  Evaluating hospitals' performance and its relation to patients'
characteristics is of utmost importance to ensure timely, effective, and
optimal treatment. Such a matter is particularly relevant in areas and
situations where the healthcare system must contend with an unexpected surge in
hospitalizations, such as for 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 (ML-CWMd) for
predicting 45-day mortality following hospitalization due to COVID-19. The
methodology flexibly accommodates dependence patterns among continuous,
categorical, and dichotomous variables; effectively accounting for
hospital-specific effects in distinct patient subgroups showing different
attributes. A tailored 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, aiming to profile
heart failure patients hospitalized for COVID-19 and investigate 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 policy-makers in the identification of patient-specific treatment
pathways.
