613Monitoring chronic kidney disease evolution using frailty models

Abstract

Abstract Background Frailty models improves traditional survival models such that a latent multiplicative effect is introduced on the risk function, representing non-observed characteristics such as genetic and behavioral. The aim of this study is to explore this kind of model on chronic kidney disease (CKD) monitoring for intermediary outcomes. Methods Using a retrospective cohort comprising 778 patients with diagnosed CKD, parametric survival models were adjusted for the months until decay of renal function ≥5mL as outcome. Models included diabetes, hypertension and CKDEPI as covariates. Latent effect were incorporated, with Gamma distribution, to the best model. Results Just diabetes presented relevant effect on outcome. Best model were Weibull. Without frailty component, estimated diabetes parameter was 0.70 (CI95% 0.54; 0.89), indicating diabetic patients present outcome 30% earlier than non-diabetic. When incorporating the Gamma fragility to the models, the effect was 0.75, (CI95% 0.61, 0.92), or 25% faster on diabetes. The 5 percent points difference between parameters on both models represent, on average, a 20-day difference, having the survival median time of 13 months as reference. It’s possible to address individual specific frailty, an important feature for clinical follow-up. Conclusion Including frailty on modeling made possible to know that in average diabetic patients would experience a fast decay in renal function earlier than what a traditional survival model could evidence. This may be crucial for clinical decision making. All models were adjusted using commercial software. Key message Modern clinical epidemiology must foster use of modern statistics