The probability of receiving a kidney transplantation in end-stage kidney disease patients who are treated with haemodiafiltration or haemodialysis: a pooled individual participant data from four randomised controlled trials

BACKGROUND: Due to a critical shortage of available kidney grafts, most patients with Stage 5 Chronic Kidney Disease (CKD5) require bridging dialysis support. It remains unclear whether treatment by different dialysis modalities changes the selection and/or preparation of a potential transplant candidate. Therefore, we assessed whether the likelihood of receiving kidney transplant (both living or deceased kidney donors) differs between haemodialysis (HD) and online haemodiafiltration (HDF) in patients with CKD5D. METHODS: Individual participant data from four randomised controlled trials comparing online HDF with HD were used. Information on kidney transplant was obtained during follow-up. The likelihood of receiving a kidney transplant was compared between HD and HDF, and evaluated across different subgroups: age, sex, diabetes, history of cardiovascular disease, albumin, dialysis vintage, fistula, and level of convection volume standardized to body surface area. Hazard ratios (HRs), with corresponding 95% confidence intervals (95% CI), comparing the effect of online HDF versus HD on the likelihood of receiving a kidney transplant, were estimated using Cox proportional hazards models with a random effect for study. RESULTS: After a median follow-up of 2.5 years (Q1 to Q3: 1.9-3.0), 331 of the 1620 (20.4%) patients with CKD5D received a kidney transplant. This concerned 22% (n = 179) of patients who were treated with online HDF compared with 19% (n = 152) of patients who were treated with HD. No differences in the likelihood of undergoing a kidney transplant were found between the two dialysis modalities in both the crude analyse (HR: 1.07, 95% CI: 0.86-1.33) and adjusted analysis for age, sex, diabetes, cardiovascular history, albumin, and creatinine (HR: 1.15, 95%-CI: 0.92-1.44). There was no evidence for a differential effect across subgroups based on patient- and disease-characteristics nor in different categories of convection volumes. CONCLUSIONS: Treatment with HD and HDF does not affect the selection and/or preparation of CKD5D patients for kidney transplant given that the likelihood of receiving a kidney transplant does not differ between the dialysis modalities. These finding persisted across a variety of subgroups differing in patient and disease characteristics and is not affected by the level of convection volume delivered during HDF treatment sessions

Personalizing treatment in end-stage kidney disease: deciding between haemodiafiltration and haemodialysis based on individualized treatment effect prediction

Background: Previous studies suggest that haemodiafiltration reduces mortality compared with haemodialysis in patients with end-stage kidney disease (ESKD), but the controversy surrounding its benefits remains and it is unclear to what extent individual patients benefit from haemodiafiltration. This study is aimed to develop and validate a treatment effect prediction model to determine which patients would benefit most from haemodiafiltration compared with haemodialysis in terms of all-cause mortality. Methods: Individual participant data from four randomized controlled trials comparing haemodiafiltration with haemodialysis on mortality were used to derive a Royston-Parmar model for the prediction of absolute treatment effect of haemodiafiltration based on pre-specified patient and disease characteristics. Validation of the model was performed using internal-external cross validation. Results: The median predicted survival benefit was 44 (Q1-Q3: 44-46) days for every year of treatment with haemodiafiltration compared with haemodialysis. The median survival benefit with haemodiafiltration ranged from 2 to 48 months. Patients who benefitted most from haemodiafiltration were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine and albumin levels. Internal-external cross validation showed adequate discrimination and calibration. Conclusion: Although overall mortality is reduced by haemodiafiltration compared with haemodialysis in ESKD patients, the absolute survival benefit can vary greatly between individuals. Our results indicate that the effects of haemodiafiltration on survival can be predicted using a combination of readily available patient and disease characteristics, which could guide shared decision-making

