Jiang Tammy, Nagy Dávid, Rosellini Anthony J, Horváth-Puhó Erzsébet, Keyes Katherine M, Lash Timothy L, Galea Sandro, Sørensen Henrik T, Gradus Jaimie L
Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
J Psychiatr Res. 2021 Oct;142:275-282. doi: 10.1016/j.jpsychires.2021.08.003. Epub 2021 Aug 11.
Accurate identification of persons at risk of suicide is challenging because suicide is a rare outcome with a multifactorial origin. The purpose of this study was to predict suicide among persons with depression using machine learning methods.
A case-cohort study was conducted in Denmark between January 1, 1995 and December 31, 2015. Cases were all persons who died by suicide and had an incident depression diagnosis in Denmark (n = 2,774). The comparison subcohort was a 5% random sample of all individuals in Denmark at baseline, restricted to persons with an incident depression diagnosis during the study period (n = 11,963). Classification trees and random forests were used to predict suicide.
In men with depression, there was a high risk of suicide among those who were prescribed other analgesics and antipyretics (i.e., non-opioid analgesics such as acetaminophen), prescribed hypnotics and sedatives, and diagnosed with a poisoning (n = 96; risk = 81%). In women with depression, there was an elevated risk of suicide among those who were prescribed other analgesics and antipyretics, anxiolytics, and hypnotics and sedatives, but were not diagnosed with poisoning nor cerebrovascular diseases (n = 338; risk = 58%).
Psychiatric disorders and their associated medications were strongly indicative of suicide risk. Notably, anti-inflammatory medications (e.g., acetaminophen) prescriptions, which are used to treat chronic pain and illnesses, were associated with suicide risk in persons with depression. Machine learning may advance our ability to predict suicide deaths.
准确识别有自杀风险的人群具有挑战性，因为自杀是一种罕见的结果，其起源是多因素的。本研究的目的是使用机器学习方法预测抑郁症患者的自杀情况。
1995年1月1日至2015年12月31日在丹麦进行了一项病例队列研究。病例为丹麦所有自杀死亡且有新发抑郁症诊断的人（n = 2774）。对照亚队列是丹麦所有个体在基线时的5%随机样本，仅限于研究期间有新发抑郁症诊断的人（n = 11963）。使用分类树和随机森林来预测自杀。
在患有抑郁症的男性中，服用其他镇痛药和解热药（即对乙酰氨基酚等非阿片类镇痛药）、服用催眠药和镇静剂以及被诊断为中毒的人自杀风险较高（n = 96；风险 = 81%）。在患有抑郁症的女性中，服用其他镇痛药和解热药、抗焦虑药以及催眠药和镇静剂，但未被诊断为中毒或脑血管疾病的人自杀风险升高（n = 338；风险 = 58%）。
精神疾病及其相关药物强烈表明自杀风险。值得注意的是，用于治疗慢性疼痛和疾病（如对乙酰氨基酚）的抗炎药物处方与抑郁症患者的自杀风险相关。机器学习可能会提高我们预测自杀死亡的能力。
