  Globally, the incidence of heart failure is increasing, and its principal
treatment involves drug therapy. However, widespread non-adherence to therapies
is prevalent among heart failure patients and often results in worsening health
conditions and an increase in hospital admissions. This study aims to develop
an innovative approach, the State-Sequence analysis, to profile heart failure
patients based on different drug-utilization patterns. These patterns aim to
capture both the multidimensional and dynamic effects of therapies.
Subsequently, the study explores how combining clustering algorithms with this
technique influences overall patient survival. Findings highlight the
importance of continued drug therapy after the first hospitalization in
improving heart failure prognosis, irrespective of its severity. The proposed
approach can assist healthcare specialists in evaluating the pathways provided
to patients, allowing for a change in analysis from a transversal and
syntactical approach to a holistic one that leverages statistical tools that
are slightly more complex than traditional methods. Moreover, because of the
many options available for defining states, temporal granularity, and spacing
metrics, SSA is a flexible method applicable to various epidemiological
scenarios.
