[论文解读] Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19

Sakifa Aktar, Ashis Talukder+10 more|arXiv (Cornell University)|Aug 21, 2020

COVID-19 Clinical Research Studies参考文献 33被引用 7

一句话总结

本研究结合全球文献的元分析与聚合的COVID-19患者数据的机器学习方法，识别出与死亡率显著相关的共病及症状组合。主要发现表明，COPD、CVD、2型糖尿病、高血压、CKD和恶性肿瘤为最主要的危险因素，其中肺炎-高血压和ARDS-高血压组合与死亡率的关联最强，即使在控制年龄和性别后依然显著。

ABSTRACT

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.

研究动机与目标

• 识别在COVID-19患者中显著增加死亡风险的共病与症状。
• 通过综合数据整合方法，评估既存疾病与COVID-19严重程度之间的相互作用。
• 通过识别高风险患者群体，改进临床风险分层，以支持针对性的护理规划。
• 利用大规模聚合的全球数据集进行机器学习，验证研究发现。
• 通过识别严重结局的最关键风险因素，为医院资源优先分配提供建议。

提出的方法

• 对全球发表的关于COVID-19共病与死亡率结果的文献进行元分析。
• 收集并聚合大规模全球COVID-19患者记录数据，用于机器学习分析。
• 应用监督式机器学习分类方法，识别对生存与死亡最具预测力的特征。
• 使用特征重要性分析，按其对死亡率的预测能力对共病与症状组合进行排序。
• 结合元分析与机器学习的结果，验证并交叉核对主要风险因素。
• 评估症状与共病之间的交互效应，如肺炎-高血压与ARDS-高血压组合对死亡率的影响。

实验结果

研究问题

• RQ1 根据全球文献，哪些共病与COVID-19患者死亡率的增加最为显著相关？
• RQ2 在聚合数据上使用机器学习分析时，哪些患者症状与共病对死亡最具预测力？
• RQ3 症状与共病的组合（如肺炎合并高血压）与单一因素相比，如何影响死亡率风险？
• RQ4 年龄与性别在多大程度上调节共病对COVID-19死亡率的影响？
• RQ5 哪些共病在元分析与机器学习方法中均一致地表现为最突出的预测因子？

主要发现

• 元分析显示，慢性阻塞性肺病（COPD）、脑血管病（CEVD）、心血管疾病（CVD）、2型糖尿病、恶性肿瘤和高血压与COVID-19严重程度显著相关。
• 在聚合的全球数据上进行的机器学习识别出COPD、CVD、CKD、2型糖尿病、恶性肿瘤、高血压和哮喘为死亡率的最重要预测因子。
• 年龄与性别是死亡率的最重要个体预测因子，但症状-共病组合显示出更强的效应量。
• 肺炎-高血压与ARDS-高血压组合与死亡率增加的关联最为显著。
• 元分析与机器学习方法在结果上的一致性增强了所识别风险因素的有效性。
• 研究结果支持对具有这些共病的患者优先实施早期干预与资源分配，以优化医疗规划。