Effect of Clinical History on Predictive Model Performance for Renal Complications of Diabetes

TL;DR

This study develops logistic regression models to predict renal complications in diabetic patients, demonstrating that incorporating patient history improves prediction accuracy and identifying key predictive features.

Contribution

It introduces a comprehensive analysis of how historical patient data enhances predictive models for diabetic renal complications.

Findings

Models achieve AUROC up to 0.98

Inclusion of past visit data improves performance by up to 4%

Feature importance analysis highlights key predictive variables

Abstract

Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy, which, in turn, is the leading cause of end-stage chronic kidney disease. The early identification of individuals at heightened risk of such complications or their exacerbation can be of paramount importance to set a correct course of treatment. In the present work, from the data collected in the DARWIN-Renal (DApagliflozin Real-World evIdeNce-Renal) study, a nationwide multicentre retrospective real-world study, we develop an array of logistic regression models to predict, over different prediction horizons, the crossing of clinically relevant glomerular filtration rate (eGFR) thresholds for patients with diabetes by means of variables associated with demographic, anthropometric, laboratory, pathology, and therapeutic data. In doing so, we investigate the impact of information coming from patient's past visits on the model's predictive performance, coupled with an analysis of feature importance through the Boruta algorithm. Our models yield very good performance (AUROC as high as 0.98). We also show that the introduction of information from patient's past visits leads to improved model performance of up to 4%. The usefulness of past information is further corroborated by a feature importance analysis.