Characterizing High Risk Patients in Heart Failure: A Latent Class Analysis of Rehospitalization and Mortality

Patients with heart failure (HF) are typically classified based on left ventricular ejection fraction (LVEF). However, this may not optimally reflect distinct groups or predict risk. Therefore, alternative classification methods are needed.

Our aim was to classify patients with HF based on sociodemographic and clinical data using latent class analysis, assess if latent classes pose varying risks of mortality and rehospitalization, and explore if these classes offer better risk stratification than LVEF or N-terminal prohormone of brain natriuretic peptide (NT-proBNP) alone.

Data from 1045 patients were analyzed using latent class analysis to identify classes and assess mortality and rehospitalization risks. Kaplan-Meier curves with log-rank tests were used to compare mortality across latent classes, LVEF-based groups, and NT-proBNP-based groups.

Three latent classes were identified. Class 1 (48.8%) comprised middle-aged males with HF with reduced ejection fraction (HFrEF) and high NT-proBNP levels. Class 2 (26.1%) consisted of mainly older females with HF with preserved ejection fraction, high body mass index, hypertension, atrial fibrillation, and anemia. Class 3 (25.2%) included younger patients with HFrEF, high body mass index, and a high smoking rate. Class 1 and 2 had higher mortality risks ( P < .001) and longer rehospitalization durations ( P = .011) than class 3.

Latent class analysis categorized a heterogeneous group of patients with HF into homogeneous classes. These classes provide a close approximation of what could be observed in clinical practice and provide insight into patients at higher risk of mortality and rehospitalization.