Cheng Fu-Yuan, Joshi Himanshu, Tandon Pranai, Freeman Robert, Reich David L, Mazumdar Madhu, Kohli-Seth Roopa, Levin Matthew, Timsina Prem, Kia Arash
Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA.
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA.
J Clin Med. 2020 Jun 1;9(6):1668. doi: 10.3390/jcm9061668.
Approximately 20-30% of patients with COVID-19 require hospitalization, and 5-12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers' efforts and help hospitals plan their flow of operations.
A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated.
The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2-81.1%) sensitivity, 76.3% (95% CI: 74.7-77.9%) specificity, 76.2% (95% CI: 74.6-77.7%) accuracy, and 79.9% (95% CI: 75.2-84.6%) area under the receiver operating characteristics curve.
A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.
约20%-30%的新冠病毒病（COVID-19）患者需要住院治疗，5%-12%的患者可能需要在重症监护病房（ICU）接受重症监护。严重COVID-19病例的迅速增加将导致对ICU护理的需求相应激增。由于资源限制，一线医护人员可能无法对所有具有临床恶化高风险的患者进行所需的频繁监测和评估。我们开发了一种基于机器学习的风险优先级工具，可预测24小时内的ICU转运情况，旨在促进护理人员工作的高效利用，并帮助医院规划其运营流程。
回顾性队列研究纳入了2020年2月26日至4月18日期间在一个大型急症护理卫生系统非ICU病房收治的COVID-19患者。包括生命体征、护理评估、实验室数据和心电图在内的时间序列数据被用作训练随机森林（RF）模型的输入变量。该队列被随机分为训练集和测试集（70:30）。RF模型在训练集上使用10折交叉验证进行训练，然后评估其在测试集上的预测性能。
该队列由1987例确诊为COVID-19并入住该医院非ICU病房的患者组成。从入院到转入ICU的中位时间为2.45天。与实际转入ICU的情况相比，该工具的灵敏度为72.8%（95%CI：63.2%-81.1%），特异度为76.3%（95%CI：74.7%-77.9%），准确度为76.2%（95%CI：74.6%-77.7%），受试者工作特征曲线下面积为79.9%（95%CI：75.2%-84.6%）。
基于机器学习的预测模型可作为一种筛查工具，用于识别24小时内有即将转入ICU风险的患者。该工具可改善医院资源管理和患者流量规划，从而为COVID-19住院患者提供更有效的护理。
