Background: Identifying and characterising the longitudinal patterns of multimorbidity associated with stroke is needed to better understand patients' needs and inform new models of care. Methods: We used an unsupervised patient-oriented clustering approach to analyse primary care electronic health records (EHR) of 30 common long-term conditions (LTC), in patients with stroke aged over 18, registered in 41 general practices in south London between 2005 and 2021. Results: Of 849,968 registered patients, 9,847 (1.16%) had a record of stroke, 46.5% were female and median age at record was 65.0 year (IQR: 51.5 to 77.0). The median number of LTCs in addition to stroke was 3 (IQR: from 2 to 5). Patients were stratified in eight clusters. These clusters revealed contrasted patterns of multimorbidity, socio-demographic characteristics (age, gender and ethnicity) and risk factors. Beside a core of 3 clusters associated with conventional stroke risk-factors, minor clusters exhibited less common but recurrent combinations of LTCs including mental health conditions, asthma, osteoarthritis and sickle cell anaemia. Importantly, complex profiles combining mental health conditions, infectious diseases and substance dependency emerged. Conclusion: This patient-oriented approach to EHRs uncovers the heterogeneity of profiles of multimorbidity and socio-demographic characteristics associated with stroke. It highlights the importance of conventional stroke risk factors as well as the association of mental health conditions in complex profiles of multimorbidity displayed in a significant proportion of patients. These results address the need for a better understanding of stroke-associated multimorbidity and complexity to inform more efficient and patient-oriented healthcare models.

翻译：背景：识别并描述卒中相关的纵向多病模式，有助于更好地理解患者需求并为新型护理模式提供依据。方法：采用面向患者的无监督聚类方法，分析2005年至2021年间伦敦南部41家全科诊所注册的18岁以上卒中患者的初级保健电子健康记录（EHR）中30种常见长期病症（LTC）的数据。结果：在849,968名注册患者中，9,847人（1.16%）有卒中记录，其中46.5%为女性，记录时中位年龄为65.0岁（四分位距：51.5至77.0）。除卒中外的中位LTC数为3（四分位距：2至5）。患者被划分为八个聚类。这些聚类揭示了多病模式、社会人口学特征（年龄、性别和种族）及风险因素的对比性分布。除了与常规卒中危险因素相关的三个核心聚类外，次要聚类呈现了包括精神健康疾病、哮喘、骨关节炎和镰状细胞性贫血在内的罕见但反复出现的LTC组合。重要的是，出现了结合精神健康疾病、感染性疾病和物质依赖的复杂特征谱。结论：这种面向患者的EHR分析方法揭示了与卒中相关的多病特征及社会人口学特征的异质性。它强调了常规卒中危险因素的重要性，以及在相当比例患者中呈现的复杂多病特征中精神健康疾病的相关性。这些结果满足了对卒中相关多病及复杂性更深入理解的需求，以支持更高效且以患者为中心的医疗模式。