Changing predictor measurement procedures affected the performance of prediction models in clinical examples

Objectives: The aim of this study was to quantify the impact of predictor measurement heterogeneity on prediction model performance. Predictor measurement heterogeneity refers to variation in the measurement of predictor(s) between the derivation of a prediction model and its validation or application. It arises, for instance, when predictors are measured using different measurement instruments or protocols. Study Design and Setting: We examined the effects of various scenarios of predictor measurement heterogeneity in r

Prediction meets causal inference: the role of treatment in clinical prediction models

In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a ‘predictimand’ framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference

Serum magnesium and sudden death in European hemodialysis patients

Despite suggestions that higher serum magnesium (Mg) levels are associated with improved outcome, the association with mortality in European hemodialysis (HD) patients has only scarcely been investigated. Furthermore, data on the association between serum Mg and sudden death in this patient group is limited. Therefore, we evaluated Mg in a posthoc analysis using pooled data from the CONvective TRAnsport STudy (CONTRAST, NCT00205556), a randomized controlled trial (RCT) evaluating the survival risk in dialysis patients on hemodiafiltration (HDF) compared to HD with a mean follow-up of 3.1 years. Serum Mg was measured at baseline and 6, 12, 24 and 36 months thereafter. Cox proportional hazards models, adjusted for confounders using inverse probability weighting, were used to estimate hazard ratios (HRs) of baseline serum Mg on all-cause mortality, cardiovascular mortality, non-cardiovascular mortality and sudden death. A generalized linear mixed model was used to investigate Mg levels over time. Out of 714 randomized patients, a representative subset of 365 (51%) were analyzed in the present study. For every increase in baseline serum Mg of 0.1 mmol/L, the HR for all-cause mortality was 0.85 (95% CI 0.77-94), the HR for cardiovascular mortality 0.73 (95% CI 0.62-0.85) and for sudden death 0.76 (95% CI 0.62-0.93). These findings did not alter after extensive correction for potential confounders, including treatment modality. Importantly, no interaction was found between serum phosphate and serum Mg. Baseline serum Mg was not related to non-cardiovascular mortality. Mg decreased slightly but statistically significant over time (Ä -0.011 mmol/L/year, 95% CI -0.017 to -0.009, p = 0.03). In short, serum Mg has a strong, independent association with all-cause mortality, cardiovascular mortality and sudden death in European HD patients. Serum Mg levels decrease slightly over time