Sottile Peter D, Albers David, DeWitt Peter E, Russell Seth, Stroh J N, Kao David P, Adrian Bonnie, Levine Matthew E, Mooney Ryan, Larchick Lenny, Kutner Jean S, Wynia Matthew K, Glasheen Jeffrey J, Bennett Tellen D
medRxiv. 2021 Jan 15:2021.01.14.21249793. doi: 10.1101/2021.01.14.21249793.
The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality.
To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA.
We conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.
We prospectively analyzed 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) included intensive care unit (ICU)-level care, 1,480 (5.4%) included invasive mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted overall mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted overall mortality with AUROC 0.94. In the subset of patients with COVID-19, we predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.
We developed and validated an accurate, in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model, that improved upon SOFA.
Can we improve upon the SOFA score for real-time mortality prediction during the COVID-19 pandemic by leveraging electronic health record (EHR) data? We rapidly developed and implemented a novel yet SOFA-anchored mortality model across 12 hospitals and conducted a prospective cohort study of 27,296 adult hospitalizations, 1,358 (5.0%) of which were positive for SARS-CoV-2. The Charlson Comorbidity Index and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. A novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）已感染数百万人，使一些地区的重症监护资源不堪重负。许多在危机期间分配重症监护资源的计划都基于序贯器官衰竭评估（SOFA）评分。2019冠状病毒病（COVID-19）大流行迫切需要开发并验证一种新型的电子健康记录（EHR）可计算工具来预测死亡率。
为2019冠状病毒病大流行快速开发、验证并实施一种新型实时死亡率评分，以改进SOFA评分。
我们对2020年3月至2020年7月间科罗拉多州拥有12家医院的区域卫生系统进行了一项前瞻性队列研究。纳入研究期间所有年龄大于14岁且无“不要复苏”医嘱而住院的患者。患者按COVID-19诊断进行分层。从该队列中，我们通过堆叠泛化开发并验证了一个模型，通过结合五个先前验证的评分以及据报道与COVID-19特异性死亡率相关的其他新变量，利用EHR中广泛可用的数据预测死亡率。我们将新模型的受试者操作特征曲线下面积（AUROC）与SOFA评分和查尔森合并症指数进行比较。
我们前瞻性分析了27296次就诊，其中1358例（5.0%）SARS-CoV-2检测呈阳性，4494例（16.5%）接受了重症监护病房（ICU）级别的护理，1480例（5.4%）接受了有创机械通气，717例（2.6%）死亡。查尔森合并症指数和SOFA评分预测总体死亡率的AUROC分别为0.72和0.90。我们的新评分预测总体死亡率的AUROC为0.94。在COVID-19患者亚组中，我们预测死亡率的AUROC为0.90，而SOFA的AUROC为0.85。
我们开发并验证了一种准确的、基于实时EHR的院内死亡率预测评分，该评分使用一种新型模型进行自动和连续计算，改进了SOFA评分。
在2019冠状病毒病大流行期间，我们能否通过利用电子健康记录（EHR）数据改进SOFA评分以进行实时死亡率预测？我们在12家医院快速开发并实施了一种新型但以SOFA为基础的死亡率模型，并对27296例成人住院患者进行了前瞻性队列研究，其中1358例（5.0%）SARS-CoV-2检测呈阳性。查尔森合并症指数和SOFA评分预测全因死亡率的AUROC分别为0.72和0.90。我们的新评分预测死亡率的AUROC为0.94。一种基于新型EHR的死亡率评分可快速实施，以在不断演变的大流行期间更好地预测患者预后。
