Uncovering mortality patterns and hospital effects in COVID-19 heart failure patients: a novel multilevel logistic cluster-weighted modeling approach.

Abstract

Evaluating hospital performance and its relationship to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. This is particularly relevant in areas and situations where the healthcare system must deal with an unexpected surge in hospitalizations, such as heart failure patients in the Lombardy Region of Italy during the COVID-19 pandemic. Motivated by this issue, the paper introduces a novel multilevel logistic cluster-weighted model for predicting 45-day mortality following hospitalization due to COVID-19. The methodology flexibly accommodates dependence patterns among continuous and dichotomous variables; effectively accounting for group-specific effects in distinct subgroups showing different attributes. A tailored classification expectation-maximization algorithm is developed for parameter estimation, and extensive simulation studies are conducted to evaluate its performance against competing models. The novel approach is applied to administrative data from the Lombardy Region, with the aim of profiling heart failure patients hospitalized for COVID-19 and investigating the hospital-level impact on their overall mortality. A scenario analysis demonstrates the model's efficacy in managing multiple sources of heterogeneity, thereby yielding promising results in aiding healthcare providers and policymakers in the identification of patient-specific treatment pathways.