Variables included in the analysis.

More than 60% of suicides globally are estimated to take place in low- and middle-income nations. Prior research on suicide has indicated that over 50% of those who die by suicide do so on their first attempt. Nevertheless, there is a dearth of knowledge on the attributes of individuals who die on their first attempt and the factors that can predict mortality on the first attempt in these regions. The objective of this study was to create an individual-level risk-prediction model for mortality on the first suicide attempt. We analyzed records of individuals’ first suicide attempts that occurred between May 1, 2017, and April 30, 2018, from the national suicide surveillance system, which includes all of the provinces of Thailand. Subsequently, a risk-prediction model for mortality on the first suicide attempt was constructed utilizing multivariable logistic regression and presented through a web-based application. The model’s performance was assessed by calculating the area under the receiver operating curve (AUC), as well as measuring its sensitivity, specificity, and accuracy. Out of the 3,324 individuals who made their first suicide attempt, 50.5% of them died as a result of that effort. Nine out of the 21 potential predictors demonstrated the greatest predictive capability. These included male sex, age over 50 years old, unemployment, having a depressive disorder, having a psychotic illness, experiencing interpersonal problems such as being aggressively criticized or desiring plentiful attention, having suicidal intent, and displaying suicidal warning signals. The model demonstrated a good predictive capability, with an AUC of 0.902, a sensitivity of 84.65%, a specificity of 82.66%, and an accuracy of 83.63%. The implementation of this predictive model can assist physicians in conducting comprehensive evaluations of suicide risk in clinical settings and devising treatment plans for preventive intervention.

据估算，全球范围内超过60%的自杀事件发生在中低收入国家（low- and middle-income nations）。既往关于自杀的研究显示，超过50%的自杀身亡者均为首次实施自杀行为即死亡。然而，针对这些地区中首次自杀后死亡的个体特征，以及可预测首次自杀后死亡风险的相关因素，目前仍存在相关认知空白。本研究旨在构建针对首次自杀尝试后死亡风险的个体水平预测模型。我们分析了2017年5月1日至2018年4月30日期间，来自覆盖泰国所有省份的全国自杀监测系统的个体首次自杀尝试相关记录。随后，我们采用多变量逻辑回归（multivariable logistic regression）构建了首次自杀尝试后死亡的风险预测模型，并通过基于网页的应用程序进行呈现。该模型的性能通过计算受试者工作特征曲线下面积（area under the receiver operating curve, AUC），以及灵敏度、特异度和准确率进行评估。在3324名首次尝试自杀的个体中，有50.5%的人因该次自杀行为身亡。21项潜在预测因素中有9项展现出最优的预测效能，包括男性性别、50岁以上年龄、失业状态、伴发抑郁障碍、存在精神病性疾病、遭遇人际问题（如遭受攻击性指责或渴求大量关注）、存在自杀意图以及出现自杀预警信号。本模型展现出良好的预测性能，其AUC值为0.902，灵敏度为84.65%，特异度为82.66%，准确率为83.63%。该预测模型的应用可辅助临床医师在临床场景中全面评估自杀风险，并制定预防性干预的治疗方案。