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Background: Providing appropriate care for people suffering from COVID-19,the disease caused by the pandemic SARS-CoV-2 virus is a significant globalchallenge. Many individuals who become infected have pre-existing conditionsthat may interact with COVID-19 to increase symptom severity and mortalityrisk. COVID-19 patient comorbidities are likely to be informative aboutindividual risk of severe illness and mortality. Accurately determining howcomorbidities are associated with severe symptoms and mortality would thusgreatly assist in COVID-19 care planning and provision.  Methods: To assess the interaction of patient comorbidities with COVID-19severity and mortality we performed a meta-analysis of the published globalliterature, and machine learning predictive analysis using an aggregatedCOVID-19 global dataset.  Results: Our meta-analysis identified chronic obstructive pulmonary disease(COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2diabetes, malignancy, and hypertension as most significantly associated withCOVID-19 severity in the current published literature. Machine learningclassification using novel aggregated cohort data similarly found COPD, CVD,CKD, type 2 diabetes, malignancy and hypertension, as well as asthma, as themost significant features for classifying those deceased versus those whosurvived COVID-19. While age and gender were the most significant predictor ofmortality, in terms of symptom-comorbidity combinations, it was observed thatPneumonia-Hypertension, Pneumonia-Diabetes and Acute Respiratory DistressSyndrome (ARDS)-Hypertension showed the most significant effects on COVID-19mortality.  Conclusions: These results highlight patient cohorts most at risk of COVID-19related severe morbidity and mortality which have implications forprioritization of hospital resources.

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