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Published on: May 15, 2020
Prediction of self-harm in people with newly-diagnosed depression: development and validation of risk prediction
Heidi Ka Ying Lo1, Ivan Wai Lok Chu1, Joe Kwun Nam Chan1
1Department of Psychiatry, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong.
Abstract:
Depression is associated with increased self-harm risk, particularly in the early illness course, yet individualised predictions models remain underexplored. We aimed to develop and externally validate a prediction model for self-harm risk in people with newly-diagnosed-depression. Utilizing a territory-wide electronic health-record (EHR) database spanning Hong-Kong public healthcare services (including all public hospitals, specialists, and general outpatient clinics), we identified individuals aged ≥12 years with first-diagnosed depression between 1-January-2002 and 31-December-2021. The primary outcome was non-fatal self-harm and/or completed suicide. We developed 1-year and 3-year self-harm risk prediction models using the least absolute shrinkage and selection operator (LASSO) method and backward regression model. This population-based cohort comprised 102,863 individuals with newly-diagnosed-depression (mean age 48.22 [SD 17.78] years; 71.5% female), 2678 self-harm incidents occurred over 98,807.5 person-years (rate: 27.09 [95%CI 26.1-28.1] per 1000 person-years). Key predictors included history of self-poisoning/self-inflicted injury, past psychiatric hospitalisation, comorbid somatoform and conversion disorders, and substance use disorders, while use of lithium and antidepressants represented protective factors. In external validation cohort (n = 14,843), our model achieved good discrimination (C-statistics = 0.83 [95%CI 0.80-0.85], D = 2.35 [2.17-2.53]), near-perfect calibration (calibration slope =1.00 [0.94-1.06], O/E ratio = 1.00 [0.90-1.10]), and high accuracy (brier score = 0.02 [0.02-0.02]). Performance remained robust in age, sex-stratified subgroups and 1-year vs. 3-year self-harm prediction windows. This validated model leverages EHR data to accurately identified individuals at elevated self-harm risk post-depression diagnosis, may tailor individual-level risk estimates and facilitate timely interventions, thereby potentially averting risk escalation, in the critical window of heightened self-harm risk.
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