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.

翻译：全球范围内，心力衰竭的发生率持续上升，其主要治疗手段为药物治疗。然而，心衰患者普遍存在药物依从性差的问题，常导致健康状况恶化及住院率增加。本研究旨在开发一种创新方法——状态序列分析，基于不同的药物使用模式对心衰患者进行分型。这些模式旨在捕捉治疗的多维度及动态效应。随后，研究探讨了将聚类算法与该技术相结合如何影响患者的总体生存率。研究结果强调，无论疾病严重程度如何，首次住院后持续药物治疗对于改善心衰预后至关重要。所提出的方法能够帮助医疗专家评估患者接受的治疗路径，从而实现从横向句法分析向整体性分析的转变，这种分析利用了比传统方法稍复杂的统计工具。此外，由于在定义状态、时间粒度和间隔度量方面存在多种选择，SSA是一种适用于多种流行病学场景的灵活方法。