PDF(Pubmed)
Abstract：
COVID-19 mortality prediction Background COVID-19 has become a major global public health problem, despite prevention and efforts. The daily number of COVID-19 cases rapidly increases, and the time and financial costs associated with testing procedure are burdensome. Method To overcome this, we aim to identify immunological and metabolic biomarkers to predict COVID-19 mortality using a machine learning model. We included inpatients from Hong Kong\'s public hospitals between January 1, and September 30, 2020, who were diagnosed with COVID-19 using RT-PCR. We developed three machine learning models to predict the mortality of COVID-19 patients based on data in their electronic medical records. We performed statistical analysis to compare the trained machine learning models which are Deep Neural Networks (DNN), Random Forest Classifier (RF) and Support Vector Machine (SVM) using data from a cohort of 5,059 patients (median age = 46 years; 49.3% male) who had tested positive for COVID-19 based on electronic health records and data from 532,427 patients as controls. Result We identified top 20 immunological and metabolic biomarkers that can accurately predict the risk of mortality from COVID-19 with ROC-AUC of 0.98 (95% CI 0.96-0.98). Of the three models used, our result demonstrate that the random forest (RF) model achieved the most accurate prediction of mortality among COVID-19 patients with age, glomerular filtration, albumin, urea, procalcitonin, c-reactive protein, oxygen, bicarbonate, carbon dioxide, ferritin, glucose, erythrocytes, creatinine, lymphocytes, PH of blood and leukocytes among the most important biomarkers identified. A cohort from Kwong Wah Hospital (131 patients) was used for model validation with ROC-AUC of 0.90 (95% CI 0.84-0.92). Conclusion We recommend physicians closely monitor hematological, coagulation, cardiac, hepatic, renal and inflammatory factors for potential progression to severe conditions among COVID-19 patients. To the best of our knowledge, no previous research has identified important immunological and metabolic biomarkers to the extent demonstrated in our study.
UNASSIGNED: The online version contains supplementary material available at 10.1186/s44247-022-00001-0.
UNASSIGNED: The online version contains supplementary material available at 10.1186/s44247-022-00001-0.
摘要：
COVID-19死亡率预测背景COVID-19已成为全球主要的公共卫生问题，尽管有预防和努力。每天COVID-19病例数迅速增加，与测试程序相关的时间和财务成本是繁重的。方法为了克服这一点，我们的目标是使用机器学习模型鉴定免疫和代谢生物标志物以预测COVID-19死亡率.我们纳入了2020年1月1日至9月30日期间香港公立医院的住院患者，这些患者使用RT-PCR诊断为COVID-19。我们开发了三种机器学习模型来根据COVID-19患者的电子病历数据预测其死亡率。我们进行了统计分析，以比较深度神经网络(DNN)的训练后的机器学习模型，随机森林分类器(RF)和支持向量机(SVM)使用来自5059名患者(中位年龄=46岁；49.3%男性)的数据，这些患者基于电子健康记录和532,427名患者作为对照的数据检测出COVID-19阳性。结果我们确定了可以准确预测COVID-19死亡风险的前20种免疫和代谢生物标志物，ROC-AUC为0.98(95%CI0.96-0.98)。在使用的三种模型中，我们的结果表明，随机森林（RF）模型在COVID-19患者中实现了最准确的死亡率预测，肾小球滤过,白蛋白,尿素,降钙素原,c反应蛋白,氧气,碳酸氢盐,二氧化碳,铁蛋白,葡萄糖，红细胞,肌酐,淋巴细胞，血液和白细胞的PH是确定的最重要的生物标志物。来自广华医院的队列（131名患者）用于模型验证，ROC-AUC为0.90（95％CI0.84-0.92）。结论建议医师密切监测血液学,凝血,心脏,肝，COVID-19患者中肾脏和炎症因子可能进展为严重疾病。据我们所知,以前的研究中没有发现重要的免疫和代谢生物标志物,达到我们研究中所证明的程度.
■在线版本包含补充材料，可在10.1186/s44247-022-00001-0获得。
■在线版本包含补充材料，可在10.1186/s44247-022-00001-0获得。
