Predicting Suicidal Behavior From Longitudinal Electronic Health Records
Predicting Suicidal Behavior From Longitudinal Electronic Health Records
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DOI：
10.1176/appi.ajp.2016.16010077
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发表时间：
2017-02-01
影响因子：
17.7
通讯作者：
Reis, Ben Y.
中科院分区：
文献类型：
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作者：
Barak-Corren, Yuval;Castro, Victor M.;Reis, Ben Y.
目的：探讨电子健康档案系统中常见的纵向历史数据是否可用于预测患者未来自杀行为的风险。来自大型卫生保健数据库的EHR数据跨越15年(1998-2012年)住院和门诊就诊，用于预测未来有记录的自杀行为(即自杀未遂或死亡)。研究对象为就诊3次或以上的患者(N=1,728,549例)。基于ICD-9的自杀行为案例定义是通过对2700份叙述性EHR笔记(来自520名患者)的专家临床医生共识审查得出的，并辅之以州死亡证明。结果：在研究人群中，1.2%(N=20246)符合自杀行为的病例定义。该模型实现了对患者未来自杀行为的敏感性(33%-45%)、特异性(90%-295%)和早期(平均提前3-4年)的预测。该模型确定的最强预测因素既包括众所周知的危险因素(例如，药物滥用和精神障碍)，也包括不那么传统的危险因素(例如，某些伤害和慢性病)，这表明数据驱动的方法可以产生更全面的风险概况。结论：临床环境中常见的纵向EHR数据可以用于预测未来自杀行为的风险。这种建模方法可以作为早期预警系统，帮助临床医生识别高危患者进行进一步筛查。通过分析EHR的全部表型广度，计算机化的风险筛查方法可能会增强预测，超出个别临床医生的可行范围。
Objective: The purpose of this article was to determine whether longitudinal historical data, commonly available in electronic health record (EHR) systems, can be used to predict patients' future risk of suicidal behavior.Method: Bayesian models were developed using a retrospective cohort approach. EHR data from a large health care database spanning 15 years (1998-2012) of inpatient and outpatient visits were used to predict future documented suicidal behavior (i.e., suicide attempt or death). Patients with three or more visits (N= 1,728,549) were included. ICD-9-based case definition for suicidal behavior was derived by expert clinician consensus review of 2,700 narrative EHR notes (from 520 patients), supplemented by state death certificates. Model performance was evaluated retrospectively using an independent testing set.Results: Among the study population, 1.2% (N= 20,246) met the case definition for suicidal behavior. The model achieved sensitive (33%-45% sensitivity), specific (90%-295% specificity), and early (3-4 years in advance on average) prediction of patients' future suicidal behavior. The strongest predictors identified by the model included both well-known (e.g., substance abuse and psychiatric disorders) and less conventional (e.g., certain injuries and chronic conditions) risk factors, indicating that a data-driven approach can yield more comprehensive risk profiles.Conclusions: Longitudinal EHR data, commonly available in clinical settings, can be useful for predicting future risk of suicidal behavior. This modeling approach could serve as an early warning system to help clinicians identify high-risk patients for further screening. By analyzing the full pheno-typic breadth of the EHR, computerized risk screening approaches may enhance prediction beyond what is feasible for individual clinicians.