Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.
Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.
复制标题
用于识别快速恶化高风险的 COVID-19 患者的动态预后模型的前瞻性验证。
DOI：
10.1002/pds.5580
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发表时间：
2023
影响因子：
2.6
通讯作者：
Wang,ShirleyV
中科院分区：
文献类型：
--
作者：
Lin,KueiyuJoshua;D'Andrea,Elvira;Desai,RishiJ;Gagne,JoshuaJ;Liu,Jun;Wang,ShirleyV
背景我们试图开发并前瞻性验证一个动态模型，该模型结合生物标志物的变化来预测因COVID-19住院患者的快速临床恶化。方法我们使用来自马萨诸塞州大型综合护理提供网络的电子健康记录（EHR），建立了一个年龄≥18岁的实验室确诊COVID-19住院患者的回顾性队列，包括> 2020年3月至11月的40个设施。共筛选了71个因素，包括住院期间随时间变化的生命体征和实验室检查结果。我们使用弹性网络回归和基于树的扫描统计进行变量选择，以预测快速恶化，定义为在未来24小时内进展两个已发表的严重程度量表。开发队列包括按日历时间顺序确定的前70%的患者;后30%作为验证队列。估计了一个临界点，以提醒临床医生即将发生临床恶化的高风险。结果总体而言，3706名患者（开发队列中2587名，验证队列中1119名）符合资格标准，中位随访时间为6天。在最终模型中选择了24个变量，包括16个实验室结果或生命体征的动态变化。开发集的ROC曲线下面积为0.81（95% CI，0.79-0.82），验证集为0.74（95% CI，0.71-0.78）。该模型经过良好校准（在验证集中的校准图上，斜率= 0.84，截距=-0.07）。阳性预测值为83%的估计截断点为0.78。结论我们的前瞻性验证的动态预后模型在快速发展的流行病中表现出时间的普遍性，可用于根据生物生理因素的动态变化为日常治疗和资源分配决策提供信息。
BackgroundWe sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID‐19.MethodsWe established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory‐confirmed COVID‐19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including >40 facilities from March to November 2020. A total of 71 factors, including time‐varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree‐based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 h. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut‐off point was estimated to alert clinicians of high risk of imminent clinical deterioration.ResultsOverall, 3706 patients (2587 in the development and 1119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow‐up. Twenty‐four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79–0.82) in the development set and 0.74 (95% CI, 0.71–0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = −0.07 on the calibration plot in the validation set). The estimated cut‐off point, with a positive predictive value of 83%, was 0.78.ConclusionsOur prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day‐to‐day treatment and resource allocation decisions based on dynamic changes in biophysiological factors.