Effects of Zibotentan and Dapagliflozin on Fluid Retention in Patients with CKD

We read with great interest the recent article by Heerspink et al. titled “Effects of Zibotentan Alone and in Combination with Dapagliflozin on Fluid Retention in Patients with CKD” published in JASN.1 The study found that high doses of zibotentan were associated with a higher risk of fluid retention, which was mitigated by using lower doses and adding dapagliflozin. However, we note several biases in the use of the Cox proportional hazards model that the authors did not address. The established criteria may result in mixed censoring outcomes, that is, right-censoring and interval-censoring events.2,3 Fluid retention diagnosed through medical records could result in interval censoring if they occurred between follow-up visits and right censoring if diagnosed between the end of follow-up and the time of data analysis. The Cox proportional hazards model primarily handles right-censored data. By contrast, the accelerated failure time (AFT) model is often preferred for scenarios involving various types of censored data.4 The AFT model can effectively handle left-censored, right-censored, and interval-censored data by appropriately adjusting the likelihood function.5 By using the “survival” and “icenReg” packages, mixed censored data can be fitted and analyzed and event times can be estimated.6 Moreover, the Cox proportional hazards model requires the proportional hazards assumption, meaning that covariate effects are constant over time.7 If this assumption is violated, the model may not provide unbiased estimates of the coefficients, and the predictions may not be reliable. The authors should use Schoenfeld residuals or alternative methods to evaluate the proportional hazards assumption for the association between covariates and the risk of fluid retention. Schoenfeld residuals are calculated as the differences between the observed and expected values of covariates at each failure time.8 If the residuals exhibit a systematic change over time, it suggests that the effect of the covariate may be time dependent. When the proportional hazards assumption does not hold, authors should use a stratified Cox model, a Cox model with time-varying effects, or an AFT model instead of the standard Cox proportional hazards model.4,9 In conclusion, we believe that a re-evaluation considering the potential effect of censoring events and the proportional hazards assumption is necessary. Further research is anticipated to provide more empirical data and clearer insights into this field.