Dynamic prediction of death risk given a renewal hospitalization process

TL;DR

This paper introduces a dynamic prediction framework for estimating death risk in chronic patients based on their hospitalization history, using a joint model that accounts for the timing and frequency of hospitalizations.

Contribution

It develops a joint modeling approach for death and hospitalization processes, specifically applying a renewal process model to improve prediction accuracy.

Findings

More concentrated hospitalizations increase death risk.

The renewal model captures the impact of hospitalization timing on mortality.

Application to COPD patients demonstrates practical utility.

Abstract

Predicting the risk of death for chronic patients is highly valuable for informed medical decision-making. This paper proposes a general framework for dynamic prediction of the risk of death of a patient given her hospitalization history, which is generally available to physicians. Predictions are based on a joint model for the death and hospitalization processes, thereby avoiding the potential bias arising from selection of survivors. The framework accommodates various submodels for the hospitalization process. In particular, we study prediction of the risk of death in a renewal model for hospitalizations, a common approach to recurrent event modelling. In the renewal model, the distribution of hospitalizations throughout the follow-up period impacts the risk of death. This result differs from prediction in the Poisson model, previously studied, where only the number of hospitalizations matters. We apply our methodology to a prospective, observational cohort study of 512 patients treated for COPD in one of six outpatient respiratory clinics run by the Respiratory Service of Galdakao University Hospital, with a median follow-up of 4.7 years. We find that more concentrated hospitalizations increase the risk of death.