Center for Information and Systems Engineering, Boston University, Boston, Massachusetts, USA.
Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts, USA.
J Am Med Inform Assoc. 2022 Jun 14;29(7):1253-1262. doi: 10.1093/jamia/ocac062.
To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs.
Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models.
Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively.
The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories.
This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role.
开发针对 2019 年冠状病毒病（COVID-19）结局的预测模型，阐明社会经济因素的影响，并使用具有高社会需求的种族多样化患者群体评估算法的种族公平性。
数据包括马萨诸塞州一个医疗保障系统中 7102 例经聚合酶链反应（RT-PCR）检测呈阳性的严重急性呼吸综合征冠状病毒 2 检测的患者。采用线性和非线性分类方法。基于递归神经网络和变压器架构开发了一个评分系统，以捕捉生命体征的动态演变。该动态生命评分与患者特征、临床变量和医院占用率指标相结合，用于训练预测模型。
使用症状、医院占用率和患者特征（包括健康的社会决定因素），可以预测住院情况，其受试者工作特征曲线下面积（AUC）为 92%。使用最近的实验室和生命体征来预测重症监护、机械通气和死亡率的简约模型，其 AUC 分别为 92.7%、91.2%和 94%。使用更接近入院时的实验室和生命体征的早期预测模型，其 AUC 分别为 81.1%、84.9%和 92%。
最准确的模型表现出种族偏见，更有可能错误地预测黑人患者将住院。仅基于动态生命体征的模型的准确性接近最佳简约模型，尽管后者也使用实验室。
这项大型研究表明，使用考虑生命体征动态演变的评分可以准确预测 COVID-19 的严重程度。此外，种族、健康的社会决定因素和医院占用率起着重要作用。