Long-term peridialytic blood pressure changes are related to mortality

Background: In chronic haemodialysis (HD) patients, the relationship between long-term peridialytic blood pressure (BP) changes and mortality has not been investigated. Methods: To evaluate whether long-term changes in peridialytic BP are related to mortality and whether treatment with HD or haemodiafiltration (HDF) differs in this respect, the combined individual participant data of three randomized controlled trials comparing HD with HDF were used. Time-varying Cox regression and joint models were applied. Results: During a median follow-up of 2.94 years, 609 of 2011 patients died. As for pre-dialytic systolic BP (pre-SBP), a severe decline (≥21 mmHg) in the preceding 6 months was independently related to increased mortality [hazard ratio (HR) 1.61, P =. 01] when compared with a moderate increase. Likewise, a severe decline in post-dialytic diastolic BP (DBP) was associated with increased mortality (adjusted HR 1.96, P <. 0005). In contrast, joint models showed that every 5-mmHg increase in pre-SBP and post-DBP during total follow-up was related to reduced mortality (adjusted HR 0.97, P =. 01 and 0.94, P =. 03, respectively). No interaction was observed between BP changes and treatment modality. Conclusion: Severe declines in pre-SBP and post-DBP in the preceding 6 months were independently related to mortality. Therefore peridialytic BP values should be interpreted in the context of their changes and not solely as an absolute value

The importance of considering competing treatment affecting prognosis in the evaluation of therapy in trials: the example of renal transplantation in hemodialysis trials.

Background: During the follow-up in a randomized controlled trial (RCT), participants may receive additional (non-randomly allocated) treatment that affects the outcome. Typically such additional treatment is not taken into account in evaluation of the results. Two pivotal trials of the effects of hemodiafiltration (HDF) versus hemodialysis (HD) on mortality in patients with end-stage renal disease reported differing results. We set out to evaluate to what extent methods to take other treatments (i.e. renal transplantation) into account may explain the difference in findings between RCTs. This is illustrated using a clinical example of two RCTs estimating the effect of HDF versus HD on mortality. Methods: Using individual patient data from the Estudio de Supervivencia de Hemodiafiltración On-Line (ESHOL; n  =  902) and The Dutch CONvective TRAnsport STudy (CONTRAST; n  = 714) trials, five methods for estimating the effect of HDF versus HD on all-cause mortality were compared: intention-to-treat (ITT) analysis (i.e. not taking renal transplantation into account), per protocol exclusion (PP excl ; exclusion of patients who receive transplantation), PP cens (censoring patients at the time of transplantation), transplantation-adjusted (TA) analysis and an extension of the TA analysis (TA ext ) with additional adjustment for variables related to both the risk of receiving a transplant and the risk of an outcome (transplantation-outcome confounders). Cox proportional hazards models were applied. Results: Unadjusted ITT analysis of all-cause mortality led to differing results between CONTRAST and ESHOL: hazard ratio (HR) 0.95 (95% CI 0.75-1.20) and HR 0.76 (95% CI 0.59-0.97), respectively; difference between 5 and 24% risk reductions. Similar differences between the two trials were observed for the other unadjusted analytical methods (PP cens, PP excl , TA) The HRs of HDF versus HD treatment became more similar after adding transplantation as a time-varying covariate and including transplantation-outcome confounders: HR 0.89 (95% CI 0.69-1.13) in CONTRAST and HR 0.80 (95% CI 0.62-1.02) in ESHOL. Conclusions: The apparent differences in estimated treatment effects between two dialysis trials were to a large extent attributable to differences in applied methodology for taking renal transplantation into account in their final analyses. Our results exemplify the necessity of careful consideration of the treatment effect of interest when estimating the therapeutic effect in RCTs in which participants may receive additional treatments

Haemodiafiltration versus haemodialysis for kidney failure : an individual patient data meta-analysis of randomised controlled trials

