Douglas Molly J, Bell Brian W, Kinney Adrienne, Pungitore Sarah A, Toner Brian P
Department of Surgery, University of Arizona, Tucson, Arizona, USA.
Program in Applied Mathematics, University of Arizona, Tucson, Arizona, USA.
Trauma Surg Acute Care Open. 2022 Aug 30;7(1):e000892. doi: 10.1136/tsaco-2022-000892. eCollection 2022.
COVID-19 has strained healthcare systems globally. In this and future pandemics, providers with limited critical care experience must distinguish between moderately ill patients and those who will require aggressive care, particularly endotracheal intubation. We sought to develop a machine learning-informed Early COVID-19 Respiratory Risk Stratification (ECoRRS) score to assist in triage, by providing a prediction of intubation within the next 48 hours based on objective clinical parameters.
Electronic health record data from 3447 COVID-19 hospitalizations, 20.7% including intubation, were extracted. 80% of these records were used as the derivation cohort. The validation cohort consisted of 20% of the total 3447 records. Multiple randomizations of the training and testing split were used to calculate confidence intervals. Data were binned into 4-hour blocks and labeled as cases of intubation or no intubation within the specified time frame. A LASSO (least absolute shrinkage and selection operator) regression model was tuned for sensitivity and sparsity.
Six highly predictive parameters were identified, the most significant being fraction of inspired oxygen. The model achieved an area under the receiver operating characteristic curve of 0.789 (95% CI 0.785 to 0.812). At 90% sensitivity, the negative predictive value was 0.997.
The ECoRRS score enables non-specialists to identify patients with COVID-19 at risk of intubation within 48 hours with minimal undertriage and enables health systems to forecast new COVID-19 ventilator needs up to 48 hours in advance.
IV.
新型冠状病毒肺炎（COVID-19）使全球医疗系统不堪重负。在此次及未来的大流行中，重症监护经验有限的医护人员必须区分病情中等的患者和那些需要积极治疗的患者，尤其是气管插管治疗的患者。我们试图开发一种基于机器学习的早期COVID-19呼吸风险分层（ECoRRS）评分，通过根据客观临床参数预测未来48小时内的插管情况，以协助进行分诊。
提取了3447例COVID-19住院患者的电子健康记录数据，其中20.7%包括插管治疗。这些记录的80%用作推导队列。验证队列由3447条记录总数的20%组成。训练和测试分割的多次随机化用于计算置信区间。数据按4小时时间段进行分组，并标记为在指定时间范围内是否进行插管治疗。对套索（LASSO，最小绝对收缩和选择算子）回归模型进行敏感性和稀疏性调整。
确定了六个高度预测性参数，其中最显著的是吸入氧分数。该模型在受试者工作特征曲线下的面积为0.789（95%CI 0.785至0.812）。在90%的敏感性下，阴性预测值为0.997。
ECoRRS评分使非专科医生能够以最小的分诊失误识别出48小时内有插管风险的COVID-19患者，并使卫生系统能够提前48小时预测新的COVID-19呼吸机需求。
IV级。
