Psychosocial profiles and their predictors in epilepsy using patient‐reported outcomes and machine learning. (20th May 2020)

• Record Type: Journal Article
• Authors: Josephson, Colin B.; Engbers, Jordan D. T.; Wang, Meng; Perera, Kevin; Roach, Pamela; Sajobi, Tolulope T.; Wiebe, Samuel
• Other Names: Federico Paolo investigator.; Klein Karl Martin investigator.; Murphy William investigator.; Pillay Neelan investigator.; Salmon Andrea investigator.; Singh Shaily investigator.
• Is Part Of: Epilepsia. Volume 61:issue 6(2020)
• Page Start: 1201
• Page End: 1210
• Publication Date: 2020-05-20
• Subjects: cohort studies -- epilepsy/seizures -- machine learning -- patient‐reported outcome measures -- quality of life; Epilepsy -- Periodicals; 616.853
• DOI: 10.1111/epi.16526
• Languages: English
• ISSNs: 0013-9580

Abstract

Abstract: Objective: To apply unsupervised machine learning to patient‐reported outcomes to identify clusters of epilepsy patients exhibiting unique psychosocial characteristics. Methods: Consecutive outpatients seen at the Calgary Comprehensive Epilepsy Program outpatient clinics with complete patient‐reported outcome measures on quality of life, health state valuation, depression, and epilepsy severity and disability were studied. Data were acquired at each patient's first clinic visit. We used k‐means++ to segregate the population into three unique clusters. We then used multinomial regression to determine factors that were statistically associated with patient assignment to each cluster. Results: We identified 462 consecutive patients with complete patient‐reported outcome measure (PROM) data. Post hoc analysis of each cluster revealed one reporting elevated measures of psychosocial health on all five PROMs ("high psychosocial health" cluster), one with intermediate measures ("intermediate" cluster), and one with poor overall measures of psychosocial health ("poor psychosocial health" cluster). Failing to achieve at least 1 year of seizure freedom (relative risk [RR] = 4.34, 95% confidence interval [CI] = 2.13‐9.09) predicted placement in the "intermediate" cluster relative to the "high" cluster. In addition, failing to achieve seizure freedom, social determinants of health, including the need for partially or completely subsidized income support (RR = 6.10, 95% CI = 2.79‐13.31, P < .001) and inability to drive (RR = 4.03, 95% CI = 1.6‐10.00, P = .003), and a history of a psychiatric disorder (RR = 3.16, 95% CI = 1.46‐6.85, P = .003) were associated with the "poor" cluster relative to the "high" cluster. Significance: Seizure‐related factors appear to drive placement in the "intermediate" cluster, with social determinants driving placement in the "poor" cluster, suggesting a threshold effect. Precision intervention based on cluster assignment, with an initial emphasis on improving social support and careful titration of medications for those reporting the worst psychosocial health, could help optimize health for patients with epilepsy.