Background: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. Objective: The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics—baseline demographics, comorbidities, and symptoms. Methods: A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). Results: A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively. Conclusions: We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.

中文翻译：

---

易于使用的机器学习模型来预测COVID-19患者的预后：回顾性队列研究

背景：为降低COVID-19大流行期间的死亡率，必须对需要重症监护的患者进行优先处理。尽管已经引入了几种计分方法，但许多计分方法需要实验室或射线照相的发现，而这些发现并不总是很容易获得。目的：本研究的目的是开发一种机器学习模型，该模型使用易于获得的特征（基线人口统计学，合并症和症状）来预测COVID-19患者的重症监护需求。方法：使用韩国全国范围的队列进行回顾性研究。纳入自2020年1月25日至2020年6月3日在100所医院住院的患者。由每家医院的主治医生回顾性收集患者信息，并将其上传到在线病例报告表。提取了可以轻松提供的变量。这些变量是年龄，性别，吸烟史，体温，合并症，日常生活活动和症状。主要结果是需要重症监护，即入院重症监护病房，使用体外生命支持，机械通气，升压药或住院30天内死亡。入组至2020年3月20日的患者将使用自动机器学习技术开发预测模型。该模型在2020年3月21日之后入院的患者中进行了外部验证。选择了具有最佳判别性能的机器学习模型，并将其与CURB-65（精神错乱，尿素，呼吸频率，血压，且年龄在65岁及以上的用户）使用接收器工作特征曲线（AUC）下的区域得分。结果：共纳入4787例患者，其中3294例归入衍生组，1493例归入验证组。在4787名患者中，有460名（9.6％）患者需要重症监护。在开发的55种机器学习模型中，XGBoost模型显示出最高的识别性能。对于衍生组，XGBoost模型的AUC为0.897（95％CI 0.877-0.917），对于验证组，其AUC为0.885（95％CI 0.855-0.915）。两种AUC均优于CURB-65，分别为0.836（95％CI 0.825-0.847）和0.843（95％CI 0.829-0.857）。结论：我们开发了一种包含简单的患者提供的特征的机器学习模型，

这仅仅是抽象的。阅读JMIR网站上的全文。JMIR是互联网时代电子健康和医疗保健领域领先的开放获取期刊。