Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial.
Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial.
复制标题
DOI：
10.1055/s-0042-1750416
复制
发表时间：
2022-05
影响因子：
2.9
通讯作者：
Aphinyanaphongs, Yindalon
中科院分区：
文献类型：
--
作者：
Major, Vincent J.;Jones, Simon A.;Razavian, Narges;Bagheri, Ashley;Mendoza, Felicia;Stadelman, Jay;Horwitz, Leora, I;Austrian, Jonathan;Aphinyanaphongs, Yindalon
关键词：
背景 我们之前开发并验证了一个预测模型，以帮助临床医生识别患有2019年冠状病毒病(新冠肺炎)的住院成年人，鉴于他们不良事件的低风险，他们可能已经准备好出院。这种算法在实践中是否能更及时地提示病情稳定的患者出院，目前尚不清楚。目的 本研究的目的是评估显示危险分数对住院时间(LOS)的影响。方法 我们将模型输出集成到一个医疗系统中的四家医院的电子健康记录中，方法是在患者列表列中显示指示低/中/高风险的绿色/橙色/红色分数，并为每个患者显示更大的新冠肺炎摘要报告。使用传递给模型执行代码的患者识别符，将分数的显示以1：1的伪随机化方式分为干预组和控制组。通过比较干预组和对照组的LOS来评估干预效果。死亡、临终关怀和复诊的不良安全结果分别进行测试，并作为一个综合指标。我们通过每天的分数显示来跟踪采用和持续使用情况。结果从2020年5月15日到2020年12月7日， 招募了1,010名患者，试验没有发现LOS有明显差异。干预对死亡、临终关怀或出院后复诊的安全指标没有影响。这些分数在整个研究期间都得到了一致的显示，但这项研究缺乏基于分数的提供者行为的因果联系过程衡量标准。二次分析揭示了LOS在时间上、主要症状和医院位置上的复杂动态。结论 是一种基于人工智能的新冠肺炎风险评分，在住院成人新冠肺炎的常规护理中被动显示给临床医生，是安全的，但对LOS没有明显影响。卫生技术挑战，如采用不足，使用不统一，提供商信任，再加上新冠肺炎大流行的时间因素，可能是导致无效结果的原因之一。试验注册 ClinicalTrials.gov标识符：NCT04570488。
Background We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown. Objectives The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS). Methods We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays. Results Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location. Conclusion An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result. Trial registration ClinicalTrials.gov identifier: NCT04570488.
登录
查看更多内容
影响因子：
2.7
作者：
Ogrinc, Greg;Davies, Louise;Stevens, David
通讯作者：
Stevens, David
影响因子：
24.3
作者：
Guan, Wei-jie;Liang, Wen-hua;He, Jian-xing
通讯作者：
He, Jian-xing
DOI：
10.1016/0021-9681(57)90048-6
发表时间：
1957-08-01
期刊：
Journal of chronic diseases
影响因子：
--
作者：
ROSENFELD, L S;GOLDMANN, F;KAPRIO, L A
通讯作者：
KAPRIO, L A
影响因子：
120.7
作者：
Begg, C;Cho, M;Stroup, DF
通讯作者：
Stroup, DF
影响因子：
8.9
作者：
Pereira NL;Ahmad F;Byku M;Cummins NW;Morris AA;Owens A;Tuteja S;Cresci S
通讯作者：
Cresci S