Asthma and COVID-19; different entities, same outcome: A meta-analysis of 107,983 patients.

Hussein, Mohammad H; Elshazli, Rami M; Attia, Abdallah S; Nguyen, Therese P; Aboueisha, Mohamed; Munshi, Ruhul; Toraih, Eman A; Fawzy, Manal S; Kandil, Emad

Objective: There are varying reports of the prevalence and effect of comorbid asthma in coronavirus disease-2019 (COVID-19) patients. We sought to conduct a meta-analysis comparing asthmatic and non-asthmatic patients to determine the clinical significance of pre-existing asthma in COVID-19 patients. Data Sources: Online databases PubMed, ScienceDirect, Web of Science, and Scopus, were searched up to July 15, 2020, for papers comparing asthma versus non-asthma COVID-19 patients. Study Selection: According to prespecified inclusion criteria, this analysis included eleven retrospective studies with 107,983 COVID-19 patients. Subgroup analysis was performed based on age groups. Results: The mean age of the patients was 59.9 years (95%CI =51.9-67.9). Across studies, the prevalence of asthma was 11.2% (95%CI: 9.1%-13.3%) among COVID-19 patients who attended the hospitals. Asthma patients were more likely to be younger (SMD=-0.36, 95%CI=-0.61 to -0.10, p = 0.005), and obese (OR =1.98, 95%CI =1.54-2.55, p < 0.001), there was no differential risk of hospitalization rate, ICU admission, or development of acute respiratory distress syndrome (ARDS) between asthmatic and non-asthmatic cohorts. However, asthmatic patients had increased risk of endotracheal intubation (RR =1.27, 95%CI =1.02-1.58, p = 0.030) especially patients aged <50 years (RR =6.68, 95%CI =1.76-11.13, p = 0.009). Despite this result, asthmatic patients had better recovery with a higher liability of being discharged and were less likely to die (RR =0.80, 95%CI =0.65-0.97, p = 0.026). Conclusion: To our knowledge, our meta-analysis is the largest to shed light on pre-existing asthma as a predictor of intubation in COVID-19, especially in young and obese patients. Identifying high-risk groups is crucial for designing more effective intervention plans and optimization of efficient resource allocation.