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<p>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 <i>vs</i>. 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.</p>

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Prediction of self-harm in people with newly-diagnosed depression: development and validation of risk prediction models

  • 期刊论文

  • 发表日期：

    2026年3月25日

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  • Heidi Ka Ying Lo,
  • Ivan Wai Lok Chu,
  • Joe Kwun Nam Chan,
  • Corine Sau Man Wong,
  • Wing Chung Chang

摘要

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.