Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial
Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial
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DOI：
10.1016/j.compbiomed.2020.103949
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发表时间：
2020-09-01
影响因子：
7.7
通讯作者：
Das, Ritankar
中科院分区：
文献类型：
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作者：
Burdick, Hoyt;Lam, Carson;Das, Ritankar
背景：目前，医生为COVID-19阳性患者提供准确预后的能力有限。现有的评分系统对于识别患者失代偿是无效的。机器学习（ML）可以提供一种替代策略。一个前瞻性验证的方法来预测COVID-19患者的通气需求是必不可少的，以帮助分诊患者，分配资源，并防止紧急插管及其相关风险。方法：在一项多中心临床试验中，我们评估了机器学习算法的性能，用于预测COVID-19患者在初次就诊后24小时内的有创机械通气。我们招募了2020年3月24日至5月4日期间在美国五个卫生系统住院的COVID-19诊断患者。结果：197名患者参加了RESPIRATORY失代偿和COVID-19患者分诊模型：一项前瞻性研究（READY）临床试验。该算法预测通气的诊断比值比（DOR，12.58）高于比较预警系统，即改良预警评分（MEWS）。该算法的灵敏度（0.90）也显著高于MEWS，MEWS的灵敏度为0.78，同时保持了更高的特异性（p < 0.05）。结论：在COVID-19患者通气需求机器学习算法的首次临床试验中，该算法证明了24小时内对机械通气需求的准确预测。该算法可以帮助护理团队有效地对患者进行分类并分配资源。此外，该算法能够准确识别比广泛使用的评分系统多16%的患者，同时最大限度地减少假阳性结果。
Background: Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks.Methods: In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020.Results: 197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05).Conclusions: In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.