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Association of comorbid-socioeconomic clusters with mortality in late onset epilepsy derived through unsupervised machine learning

Josephson, Colin B; Gonzalez-Izquierdo, Arturo; Engbers, Jordan DT; Denaxas, Spiros; Delgado-Garcia, Guillermo; Sajobi, Tolulope T; Wang, Meng; ... Wiebe, Samuel; + view all (2023) Association of comorbid-socioeconomic clusters with mortality in late onset epilepsy derived through unsupervised machine learning. Seizure: European Journal of Epilepsy , 111 pp. 58-67. 10.1016/j.seizure.2023.07.016.

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Abstract

Background and objectives Late-onset epilepsy is a heterogenous entity associated with specific aetiologies and an elevated risk of premature mortality. Specific multimorbid-socioeconomic profiles and their unique prognostic trajectories have not been described. We sought to determine if specific clusters of late onset epilepsy exist, and whether they have unique hazards of premature mortality. Methods We performed a retrospective observational cohort study linking primary and hospital-based UK electronic health records with vital statistics data (covering years 1998–2019) to identify all cases of incident late onset epilepsy (from people aged ≥65) and 1:10 age, sex, and GP practice-matched controls. We applied hierarchical agglomerative clustering using common aetiologies identified at baseline to define multimorbid-socioeconomic profiles, compare hazards of early mortality, and tabulating causes of death stratified by cluster. Results From 1,032,129 people aged ≥65, we identified 1048 cases of late onset epilepsy who were matched to 10,259 controls. Median age at epilepsy diagnosis was 68 (interquartile range: 66–72) and 474 (45%) were female. The hazard of premature mortality related to late-onset epilepsy was higher than matched controls (hazard ratio [HR] 1.73; 95% confidence interval [95%CI] 1.51–1.99). Ten unique phenotypic clusters were identified, defined by ‘healthy’ males and females, ischaemic stroke, intracerebral haemorrhage (ICH), ICH and alcohol misuse, dementia and anxiety, anxiety, depression in males and females, and brain tumours. Cluster-specific hazards were often similar to that derived for late-onset epilepsy as a whole. Clusters that differed significantly from the base late-onset epilepsy hazard were ‘dementia and anxiety’ (HR 5.36; 95%CI 3.31–8.68), ‘brain tumour’ (HR 4.97; 95%CI 2.89–8.56), ‘ICH and alcohol misuse’ (HR 2.91; 95%CI 1.76–4.81), and ‘ischaemic stroke’ (HR 2.83; 95%CI 1.83–4.04). These cluster-specific risks were also elevated compared to those derived for tumours, dementia, ischaemic stroke, and ICH in the whole population. Seizure-related cause of death was uncommon and restricted to the ICH, ICH and alcohol misuse, and healthy female clusters. Significance Late-onset epilepsy is an amalgam of unique phenotypic clusters that can be quantitatively defined. Late-onset epilepsy and cluster-specific comorbid profiles have complex effects on premature mortality above and beyond the base rates attributed to epilepsy and cluster-defining comorbidities alone.

Type: Article
Title: Association of comorbid-socioeconomic clusters with mortality in late onset epilepsy derived through unsupervised machine learning
DOI: 10.1016/j.seizure.2023.07.016
Publisher version: https://doi.org/10.1016/j.seizure.2023.07.016
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Epilepsy, cohort study, electronic health records, unsupervised machine learning, elderly, late-onset epilepsy
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10174538
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