renal replacement therapy during an AKI episode (Fuhrman DY, Curr Opin Anaesthesiol 2017; Brown JK et al, J Cardiothorac Vasc Anesth 2023). Thus far, the prevention and management of AKI remains largely supportive - focusing on fluid balance, optimizing hemodynamics, and nephrotoxin avoidance. 

Flozins provide kidney-protective benefits theoretically by improving renal oxygenation, reducing oxidative stress, and decreasing tubular workload. In a recent meta-analysis, SGLT2i use was associated with a statistically significant decrease in both mortality (OR 0.73; CI 0.58-0.92) and acute kidney injury (OR 0.68; CI 0.61-0.76). However, like most meta-analyses, the outcomes and adverse effects data were muddied by considerable heterogeneity in data (Balbaa E et al, Endocrinol Diabetes Metab 2026). Hypothetically, SGLT2i should be beneficial in cardiac surgery patients, especially by lowering renal oxygen demands in the cardio-pulmonary bypass (CPB)  ischemia-reperfusion window. However, most current guidance recommends holding SGLT2i medications 3-4 days prior to any surgery because of the risk of euglycemic ketoacidosis, dehydration, and excess fluid loss. This study examines whether Flozins, given in the peri-operative period in cardiac surgery, can reduce the incidence of AKI.

Methods

Study design

MERCURI-2 was a multicenter, double-blind, placebo-controlled randomized clinical trial conducted in the Netherlands. Randomization (performed via Castor Electronic Data Capture) used computer-generated blocks of 4, 6, and 8, stratified by sex, type 2 diabetes, and study site. Allocation was visible only to the trial pharmacists, who prepared study medication but were otherwise uninvolved.

Study population

• Adults ≥18 years scheduled for any elective cardiac surgery (CABG or non-CABG) 

• Exclusion criteria included

• prior SGLT2 inhibitor or insulin use

• a history of diabetic ketoacidosis  

• baseline hypotension (defined by systolic blood pressure <100 mmHg) 

• eGFR <20 ml/min/1.73 m² 

• Pregnancy 

• Any contraindications based upon drug labeling

Intervention

Once randomized, patients received either placebo (a cellulose-containing excipient) or  AstraZeneca-dapagliflozin (encapsulated in-house by the academic trial pharmacy for blinding purposes - AstraZeneca supplied the source tables but was not a trial funder; more on that distinction below). 

The first dose was given the day before surgery, followed by daily doses until the second postoperative day (four doses total).

Outcomes
Primary Outcome

Incidence of AKI up to 7 days after cardiac surgery, defined by the 2012 KDIGO criteria:

• An increase in serum creatinine level of 0.3 mg/dL (26.5 μmol/L) or greater within 48 hours, or

• 1.5-fold or greater increase in creatinine level within 7 days of surgery, or

• Urine output less than 0.5 mL/kg/h for 6 to 12 hours after surgery.

They used the most recently obtained creatinine as the reference value, which mostly corresponded to the blood test taken the day before the surgery, though they don't actually report what percentage that applied to. Following the procedure, serum creatinine values were obtained daily during the first 7 days after the intervention. All patients had a urinary catheter in place for the entire 7-day period to measure urine output.

Secondary outcomes

• Stage 3 AKI as per KDIGO 2012 criteria 

• Postoperative maximum change in eGFR within the first 7 days

• De novo postoperative atrial fibrillation recorded on a 12-lead ECG; length of stay in the ICU  and hospital

• Incidence of major adverse kidney events (MAKE30): composite endpoint of death, new dialysis, or a persistent increase in serum creatinine concentration to 200% or more of the baseline, 30 days after the operation*

• Incidence of major adverse cardiac events (MACE30): composite endpoint of cardiovascular death, nonfatal myocardial infarction, nonfatal ischemic cerebrovascular accident, and hospitalization for heart failure, 30 days after the operation*

• World Health Organization Disability Assessment Schedule 2.0 (WHO-DAS 2.0) score within 30 days after the operation

• Days at home in the first 30 days (DAH30) after the operation

• EuroQol 5 dimensions 3 Levels (EQ-5D-3L) score measured at 30 days after surgery

Safety outcomes:  genital mycotic infections, ketoacidosis,  hypoglycemia

*NephJC writer’s note: The text of the article has the definitions for MACE30 and MAKE30 switched up. This text has been optimized for understanding purposes.