Background: High-dose haemodiafiltration has been shown, in a randomised clinical trial, to result in a 23% lower risk of mortality for patients with kidney failure when compared with conventional high-flux haemodialysis. Nevertheless, whether treatment effects differ across subgroups, whether a dose–response relationship with convection volume exists, and the effects on cause-specific mortality remain unclear. The aim of this individual patient data meta-analysis was to compare the effects of haemodiafiltration and standard haemodialysis on all-cause and cause-specific mortality. Methods: On July 17, 2024, we searched MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials for randomised controlled trials, published from database inception, comparing online haemodiafiltration versus haemodialysis designed to measure mortality outcomes. The primary outcome was all-cause mortality. Hazard ratios were generated using Cox proportional hazards regression models reporting hazard ratios and 95% CIs. Subgroup analyses based on predefined patient characteristics and dose–response analyses using natural splines for convection volume were performed. This analysis is registered with PROSPERO (CRD42024511514). Findings: Five trials (n=4153 patients; 2070 receiving haemodialysis and 2083 receiving haemodiafiltration) were eligible for inclusion in this analysis. After a median follow-up of 30 months (IQR 24–36), all-cause mortality occurred in 477 patients (23·3%) treated with haemodiafiltration compared with in 559 patients (27·0%) treated with haemodialysis (hazard ratio 0·84 [95% CI 0·74–0·95]). No evidence of a differential effect across subgroups was noted. A graded relationship between convection volume and mortality risk was apparent: as the volume increased, the mortality risk decreased. Interpretation: Compared with haemodialysis, online haemodiafiltration reduces all-cause mortality in people with kidney failure. Results do not differ across patient and treatment characteristics and the risk reduction appears to be dose-dependent. In conclusion, the present analysis strengthens the notion that haemodiafiltration can be considered as a superior alternative to the present standard (ie, haemodialysis). Funding: European Commission Research and Innovation, Horizon 2020

The importance of considering competing treatment affecting prognosis in the evaluation of therapy in trials: the example of renal transplantation in hemodialysis trials.

Background: During the follow-up in a randomized controlled trial (RCT), participants may receive additional (non-randomly allocated) treatment that affects the outcome. Typically such additional treatment is not taken into account in evaluation of the results. Two pivotal trials of the effects of hemodiafiltration (HDF) versus hemodialysis (HD) on mortality in patients with end-stage renal disease reported differing results. We set out to evaluate to what extent methods to take other treatments (i.e. renal transplantation) into account may explain the difference in findings between RCTs. This is illustrated using a clinical example of two RCTs estimating the effect of HDF versus HD on mortality. Methods: Using individual patient data from the Estudio de Supervivencia de Hemodiafiltración On-Line (ESHOL; n  =  902) and The Dutch CONvective TRAnsport STudy (CONTRAST; n  = 714) trials, five methods for estimating the effect of HDF versus HD on all-cause mortality were compared: intention-to-treat (ITT) analysis (i.e. not taking renal transplantation into account), per protocol exclusion (PP excl ; exclusion of patients who receive transplantation), PP cens (censoring patients at the time of transplantation), transplantation-adjusted (TA) analysis and an extension of the TA analysis (TA ext ) with additional adjustment for variables related to both the risk of receiving a transplant and the risk of an outcome (transplantation-outcome confounders). Cox proportional hazards models were applied. Results: Unadjusted ITT analysis of all-cause mortality led to differing results between CONTRAST and ESHOL: hazard ratio (HR) 0.95 (95% CI 0.75-1.20) and HR 0.76 (95% CI 0.59-0.97), respectively; difference between 5 and 24% risk reductions. Similar differences between the two trials were observed for the other unadjusted analytical methods (PP cens, PP excl , TA) The HRs of HDF versus HD treatment became more similar after adding transplantation as a time-varying covariate and including transplantation-outcome confounders: HR 0.89 (95% CI 0.69-1.13) in CONTRAST and HR 0.80 (95% CI 0.62-1.02) in ESHOL. Conclusions: The apparent differences in estimated treatment effects between two dialysis trials were to a large extent attributable to differences in applied methodology for taking renal transplantation into account in their final analyses. Our results exemplify the necessity of careful consideration of the treatment effect of interest when estimating the therapeutic effect in RCTs in which participants may receive additional treatments