机器学习和荟萃分析方法用于识别增加COVID-19死亡风险的患者合并症和症状
Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19
摘要：
背景资料：为COVID-19患者提供适当的护理是一项重大的全球挑战。许多受感染的人都有可能与COVID-19相互作用的预先存在的疾病，从而增加症状的严重程度和死亡风险。COVID-19患者的合并症可能是关于严重疾病和死亡的个体风险的信息。因此，准确确定合并症如何与严重症状和死亡率相关，将极大地有助于COVID-19护理规划和提供。研究方法：为了评估患者合并症与COVID-19严重程度和死亡率的相互作用，我们对已发表的全球文献进行了荟萃分析，并使用汇总的COVID-19全球数据集进行了机器学习预测分析。结果如下：我们的荟萃分析确定，在当前发表的文献中，慢性阻塞性肺疾病（COPD）、脑血管疾病（CEVD）、心血管疾病（CVD）、2型糖尿病、恶性肿瘤和高血压与COVID-19严重程度最显著相关。机器学习使用新的聚合队列数据进行分类，同样发现COPD、CVD、CKD、2型糖尿病、恶性肿瘤和高血压以及哮喘是对死亡者与COVID-19幸存者进行分类的最重要特征。虽然年龄和性别是死亡率的最重要预测因素，但就合并症组合而言，观察到肺炎-高血压、肺炎-糖尿病和急性呼吸窘迫综合征（ARDS）-高血压对COVID-19死亡率的影响最显著。结论：这些结果突出了最有可能发生COVID-19相关严重发病率和死亡率的患者队列，这对医院资源的优先排序有影响。
英文摘要：
Background: Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus is a significant global challenge. Many individuals who become infected have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative about individual risk of severe illness and mortality. Accurately determining how comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. Methods: To assess the interaction of patient comorbidities with COVID-19 severity and mortality we performed a meta-analysis of the published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Results: Our meta-analysis identified chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictor of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant effects on COVID-19 mortality. Conclusions: These results highlight patient cohorts most at risk of COVID-19 related severe morbidity and mortality which have implications for prioritization of hospital resources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
