Mortality reduction by post-dilution online-haemodiafiltration: a cause-specific analysis

International audienceBackground:From an individual participant data (IPD) meta-analysis from four randomized controlled trials comparing haemodialysis (HD) with post-dilution online-haemodiafiltration (ol-HDF), previously it appeared that HDF decreases all-cause mortality by 14% (95% confidence interval 25; 1) and fatal cardiovascular disease (CVD) by 23% (39; 3). Significant differences were not found for fatal infections and sudden death. So far, it is unclear, however, whether the reduced mortality risk of HDF is only due to a decrease in CVD events and if so, which CVD in particular is prevented, if compared with HD.Methods:The IPD base was used for the present study. Hazard ratios and 95% confidence intervals for cause-specific mortality overall and in thirds of the convection volume were calculated using the Cox proportional hazard regression models. Annualized mortality and numbers needed to treat (NNT) were calculated as well.Results:Besides 554 patients dying from CVD, fatal infections and sudden death, 215 participants died from 'other causes', such as withdrawal from treatment and malignancies. In this group, the mortality risk was comparable between HD and ol-HDF patients, both overall and in thirds of the convection volume. Subdivision of CVD mortality in fatal cardiac, non-cardiac and unclassified CVD showed that ol-HDF was only associated with a lower risk of cardiac casualties [0.64 (0.61; 0.90)]. Annual mortality rates also suggest that the reduction in CVD death is mainly due to a decrease in cardiac fatalities, including both ischaemic heart disease and congestion. Overall, 32 and 75 patients, respectively, need to be treated by high-volume HDF (HV-HDF) to prevent one all-cause and one CVD death, respectively, per year.Conclusion:The beneficial effect of ol-HDF on all-cause and CVD mortality appears to be mainly due to a reduction in fatal cardiac events, including ischaemic heart disease as well as congestion. In HV-HDF, the NNT to prevent one CVD death is 75 per year

Mortality reduction by post-dilution online-haemodiafiltration: a cause-specific analysis

Background: From an individual participant data (IPD) meta-analysis from four RCTs, comparing HD with online-HDF (ol-HDF), previously it appeared that HDF decreases all cause mortality by 14% (25;1) and fatal cardiovascular disease (CVD) by 23% (39; 3). Significant differences were not found for fatal infections and sudden death. So far, it is unclear, however, whether the reduced mortality risk of HDF is only due to a decrease in CVD events and if so, which CVD in particular is prevented, if compared to HD. Methods: The IPD-base was used for the present study. HRs and 95% CIs for cause specific mortality overall and in tertiles of the convection volume were calculated using the Cox Proportional hazard regression models. Annualized mortality and numbers needed to treat (NNT) were calculated. Results: Besides 554 patients dying from CVD, fatal infections and sudden death, 215 participants died from ‘other causes’, such as withdrawal from treatment and malignancies. In this group, the mortality risk was comparable between HD and ol-HDF patients, both overall and in tertiles of the convection volume. Subdivision of CVD mortality in fatal cardiac, non-cardiac and unclassified CVD, showed that ol-HDF was only associated with a lower risk of cardiac casualties [0.64(0.61;0.90)]. Annual mortality rates also suggest that the reduction in CVD death is mainly due to a decrease in cardiac fatalities, including both ischemic heart disease and congestion. Overall, 32, respectively 75 patients need to be treated by high volume HDF (HV-HDF) to prevent one all cause, respectively CVD death/year. Interpretation: The beneficial effect of ol-HDF on all cause and CVD mortality appears mainly due to a reduction in fatal cardiac events, including ischemic heart disease as well as congestion. In HV-HDF the NNT to prevent one CVD death is 75/year

Long-Term Peridialytic Blood Pressure Patterns in Patients Treated by Hemodialysis and Hemodiafiltration

International audienceOnline postdilution hemodiafiltration (HDF) is associated with a lower all-cause and cardiovascular mortality than hemodialysis (HD). This may depend on a superior peridialytic (pre- and postdialysis, and the difference between these 2 parameters) hemodynamic profile.Introduction: Online postdilution hemodiafiltration (HDF) is associated with a lower all-cause and cardiovascular mortality than hemodialysis (HD). This may depend on a superior peridialytic (pre- and postdialysis, and the difference between these 2 parameters) hemodynamic profile.Methods: In this retrospective cohort analysis of individual participant data (IPD) from 3 randomized controlled trials (RCTs) (n = 2011), the effect of HDF and HD on 2-year peridialytic blood pressure (BP) patterns was assessed. Long-term peridialytic systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and pulse pressure (PP), as well as the deltas (post- minus predialytic) were assessed in the total group of patients. Thereafter, these variables were compared between patients on HD and HDF, and in the latter group between quartiles of convection volume.Results: Mean pre- and postdialysis SBP, DBP, and MAP declined significantly during follow-up (predialytic: SBP -2.16 mm Hg, DBP -2.88 mm Hg, MAP -2.64 mm Hg), PP increased (predialytic 0.96 mm Hg). Peridialytic deltas remained unaltered. Differences between the 2 modalities, or between quartiles of convection volume were not observed. BP changes were independent of various baseline characteristics, including the decline in body weight over time.Conclusion: We speculate that the combination of a decreasing SBP and an increasing PP may be the clinical sequelae of a worsening cardiovascular system. Because especially HDF with a high convection volume has been associated with a beneficial effect on survival, our study does not support the view that superior peridialytic BP control contributes to this effect

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

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-totreat (ITT) analysis (i.e. not taking renal transplantation into account), per protocol exclusion (PPexcl; exclusion of patients who receive transplantation), PPcens (censoring patients at the time of transplantation), transplantation-adjusted (TA) analysis and an extension of the TA analysis (TAext) 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 hazardsmodels 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 (PPcens, PPexcl, 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 participantsmay receive additional treatments

Differences in quality of life of hemodialysis patients between dialysis centers

Purpose Hemodialysis patients undergo frequent and long visits to the clinic to receive adequate dialysis treatment, medical guidance, and support. This may affect health-related quality of life (HRQOL). Although HRQOL is a very important management aspect in hemodialysis patients, there is a paucity of information on the differences in HRQOL between centers. We set out to assess the differences in HRQOL of hemodialysis patients between dialysis centers and explore which modifiable center characteristics could explain possible differences. Methods This cross-sectional study evaluated 570 hemodialysis patients from 24 Dutch dialysis centers. HRQOL was measured with the Kidney Disease Quality Of Life-Short Form (KDQOL-SF). Results After adjustment for differences in case-mix, three HRQOL domains differed between dialysis centers: the physical composite score (PCS, P = 0.01), quality of social interaction (P = 0.04), and dialysis staff encouragement (P = 0.001). These center differences had a range of 11-21 points on a scale of 0-100, depending on the domain. Two center characteristics showed a clinical relevant relation with patients' HRQOL: dieticians' fulltime-equivalent and the type of dialysis center. Conclusion This study showed that clinical relevant differences exist between dialysis centers in multiple HRQOL domains. This is especially remarkable as hemodialysis is a highly standardized therapy