ABSTRACT
BACKGROUND: Coinfection with bacteria, fungi, and respiratory viruses in SARS-CoV-2 is of particular importance due to the possibility of increased morbidity and mortality. In this meta-analysis, we calculated the prevalence of such coinfections. METHODS: Electronic databases were searched from 1 December 2019 to 31 March 2021. Effect sizes of prevalence were pooled with 95% confidence intervals (CIs). To minimize heterogeneity, we performed sub-group analyses. RESULTS: Of the 6189 papers that were identified, 72 articles were included in the systematic review (40 case series and 32 cohort studies) and 68 articles (38 case series and 30 cohort studies) were included in the meta-analysis. Of the 31,953 SARS-CoV-2 patients included in the meta-analysis, the overall pooled proportion who had a laboratory-confirmed bacterial infection was 15.9% (95% CI 13.6-18.2, n = 1940, 49 studies, I2 = 99%, p < 0.00001), while 3.7% (95% CI 2.6-4.8, n = 177, 16 studies, I2 = 93%, p < 0.00001) had fungal infections and 6.6% (95% CI 5.5-7.6, n = 737, 44 studies, I2 = 96%, p < 0.00001) had other respiratory viruses. SARS-CoV-2 patients in the ICU had higher co-infections compared to ICU and non-ICU patients as follows: bacterial (22.2%, 95% CI 16.1-28.4, I2 = 88% versus 14.8%, 95% CI 12.4-17.3, I2 = 99%), and fungal (9.6%, 95% CI 6.8-12.4, I2 = 74% versus 2.7%, 95% CI 0.0-3.8, I2 = 95%); however, there was an identical other respiratory viral co-infection proportion between all SARS-CoV-2 patients [(ICU and non-ICU) and the ICU only] (6.6%, 95% CI 0.0-11.3, I2 = 58% versus 6.6%, 95% CI 5.5-7.7, I2 = 96%). Funnel plots for possible publication bias for the pooled effect sizes of the prevalence of coinfections was asymmetrical on visual inspection, and Egger's tests confirmed asymmetry (p values < 0.05). CONCLUSION: Bacterial co-infection is relatively high in hospitalized patients with SARS-CoV-2, with little evidence of S. aureus playing a major role. Knowledge of the prevalence and type of co-infections in SARS-CoV-2 patients may have diagnostic and management implications.
ABSTRACT
BACKGROUND: COVID-19 is a worldwide pandemic and has placed significant demand for acute and critical care services on hospitals in many countries. OBJECTIVES: To determine the predictors of severe COVID-19 disease requiring admission to an ICU by comparing patients who were ICU admitted to non-ICU groups. METHODS: A cohort study was conducted for the laboratory-confirmed COVID-19 patients who were admitted to six Saudi Ministry of Health's hospitals in Alahsa, between March 1, 2020, and July 30, 2020, by reviewing patient's medical records retrospectively. RESULTS: This cohort included 1014 patients with an overall mean age of 47.2 ± 19.3 years and 582 (57%) were males. A total of 205 (20%) of the hospitalized patients were admitted to the ICU. Hypertension, diabetes and obesity were the most common comorbidities in all study patients (27.2, 19.9, and 9%, respectively). The most prevalent symptoms were cough (47.7%), shortness of breath (35.7%) and fever (34.3%). Compared with non-ICU group, ICU patients had older age (p ≤ 0.0005) and comprised a higher proportion of the current smokers and had higher respiratory rates (p ≤ 0.0005), and more percentage of body temperatures in the range of 37.3-38.0 °C (p ≥ 0.0005); and had more comorbidities including diabetes (p ≤ 0.0005), hypertension (p ≥ 0.0005), obesity (p = 0.048), and sickle cell disease (p = 0.039). There were significant differences between the non-ICU and ICU groups for fever, shortness of breath, cough, fatigue, vomiting, dizziness; elevated white blood cells, neutrophils, alanine aminotransferase and alkaline aminotransferase, lactate dehydrogenase, and ferritin, and decreased hemoglobin; and proportion of abnormal bilateral chest CT images (p < 0.05). Significant differences were also found for multiple treatments (p < 0.05). ICU patients group had a much higher mortality rate than those with non-ICU admission (p ≤ 0.0005). CONCLUSION: Identifying key clinical characteristics of COVID-19 that predict ICU admission and high mortality can be useful for frontline healthcare providers in making the right clinical decision under time-sensitive and resource-constricted environment.