Patient-Oriented Unsupervised Learning to Unlock Patterns of Multimorbidity Associated with Stroke using Primary Care Electronic Health Records

TL;DR

This study uses unsupervised clustering of primary care EHRs to identify diverse multimorbidity patterns in stroke patients, revealing complex profiles including mental health and infectious diseases, to inform personalized care.

Contribution

It introduces a patient-oriented clustering approach to analyze multimorbidity patterns in stroke, uncovering heterogeneity and complex profiles beyond traditional risk factors.

Findings

Eight distinct multimorbidity clusters identified.

Complex profiles include mental health, infectious diseases, and substance dependency.

Highlights importance of personalized, comprehensive care models.

Abstract

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.