Funding

The study was supported by the Netherlands Organization for Health Research and Development (ZonMw), the European Society of Anaesthesiology and Intensive Care and BJA Charity, and the Netherlands Organization for Health Research and Development.

Dr. van Raalte (the last author) receives grants from AstraZeneca and is supported by a fellowship of the Dutch Diabetes Foundation and a Kolf fellowship from the Dutch Kidney Foundation.

Results

Out of 2551 considered, a total of 784 patients were randomized. Figure 1 lists 382 per arm as "included in primary analysis”- the full randomized cohort. But only 778 of 784 (99%) completed follow-up testing, and six patients never had surgery at all. 

Baseline characteristics were strikingly similar between groups; a median age of 68 years (IQR: 61–74) and a median BMI of 27 kg/m2. Male participants comprised over 75% of each arm, and white individuals accounted for more than 95% in both groups. Median eGFR was 80 mL/min/1.73 m² among both groups at baseline (Table 1). Surgical and clinical baseline characteristics as well as prior medication use were consistent across groups. Adherence to study protocol was high in both arms: 88% of the dapagliflozin group and 87% of the placebo group completed all four doses.

Table 1: Baseline and surgery characteristics from Oosterom-Eijmael MJP et al, JAMA 2026.

Primary Outcome

AKI occurred in 111 (28%) participants in the dapagliflozin group and in 205 (52%) participants in the placebo group (risk difference, −23.98% [−30.64% to −17.32%]; relative risk [RR], 0.54 [95% CI, 0.45-0.65]; P < 0.001).

Most primary-outcome events happened in the first 24-48 hours after surgery. That’s quick - closer to what you’d expect from transient postoperative oliguria (a normal, reversible dip in urine output from anesthesia, fluid shifts, or vasopressors) than from real structural kidney injury (see at-risk numbers under the curve), see discussion below.

Secondary Outcomes

Three (0.8%) in the dapagliflozin group and one (0.3%) in the placebo group had stage 3 AKI, however, this was not statistically significant (RR 3; 95% CI 0.31-28.71). Postoperative maximum change in eGFR compared with baseline was −7.4mL/min/1.73 m² in the dapagliflozin group and −1.75 mL/min/1.73 m² in the placebo group, likely reflecting the hemodynamic dip often seen with SGLT2i initiation. There were no between-group differences in MACE30 (5.6% vs 6.1%), MAKE30 (1.3% vs 0.5%), duration of hospital stay, or days alive and at home within 30 days after surgery (DAH30). 

Also unchanged was ECG-confirmed new-onset atrial fibrillation (32% vs 31%). Note that this is a separate variable from the “atrial fibrillation” adverse event in Table 3, which captures any AF, not just new-onset cases confirmed by ECG. Patient-reported quality of life (EQ-5D-3L) and functional status (WHO-DAS) were also unchanged. 

Exploratory Outcomes

In exploratory analyses, using a per-protocol method, the incidence of AKI was 27% in the dapagliflozin group and 51% in the placebo group, yielding a similar RR as the ITT analysis.

Post Hoc Outcomes

When AKI was defined exclusively by creatinine criteria, AKI occurred in 14% of the dapagliflozin group and 15% of the placebo group (RR, 0.93 CI, 0.66-1.32). However, when defined exclusively by urine output criteria, AKI incidence was 21% in the dapagliflozin group and 48% in the placebo group (RR, 0.44 0.35-0.54), indicating that the entire benefit lies in the AKI by urine output definition. 

Adverse Events

Recommended practice is to hold Flozins a few days before surgery to reduce the risk of ketoacidosis. However, only one episode of ketoacidosis was reported in the dapagliflozin group. Similarly, mycotic infections only occurred in one dapagliflozin treated patient. The most common post hoc adverse events were atrial fibrillation (45% vs 45%) and reoperation (11% vs 10%), which were nearly identical between groups.

Table 3. Postoperative adverse events, from Oosterom-Eijmael MJP et al, JAMA 2026.

