■准确预测COVID-19预后仍然是一个临床挑战。在这方面,早期发现重症病例有助于COVID-19病例的分诊和治疗.本文旨在探讨COVID-19患者入院时常规实验室检查的预后。
■一组数据集,包括1455名COVID-19患者(727名男性,728名女性)及其入院时进行的常规实验室检查,年龄,重症监护病房(ICU)入院,并收集了结果。将数据集随机分成训练(75%的数据)和测试数据集(25%的数据)。可解释增强机(EBM)和极端梯度增强(XGBoost)用于预测COVID-19病例的死亡率和ICU入院。此外,使用EBM和XGBoost提取特征重要性。
■EBM和XGBoost在测试数据集中达到了86.38%和88.56%的准确性,分别。此外,EBM和XGBoost预测ICU入院的准确率为89.37%,COVID-19患者的测试数据为79.29%,分别。此外,获得的模型表明天冬氨酸转氨酶(AST),淋巴细胞,血尿素氮(BUN),年龄和年龄是COVID-19死亡率的最重要预测因素。此外,淋巴细胞计数,AST,和BUN水平是COVID-19患者最重要的ICU入院预测因子。
■目前的研究表明,根据入院时的常规血液学和临床化学评估,EBM和XGBoost均可预测COVID-19患者的ICU入院和死亡率。此外,根据结果,AST,淋巴细胞计数,BUN水平可作为COVID-19预后的早期预测因子。
UNASSIGNED: The precise prediction of COVID-19 prognosis remains a clinical challenge. In this regard, early identification of severe cases facilitates the triage and management of COVID-19 cases. The present paper aims to explore the prognosis of COVID-19 patients based on routine laboratory tests taken when patients are admitted.
UNASSIGNED: A data set including 1455 COVID-19 patients (727 male, 728 female) and their routine laboratory tests conducted upon hospital admission, age, Intensive Care Unit (ICU) admission, and outcome were gathered. The data set was randomly split into the train (75% of the data) and test data set (25% of the data). The explainable boosting machine (EBM) and extreme gradient boosting (XGBoost) were used for predicting the mortality and ICU admission of COVID-19 cases. Also, feature importance was extracted using EBM and XGBoost.
UNASSIGNED: The EBM and XGBoost achieved 86.38% and 88.56% accuracy in the test data set, respectively. In addition, EBM and XGBoost predicted the ICU admission with an accuracy of 89.37%, and 79.29% in the test data set for COVID-19 patients, respectively. Also, obtained models indicated that aspartate transaminase (AST), lymphocyte, blood urea nitrogen (BUN), and age are the most significant predictors of COVID-19 mortality. Furthermore, the lymphocyte count, AST, and BUN level were the most significant ICU admission predictors of COVID-19 patients.
UNASSIGNED: The current study indicated that both EBM and XGBoost could predict the ICU admission and mortality of COVID-19 cases based on routine hematological and clinical chemistry evaluation at the time of admission. Also, based on the results, AST, lymphocyte count, and BUN levels could be used as early predictors of COVID-19 prognosis.