The Association between Presence of Comorbidities and COVID-19 Severity: A Systematic Review and Meta-Analysis.

AIM: Several studies reported the accompaniment of severe COVID-19 with comorbidities. However, there is not a systematic evaluation of all aspects of this association. Therefore, this meta-analysis aimed to assess the association between all underlying comorbidities in COVID-19 infection severity. METHODS: Electronic literature search was performed via scientific search engines. After the removal of duplicates and selection of articles of interest, 28 studies were included. A fixed-effects model was used; however, if heterogeneity was high (I2 > 50%) a random-effects model was applied to combine the data. RESULTS: A total of 6,270 individuals were assessed (1,615 severe and 4,655 non-severe patients). The median age was 63 (95% confidence interval [CI]: 49-74) and 47 (95% CI: 19-63) years in the severe and non-severe groups, respectively. Moreover, about 41% of patients had comorbidities. Severity was higher in patients with a history of cerebrovascular disease: OR 4.85 (95% CI: 3.11-7.57). The odds of being in a severe group increase by 4.81 (95% CI: 3.43-6.74) for a history of cardiovascular disease (CVD). This was 4.19 (95% CI: 2.84-6.19) for chronic lung disease and 3.18, 95% CI: 2.09-4.82 for cancer. The odds ratios of diabetes and hypertension were 2.61 (95% CI: 2.02-3.3) and 2.37 (95% CI: 1.80-3.13), respectively. CONCLUSIONS: The presence of comorbidities is associated with severity of COVID-19 infection. The strongest association was observed for cerebrovascular disease, followed by CVD, chronic lung disease, cancer, diabetes, and hypertension.

Typical chest CT features can determine the severity of COVID-19: A systematic review and meta-analysis of the observational studies.

BACKGROUND: It remains unclear whether a specific chest CT characteristic is associated with the clinical severity of COVID-19. This meta-analysis was performed to assess the relationship between different chest CT features and severity of clinical presentation in COVID-19. METHODS: PubMed, Embase, Scopus, web of science databases (WOS), Cochrane library, and Google scholar were searched up to May 19, 2020 for observational studies that assessed the relationship of different chest CT manifestations and the severity of clinical presentation in COVID-19 infection. Risk of bias assessment was evaluated applying the Newcastle-Ottawa Scale. A random-effects model or fixed-effects model, as appropriately, were used to pool results. Heterogeneity was assessed using Forest plot, Cochran's Q test, and I2. Publication bias was assessed applying Egger's test. RESULTS: A total of 18 studies involving 3323 patients were included. Bronchial wall thickening (OR 11.64, 95% CI 1.81-74.66) was more likely to be associated with severe cases of COVID-19 infection, followed by crazy paving (OR 7.60, 95% CI 3.82-15.14), linear opacity (OR 3.27, 95% CI 1.10-9.70), and GGO (OR 1.37, 95% CI 1.08-1.73). However, there was no significant association between the presence of consolidation and severity of clinical presentation (OR 2.33, 95% CI 0.85-6.36). Considering the lesion distribution bilateral lung involvement was more frequently associated with severe clinical presentation (OR 3.44, 95% CI 1.74-6.79). CONCLUSIONS: Our meta-analysis of observational studies indicates some specific chest CT features are associated with clinical severity of COVID-19.