Arutyunov Gregory P, Tarlovskaya Ekaterina I, Polyakov Dmitry S, Batluk Tatiana I, Arutyunov Alexander G
Eurasian Association of Internal Medicine, Moscow, Russia.
Department of Propaedeutics of Internal Diseases (Pediatric School), Pirogov Russian National Research Medical University, Moscow, Russia.
Heliyon. 2024 Mar 30;10(7):e28892. doi: 10.1016/j.heliyon.2024.e28892. eCollection 2024 Apr 15.
The of this study is to investigate the course of the acute period of COVID-19 and devise a prognostic scale for patients hospitalized.
The ACTIV registry encompassed both male and female patients aged 18 years and above, who were diagnosed with COVID-19 and subsequently hospitalized. Between June 2020 and March 2021, a total of 9364 patients were enrolled across 26 medical centers in seven countries. Data collected during the patients' hospital stay were subjected to multivariate analysis within the R computational environment. A predictive mathematical model, utilizing the "Random Forest" machine learning algorithm, was established to assess the risk of reaching the endpoint (defined as in-hospital death from any cause). This model was constructed using a training subsample (70% of patients), and subsequently tested using a control subsample (30% of patients).
Out of the 9364 hospitalized COVID-19 patients, 545 (5.8%) died. Multivariate analysis resulted in the selection of eleven variables for the final model: minimum oxygen saturation, glomerular filtration rate, age, hemoglobin level, lymphocyte percentage, white blood cell count, platelet count, aspartate aminotransferase, glucose, heart rate, and respiratory rate. Receiver operating characteristic analysis yielded an area under the curve of 89.2%, a sensitivity of 86.2%, and a specificity of 76.0%. Utilizing the final model, a predictive equation and nomogram (termed the ACTIV scale) were devised for estimating in-hospital mortality amongst COVID-19 patients.
The ACTIV scale provides a valuable tool for practicing clinicians to predict the risk of in-hospital death in patients hospitalized with COVID-19.
本研究的目的是调查新型冠状病毒肺炎（COVID-19）急性期的病程，并为住院患者设计一种预后量表。
ACTIV注册研究纳入了年龄在18岁及以上、被诊断为COVID-19并随后住院的男性和女性患者。在2020年6月至2021年3月期间，七个国家的26个医疗中心共招募了9364名患者。患者住院期间收集的数据在R计算环境中进行多变量分析。利用“随机森林”机器学习算法建立了一个预测数学模型，以评估达到终点（定义为任何原因导致的住院死亡）的风险。该模型使用训练子样本（70%的患者）构建，随后使用对照子样本（30%的患者）进行测试。
在9364例住院的COVID-19患者中，545例（5.8%）死亡。多变量分析最终模型选择了11个变量：最低血氧饱和度、肾小球滤过率、年龄、血红蛋白水平、淋巴细胞百分比、白细胞计数、血小板计数、天冬氨酸转氨酶、血糖、心率和呼吸频率。受试者工作特征分析得出曲线下面积为89.2%，灵敏度为86.2%，特异性为76.0%。利用最终模型，设计了一个预测方程和列线图（称为ACTIV量表），用于估计COVID-19患者的住院死亡率。
ACTIV量表为临床医生预测COVID-19住院患者的住院死亡风险提供了一个有价值的工具。
