Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.
Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.
复制标题
DOI：
10.1016/j.genhosppsych.2021.10.005
复制
发表时间：
2022-01
影响因子：
7
通讯作者：
McCoy TH Jr
中科院分区：
文献类型：
--
作者：
Castro VM;Hart KL;Sacks CA;Murphy SN;Perlis RH;McCoy TH Jr
验证先前发表的2019冠状病毒病（COVID-19）住院患者谵妄风险的机器学习模型。使用来自两个学术医疗网络的六家医院的数据，涵盖初始模型开发后发生的护理，我们使用先前开发的风险模型计算了谵妄的预测风险，该模型适用于入院时电子健康记录（EHR）中的诊断，药物，实验室和其他临床特征。我们评估了这些预测的准确性对随后的谵妄诊断在入院期间。在该队列的5102名患者中，716名（14%）出现谵妄。该模型的风险预测产生的c指数为0.75（95%CI，0.73-0.77），27.7%的病例发生在预测风险评分的前十分位。与最初的COVID-19浪潮相比，模型校准减少了。EHR谵妄风险预测模型是在COVID-19患者最初激增期间开发的，对随后的较大波动产生了一致的区分;然而，随着队列组成和谵妄发生率的变化，模型校准下降。这些结果强调了校准的重要性，以及为标准治疗和临床人群可能发生变化的临床环境开发风险模型的挑战。
To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19). Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission. Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73–0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave. This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.
登录
查看更多内容
影响因子：
13.8
作者：
Castro VM;McCoy TH;Perlis RH
通讯作者：
Perlis RH
DOI：
10.1164/rccm.202002-0320oc
发表时间：
2021-08-15
影响因子：
24.7
作者：
Fiest KM;Soo A;Hee Lee C;Niven DJ;Ely EW;Doig CJ;Stelfox HT
通讯作者：
Stelfox HT
影响因子：
3.4
作者：
Breitbart, W;Gibson, C;Tremblay, A
通讯作者：
Tremblay, A
影响因子：
8.8
作者：
Dennis JM;McGovern AP;Vollmer SJ;Mateen BA
通讯作者：
Mateen BA
DOI：
10.1093/jamia/ocz127
发表时间：
2019-12-01
影响因子：
6.4
作者：
Davis, Sharon E.;Greevy, Robert A., Jr.;Matheny, Michael E.
通讯作者：
Matheny, Michael E.