Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019.
Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019.
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DOI：
10.1016/j.jaclp.2020.12.005
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发表时间：
2021-05
影响因子：
2.3
通讯作者：
McCoy TH
中科院分区：
文献类型：
--
作者：
Castro VM;Sacks CA;Perlis RH;McCoy TH
2019年冠状病毒病大流行给卫生系统带来了前所未有的压力，并与谵妄风险增加有关。大流行病资源有限和临床需求与谵妄相关的融合要求对有针对性的预防工作进行仔细的风险分层。在2019年冠状病毒病患者中建立偶发性谵妄预测模型。我们将监督机器学习应用于三家医院2019年冠状病毒病住院患者的电子健康记录数据，以建立事件性谵妄诊断预测模型。我们在三个不同的医院验证了这个模型。两个医院队列包括学术和社区环境。在6家医院的2907例患者中，488例（16.8%）发生谵妄。在755例患者的外部验证队列中应用预测模型，c指数为0.75（0.71-0.79），前五分位数的提升为2.1。在敏感性为80%时，特异性为56%，阴性预测值为92%，阳性预测值为30%。在按年龄、性别、种族、重症监护需求和社区与学术医院护理分层的子样本中观察到等效模型性能。应用于住院时可用的电子健康记录的机器学习可用于对2019年冠状病毒病患者进行风险分层，以确定其是否发生谵妄。谵妄在2019年冠状病毒病患者中很常见，大流行期间的资源限制要求密切关注预测模型的最佳应用。
The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts. To develop an incident delirium predictive model among coronavirus disease 2019 patients. We applied supervised machine learning to electronic health record data for inpatients with coronavirus disease 2019 at three hospitals to build an incident delirium diagnosis prediction model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings. Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71–0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals. Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.
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影响因子：
2.8
作者：
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通讯作者：
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DOI：
10.1111/j.1467-985x.2009.00592.x
发表时间：
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影响因子：
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作者：
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通讯作者：
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作者：
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