Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients
Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients
复制标题
DOI：
10.3390/jcm9061668
复制
发表时间：
2020-06-01
影响因子：
3.9
通讯作者：
Kia, Arash
中科院分区：
文献类型：
--
作者：
Cheng, Fu-Yuan;Joshi, Himanshu;Kia, Arash
目的：大约20-30%的COVID-19患者需要住院治疗，5-12%的患者可能需要重症监护室（ICU）的重症监护。严重COVID-19病例的快速激增将导致对ICU护理的需求相应激增。由于资源所限，前缐医护人员可能无法为所有有高风险出现病情恶化的病人提供所需的频密监察和评估。我们开发了一种基于机器学习的风险优先级排序工具，可在24小时内预测ICU转移，旨在促进有效利用护理提供者的努力，并帮助医院规划其运营流程。研究方法：回顾性队列由2020年2月26日至4月18日期间在大型急性护理卫生系统的非ICU COVID-19住院患者组成。时间序列数据，包括生命体征，护理评估，实验室数据和心电图，被用作训练随机森林（RF）模型的输入变量。队列被随机分为（70：30）训练集和测试集。使用10倍交叉验证对训练集训练RF模型，然后评估其对测试集的预测性能。结果：该队列由1987名确诊为COVID-19并入住医院非ICU病房的独特患者组成。从入院到转入ICU的中位时间为2.45天。与实际入院相比，该工具的灵敏度为72.8%（95% CI：63.2-81.1%），特异性为76.3%（95% CI：74.7-77.9%），准确性为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%）。结论：基于ML的预测模型可用作筛选工具，以识别24 h内即将转入ICU的风险患者。该工具可以改善医院资源管理和患者吞吐量规划，从而为因COVID-19住院的患者提供更有效的护理。
Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.