Faculty of Health Studies, University of Bradford, Bradford, UK.
Wolfson Centre for Applied Health Research, Bradford Royal Infirmary, Bradford, UK.
BMJ Open. 2022 Aug 30;12(8):e050274. doi: 10.1136/bmjopen-2021-050274.
There are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and the National Early Warning Score (NEWS2).
Logistic regression model development and validation study.
Two acute hospitals (York Hospital-model development data; Scarborough Hospital-external validation data).
Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic NEWS and blood test results recorded on admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: (1) CARMc19_N: age+sex+NEWS2 including subcomponents+COVID19; (2) CARMc19_NB: CARMc19_N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically) and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+.
The risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for CARMc19_NB is better than CARMc19_N in the validation dataset (CARMc19_NB=0.88 (95% CI 0.86 to 0.90) vs CARMc19_N=0.86 (95% CI 0.83 to 0.88)). Both models had good calibration (CARMc19_NB=1.01 (95% CI 0.88 to 1.14) and CARMc19_N:0.95 (95% CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model, CARMc19_NB had better sensitivity and similar specificity.
We have developed a validated CARMc19 scores with good performance characteristics for predicting the risk of in-hospital mortality. Since the CARMc19 scores place no additional data collection burden on clinicians, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.
目前尚无专门针对包括 SARS-19(COVID-19)患者在内的计划性急诊医疗入院的死亡率风险方程。我们旨在开发和验证一种计算机辅助风险评分(CARMc19),该评分通过结合 COVID-19 状态、首次电子记录的血液测试结果和国家早期预警评分(NEWS2)来预测死亡率风险。
逻辑回归模型的开发和验证研究。
两家急性医院(约克医院-模型开发数据;斯卡伯勒医院-外部验证数据)。
在 24 个月的时间内接受电子 NEWS 和入院时记录的血液测试结果的成年(年龄≥16 岁)内科入院患者。我们使用逻辑回归模型来预测住院死亡率的风险,使用两种模型:(1)CARMc19_N:年龄+性别+NEWS2 包括子组件+COVID19;(2)CARMc19_NB:CARMc19_N 与七种血液测试结果和急性肾损伤评分结合使用。根据区分度(c 统计量)、校准(图形)和 NEWS2 阈值为 4+、5+、6+时的临床有用性来评估模型性能。
急诊医疗入院后住院死亡率在开发和验证数据集中相似(8.4%对 8.2%)。CARMc19_NB 预测死亡率的 C 统计量优于验证数据集中的 CARMc19_N(CARMc19_NB=0.88(95%CI 0.86 至 0.90)对 CARMc19_N=0.86(95%CI 0.83 至 0.88))。两个模型的校准都很好(CARMc19_NB=1.01(95%CI 0.88 至 1.14)和 CARMc19_N:0.95(95%CI 0.83 至 1.06))。在所有 NEWS2 阈值(4+、5+、6+)模型中,CARMc19_NB 的敏感性更高,特异性相似。
我们开发了一种经过验证的 CARMc19 评分,具有良好的预测住院死亡率风险的性能特征。由于 CARMc19 评分不会给临床医生增加额外的数据收集负担,因此它现在可以在具有足够信息学基础设施的医院中进行仔细的引入和评估。