Kim Hyung-Jun, Han Deokjae, Kim Jeong-Han, Kim Daehyun, Ha Beomman, Seog Woong, Lee Yeon-Kyeng, Lim Dosang, Hong Sung Ok, Park Mi-Jin, Heo JoonNyung
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Armed Forces Capital Hospital, Seongnam, Republic of Korea.
Division of Infectious Diseases, Department of Internal Medicine, Armed Forces Capital Hospital, Seongnam, Republic of Korea.
J Med Internet Res. 2020 Nov 9;22(11):e24225. doi: 10.2196/24225.
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.
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.
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).
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.
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.
在新冠疫情期间，对需要重症监护的患者进行优先排序对于降低死亡率至关重要。尽管已经引入了几种评分方法，但许多方法需要实验室或影像学检查结果，而这些结果并非总是容易获得。
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）来预测新冠患者对重症监护的需求。
使用韩国的全国性队列进行了一项回顾性研究。纳入了2020年1月25日至2020年6月3日期间入住100家医院的患者。每家医院的主治医生回顾性收集患者信息，并上传至在线病例报告表。提取易于提供的变量。这些变量包括年龄、性别、吸烟史、体温、合并症、日常生活活动能力和症状。主要结局是对重症监护的需求，定义为入住重症监护病房、使用体外生命支持、机械通气、血管活性药物或在住院30天内死亡。将2020年3月20日前入院的患者纳入推导组，使用自动机器学习技术开发预测模型。这些模型在2020年3月21日后入院的患者中进行外部验证。选择具有最佳区分性能的机器学习模型，并使用受试者操作特征曲线下面积（AUC）与CURB-65（意识模糊、尿素、呼吸频率、血压和65岁及以上）评分进行比较。
共有4787例患者纳入分析，其中3294例被分配到推导组，1493例被分配到验证组。在这4787例患者中，460例（9.6%）需要重症监护。在开发的55种机器学习模型中，XGBoost模型显示出最高的区分性能。推导组中XGBoost模型的AUC为0.897（95%CI 0.877-0.917），验证组为0.885（95%CI 0.855-0.915）。这两个AUC均优于CURB-65评分的AUC，后者分别为0.836（95%CI 0.825-0.847）和0.843（95%CI 0.829-0.857）。
我们开发了一种包含患者提供的简单特征的机器学习模型，该模型可以有效预测新冠患者对重症监护的需求。
