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

被引:0
|
作者
Josephson, Colin B. [1 ,2 ,3 ,4 ,5 ,12 ]
Gonzalez-Izquierdo, Arturo [6 ,7 ]
Engbers, Jordan D. T. [8 ]
Denaxas, Spiros [6 ,7 ,9 ]
Delgado-Garcia, Guillermo [1 ]
Sajobi, Tolulope T. [1 ,2 ,3 ,4 ]
Wang, Meng [2 ]
Keezer, Mark R. [10 ]
Wiebe, Samuel [1 ,2 ,3 ,4 ,11 ]
机构
[1] Univ Calgary, Cumming Sch Med, Dept Clin Neurosci, Calgary, AB, Canada
[2] Univ Calgary, Cumming Sch Med, Dept Community Hlth Sci, Calgary, AB, Canada
[3] Univ Calgary, Hotchkiss Brain Inst, Calgary, AB, Canada
[4] Univ Calgary, OBrien Inst Publ Hlth, Calgary, AB, Canada
[5] Univ Calgary, Ctr Hlth Informat, Calgary, AB, Canada
[6] UCL Inst Hlth Informat, London, England
[7] Hlth Data Res HDR UK, London, England
[8] Desid Labs Inc, Calgary, AB, Canada
[9] Alan Turing Inst, London, England
[10] Univ Montreal, Dept Neurosci, Montreal, PQ, Canada
[11] Univ Calgary, Cumming Sch Med, Clin Res Unit, Calgary, AB, Canada
[12] Univ Calgary, Foothills Med Ctr, Cumming Sch Med, Neurol, 1403-29St NW, Calgary, AB, Canada
来源
SEIZURE-EUROPEAN JOURNAL OF EPILEPSY | 2023年 / 111卷
关键词
Epilepsy; Cohort study; Electronic health records; Unsupervised machine learning; Elderly; Late-onset epilepsy; ELECTRONIC HEALTH RECORDS; PEOPLE; RISK; DEPRESSION; DISORDERS; SEIZURES; OUTCOMES; INDEX; CARE;
D O I
10.1016/j.seizure.2023.07.016
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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 multimorbidsocioeconomic 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.
引用
收藏
页码:58 / 67
页数:10
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