IF 2 Q2 MEDICINE, GENERAL & INTERNAL
Marc Delord, Xiaohui Sun, Annastazia Learoyd, Vasa Curcin, Charles Wolfe, Mark Ashworth, Abdel Douiri
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引用次数: 0

摘要

背景:我们的目的是识别和描述与中风相关的多病模式:我们旨在识别和描述与中风相关的多病模式:我们采用了一种无监督的以患者为导向的聚类方法,分析了 2005 年至 2021 年间在伦敦南部 41 家全科诊所登记的 18 岁以上中风患者的初级保健电子健康记录(EHR)中 30 种常见的长期病症(LTC):在 849,968 名登记患者中,9,847 人(1.16%)有中风记录,46.5% 为女性。有中风记录时的中位年龄为 65.0 岁(IQR:51.5-77.0),除中风外的 LTC 中位数为 3(IQR:2-5)。我们发现了 8 个多病群组,其社会人口特征(年龄、性别和种族)和风险因素各不相同。除了与常规中风风险因素相关的 3 个核心群组外,次要群组显示出较少见的 LTCs 组合,包括精神健康状况、哮喘、骨关节炎和镰状细胞性贫血。重要的是,出现了精神健康状况、传染病和药物依赖的复杂组合:这种以患者为导向的多病症纵向视角弥补了目前在绘制中风相关多病症模式图方面的不足,不仅包括 LTCs,还包括社会人口学特征,并为更高效、更以患者为导向的医疗保健模式提供了可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Patient-oriented unsupervised learning to uncover the patterns of multimorbidity associated with stroke using primary care electronic health records.

Background: We aimed to identify and characterise the longitudinal patterns of multimorbidity associated with stroke.

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 and 46.5% were female. The median age at record of stroke was 65.0 year (IQR: 51.5-77.0) and the median number of LTCs in addition to stroke was 3 (IQR: 2-5). We identified eight clusters of multimorbidity with contrasted 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 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 novel longitudinal and patient-oriented perspective on multimorbidity addresses existing gaps in mapping the patterns of stroke-associated multimorbidity not only in terms of LTCs, but also socio-demographic characteristics, and suggests potential for more efficient and patient-oriented healthcare models.

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