Patient-oriented unsupervised learning to uncover the patterns of multimorbidity associated with stroke using primary care electronic health records.

Abstract

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.