Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China; Shandong University Center for Suicide Prevention Research, China.
Shandong University Center for Suicide Prevention Research, China; Department of Sociology, State University of New York College at Buffalo, Buffalo, NY, 14222, USA.
J Psychiatr Res. 2021 Dec;144:217-224. doi: 10.1016/j.jpsychires.2021.10.023. Epub 2021 Oct 19.
Research on predictors and risk of recurrence after suicide attempt from China is lacking. This study aims to identify risk factors and develop prediction models for recurrent suicidal behavior among suicide attempters using Cox proportional hazard (CPH) and machine learning methods.
The prospective cohort study included 1103 suicide attempters with a maximum follow-up of 10 years from rural China. Baseline characteristics, collected by face-to-face interviews at least 1 month later after index suicide attempt, were used to predict recurrent suicidal behavior. CPH and 3 machine learning algorithms, namely, the least absolute shrinkage and selection operator, random survival forest, and gradient boosting decision tree, were used to construct prediction models. Model performance was accessed by concordance index (C-index) and the time-dependent area under the receiver operating characteristic curve (AUC) value for discrimination, and time-dependent calibration curve along with Brier score for calibration.
The median follow-up time was 7.79 years, and 49 suicide attempters had recurrent suicidal behavior during the study period. Four models achieved comparably good discrimination and calibration performance, with all C-indexes larger than 0.70, AUC values larger than 0.65, and Brier scores smaller than 0.06. Mental disorder emerged as the most important predictor across all four models. Suicide attempters with mental disorders had a 3 times higher risk of recurrence than those without. History of suicide attempt (HR = 2.84, 95% CI: 1.34-6.02), unstable marital status (HR = 2.81, 95% CI: 1.38-5.71), and older age (HR = 1.51, 95% CI: 1.14-2.01) were also identified as independent predictors of recurrent suicidal behavior by CPH model.
We developed four models to predict recurrent suicidal behavior with comparable good prediction performance. Our findings potentially provided benefits in screening vulnerable individuals on a more precise scale.
中国缺乏关于自杀未遂后预测因素和复发风险的研究。本研究旨在使用 Cox 比例风险（CPH）和机器学习方法，识别自杀未遂者复发自杀行为的风险因素，并建立预测模型。
这项前瞻性队列研究纳入了来自中国农村的 1103 名自杀未遂者，最长随访时间为 10 年。在自杀未遂后至少 1 个月进行面对面访谈，收集基线特征，用于预测复发自杀行为。使用 CPH 和 3 种机器学习算法，即最小绝对收缩和选择算子、随机生存森林和梯度提升决策树，构建预测模型。通过一致性指数（C 指数）和区分度的时间依赖性接收者操作特征曲线下面积（AUC）值、校准曲线和 Brier 评分评估模型性能。
中位随访时间为 7.79 年，研究期间有 49 名自杀未遂者出现复发自杀行为。4 个模型的区分度和校准性能相当，所有 C 指数均大于 0.70，AUC 值均大于 0.65，Brier 评分均小于 0.06。精神障碍是所有 4 个模型中最重要的预测因素。有精神障碍的自杀未遂者复发风险是无精神障碍者的 3 倍。自杀未遂史（HR=2.84，95%CI：1.34-6.02）、不稳定的婚姻状况（HR=2.81，95%CI：1.38-5.71）和年龄较大（HR=1.51，95%CI：1.14-2.01）也是 CPH 模型识别的复发自杀行为的独立预测因素。
我们开发了 4 种模型来预测复发自杀行为，具有相当好的预测性能。我们的研究结果可能有助于更精确地筛选易受影响的个体。