Discussion

AKI after major surgeries, especially cardiac surgery, remains a significant issue contributing to high costs, prolonged hospital stays, and increased morbidity. Diuretics, CCBs, bicarbonate, and statins have all failed to create a significant difference in the incidence of AKI (Zanardo G et al, J Cardiothorac Vasc Anesth 1993; Tie HT et al, Crit Care 2014; Zhao BC, J Cardiothorac Vasc Anesth 2017; Bove T et al, PLoS One 2018; Takeichi T et al, J Extra Corpor Technol 2025). Here, we have flozins appearing to finally make a breakthrough where so many others have failed…or do we?

Mercury in Roman mythology was the messenger of news, but he was also considered a trickster god who used cunning and sleight of hand to confuse patrons. Upon closer examination, this trial seems more trick than treat. Unfortunately, the stunning headline of reduced AKI incidence does not hold up to closer inspection whatsoever. 

The creatinine-based signal is null, with the CI crossing 1 across all stages. The entire treatment effect lives in the urine-output criteria. The authors say this outright in the discussion: “the high placebo incidence is likely due to the urine-output criteria.”

That's the whole ballgame, right there! Dapagliflozin is a glucosuric osmotic diuretic. An agent that increases urine output will mechanistically make patients less likely to trip a low-urine-output threshold, independent of anything else happening in the nephron. The authors anticipated this and tried to defuse it by citing steady-state, fixed-sodium-intake, euvolemic, mildly hyperglycemic outpatient PK/PD studies (Scholtes RA et al, Diabetes Care 2021; Heise T et al, Clin Ther 2016) showing Flozins aren't meaningful diuretics beyond the first dose. 

But that literature isn’t appropriate here and highlights why indirectly comparing trial results is fraught with issues. Post-cardiac-surgery patients are not necessarily euvolemic, and usually hyperglycemic from surgical stress (catecholamines, cortisol), which means more filtered glucose, more glucosuria, more osmotic diuresis during the short 7 day window used to adjudicate the outcome. 

This is a testable hypothesis, and the authors had the data to test it. Perioperative glucose measurements and hemodynamics were prespecified secondary/exploratory outcomes but are explicitly flagged as "not analyzed at the time of this report." (BOOM goes the kryptonite) That's a significant thing to hold back given it bears directly on whether this is renoprotection or an osmotic-diuresis artifact. The same trick could be pulled off with hydrochlorothiazide dosing. 

The -7.4 ml/min/1.73 m² eGFR decline with dapagliflozin in MERCURI-2 in retrograde, in more ways than one - the filtration rate goes backward, and so does the “usual dip” explanation invoked to excuse it. CREDENCE’s (Perkovic V et al, NEJM 2019) eGFR dip averaged 3-4 ml/min/1.73 m² at 3 weeks (baseline 56); EMPA-REG’s (Zinman B et al, NEJM) was 2.7 ml/min/1.73 m² at 4 weeks (baseline 74)- Both drops were smaller and in stable outpatients over weeks. A “more normal” kidney startpoint doesn’t explain the size either: DECLARE-TIMI58 (Wiviott SD et al, NEJM 2018), the closest eGFR match (median 85), showed a smaller dip than EMPA-REG, not larger. The dip tracks baseline hyperfiltration, not baseline eGFR - a post hoc DINAMO analysis found a significant dip only in hyperfiltering patients (-11.7 ml/min/1.73 m²), not in normofiltering ones (Bjornstad P et al, CJASN 2025). MERCURI-e doesn’t report hyperfiltration status. The acute surgical/CPB context remains the likelier explanation. 

In POST-CABGDM (Pitta FG et al, Diabetes Care 2025), using a creatinine-only AKI definition, the administration of empagliflozin in diabetic on-pump CABG patients - a population MERCURI-e excluded - showed a significant reduction (22.5% vs 29.1%, RR 0.57; CI 0.34-0.96), the inverse of MERCURI-2’s null creatinine finding. But it was a small, single-center, and open-label trial; an unblinded design is a real vulnerability for an outcome as sensitive to perioperative fluid and vasopressor management as AKI. Furthermore, the drug was stopped 72 hours before surgery, so its link to the acute injury window is indirect at best. 

