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Globally, the incidence of heart failure is increasing, and its principaltreatment involves drug therapy. However, widespread non-adherence to therapiesis prevalent among heart failure patients and often results in worsening healthconditions and an increase in hospital admissions. This study aims to developan innovative approach, the State-Sequence analysis, to profile heart failurepatients based on different drug-utilization patterns. These patterns aim tocapture both the multidimensional and dynamic effects of therapies.Subsequently, the study explores how combining clustering algorithms with thistechnique influences overall patient survival. Findings highlight theimportance of continued drug therapy after the first hospitalization inimproving heart failure prognosis, irrespective of its severity. The proposedapproach can assist healthcare specialists in evaluating the pathways providedto patients, allowing for a change in analysis from a transversal andsyntactical approach to a holistic one that leverages statistical tools thatare slightly more complex than traditional methods. Moreover, because of themany options available for defining states, temporal granularity, and spacingmetrics, SSA is a flexible method applicable to various epidemiologicalscenarios.

Integrating state-sequence analysis to uncover dynamic drug-utilization patterns to profile heart failure patients