Cluster-weighted modeling of lifetime hierarchical data for profiling COVID-19 heart failure patients

TL;DR

This paper introduces a novel mixture modeling approach for analyzing heterogeneous survival data of COVID-19 heart failure patients, accounting for hierarchical healthcare effects and identifying latent patient profiles.

Contribution

It proposes a new mixture model with random effects and two EM algorithms to uncover patient clusters and hospital effects in survival analysis.

Findings

Identified distinct patient survival profiles.

Quantified hospital-level variability in outcomes.

Highlighted the influence of respiratory conditions on survival.

Abstract

This study investigates the heterogeneity in survival times among COVID-19 patients with Heart Failure (HF) hospitalized in the Lombardy region of Italy during the pandemic. To address this, we propose a novel mixture model for right-censored lifetime data that incorporates random effects and allows for local distributions of the explanatory variables. Our approach identifies latent clusters of patients while estimating component-specific covariate effects on survival, taking into account the hierarchical structure induced by the healthcare facility. Specifically, a shared frailty term, unique to each cluster, captures hospital-level variability enabling a twofold decoupling of survival heterogeneity across both clusters and hierarchies. Two EM-based algorithms, namely a Classification EM (CEM) and a Stochastic EM (SEM), are proposed for parameter estimation. The devised methodology effectively uncovers latent patient profiles, evaluates within-cluster hospital effects, and quantifies the impact of respiratory conditions on survival. Our findings provide new information on the complex interplay between the impacts of HF, COVID-19, and healthcare facilities on public health, highlighting the importance of personalized and context-sensitive clinical strategies.