A 2026 meta-analysis of 13 chronic dosing RCTs (84,581 patients; Chiang YC et al, Kidney Med 2026) found a 20% AKI reduction using largely creatinine/adjudicated definitions that exclude transient fluctuations - a different philosophy than MERCURI’s urine-output inclusive composite. Together, they suggest a real, more modest creatinine-based signal may exist that MERCURI-2 itself didn’t reproduce.

MERCURI’s associated editorial (Winkelmayer & Chertow, JAMA 2026) counters: in a 105-patient Amsterdam subset, patients on dapagliflozin got less IV fluid and norepinephrine, suggesting true kidney function rather than forced diuresis (not verifiable from the JAMA manuscript). It also cites lower KIM-1/TIMP-2xIGFBP7 from the earlier open-label pilot, a smaller, unblinded, non-MERCURI-2 cohort.

Limitations

OK take a deep breath…

The authors themselves describe up to 11 limitations in their study, some of them potentially questioning the entirety of their results. Let us start by stating that evaluating results just 7 days after starting a drug can potentially prove your intervention to be null across most meaningful endpoints. Using criteria with questionable clinical weight, such as transient oliguria without serum creatinine changes, could be disregarded as a mere hemodynamic change rather than a true kidney injury.

There was no mention of any other AKI prevention protocols implemented. Without data on pre-op discontinued medications (specifically RASi), fluid therapy, hemodynamic shifts, or even how old baseline creatinine values actually were, it's really hard to take those AKI numbers at face value. On top of that, there were no urine albumin measurements or differential diagnoses for the AKI cases they found. The use of  kidney damage biomarkers and/or MAKE30 in the primary outcome may have given us a better idea about the role of Flozins in preventing AKI. Similarly, the study does not state the need for post-op vasoactive drugs, fluid therapy, or initiation of additional drugs besides dapagliflozin.

The amount of secondary outcomes is difficult to square, and at the same time, there were endpoints that were registered, but ultimately not analyzed, including important end points like health care and productivity costs, hemodynamic vital signs and cardiac function.Why include them as endpoints if you’re not going to use the data in your published report? Given the results for the kidney outcomes, we could perhaps assume they just didn’t include those other endpoints because those results were also not statistically significant.

This study population was extremely homogeneous, with 97% white individuals, >75% males. While this may be explained away by geographical location of the study and higher prevalence / earlier age onset of cardiac disease in men vs women (though some studies suggest men are more aggressively treated for their cardiac disease than women), this heterogeneity does limit generalization despite the murky results. Furthermore, the medication provided was an SGLT2i drug from AstraZeneca, which is mentioned as one of the grant supporters for one of the main authors. Sponsorship bias alert. 

The authors go on to frame limitations as potential benefits? “Specific treatment protocols for AKI prevention were not implemented. The absence of study-specific protocols enhances the generalizability of the findings to routine clinical practice.” That second sentence should really give us food for thought. In fact, a better designed trial could have had multiple arms: one could imagine a trial with AKI prevention bundle +/- dapagliflozin versus placebo could perhaps produce more robust findings and elucidate if flozins are providing anything more than diuresis. 

To add fuel to the fire, the paper is littered with multiple spelling and grammatical errors.

Conclusion

In adults undergoing elective cardiac surgery, perioperative dapagliflozin did lower the incidence of postoperative AKI strictly according to the KDIGO AKI 2012 criteria at Day 7. However, the benefits were driven by urine output criteria, whereas serum creatinine remained completely unaltered. Although the study shows a large effect size, the inappropriate choice of the primary outcome measure marred the interpretation of this ambitious study. 

All in all, we have seen our superhero SGLT2i sullied by this poorly designed study. Fear not citizens of Nephropolis, SuperFlozinators will live to fight another day (in a much more logical and worthwhile study like The Renal Lifecycle Trial due to be completed in 2027). Until then, don’t hesitate…flozinate!

Summary by

David White FNP-C

Raquel Barba Teba Nephrology specialist, Spain

Reviewed by

Akshaya Jayachandran, Brian Rifkin, Cristina Popa,
Jade Teakell, Pallavi Prasad

Header Image created by AI, based on prompts by

Brian Rifkin

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