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

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.