The role of comorbidities and clinical predictors of severe disease in COVID-19: a systematic review and meta-analysis

Background COVID_19 is unpredictable due to non-specific symptoms and clinical course diversity in different individuals. We analyzed studies regarding the factors associated with severe status of the disease to identify unique findings in severely affected patients. Methods We systematically searched the electronic databases, including PubMed, Scopus, EMBASE, Web of ‎Science, and Google Scholar from inception to 12th of March 2020. Cochranes Q and I-square statistics were used to assess the existence of heterogeneity between the included ‎studies. We used the random-effects model to pool ‎the odds ratios (ORs) at 95% confidence ‎intervals (CIs).‎ Results Seventeen articles out of 3009 citations were included. These contained 3189 patients, of whom 732 were severely affected (severe group) and 3189 were in non-severe group. Using the random-effects model, our meta-analyses showed that the odds of comorbidities, including COPD, DM, HTN, CVD, CKD, and symptoms, including dyspnea, dizziness, anorexia, and cough, were significantly higher among the severe group compared with the non-‎severe group. There were no significant changes in odds of CVA, liver disease, immunodeficiency/immunosuppression, fever, fatigue, myalgia, headache, diarrhea, sore throat, nasal congestion, sputum, nausea, vomiting, chest pain between the two groups. Conclusions Early recognition and intervention can be critical in management, and might stop progression to severe disease. Predictive symptoms and comorbidities can be used as a predictor in patients who are at risk of severe disease.