Background A mismatch between a widespread use of broad-spectrum antibiotic agents and a low prevalence of reported bacterial co-infections in patients with SARS-CoV-2 infections has been observed. Herein, we sought to characterize and compare bacterial co-infections at admission in hospitalized patients with SARS-CoV-2, influenza or respiratory syncytial virus (RSV) positive community-acquired pneumonia (CAP). Methods A retrospective cohort study of bacterial co-infections at admission in SARS-CoV-2, influenza or RSV-positive adult patients with CAP admitted to Karolinska University Hospital in Stockholm, Sweden, from year 2011 to 2020. The prevalence of bacterial co-infections was investigated and compared between the three virus groups. In each virus group, length of stay, ICU-admission and 30-day mortality was compared in patients with and without bacterial co-infection, adjusting for age, sex and co-morbidities. In the SARS-CoV-2 group, risk factors for bacterial co-infection, were assessed using logistic regression models and creation of two scoring systems based on disease severity, age, co-morbidities and inflammatory markers with assessment of concordance statistics. Results Compared to influenza and RSV, the bacterial co-infection testing frequency in SARS-CoV-2 was lower for all included test modalities. Four percent [46/1243 (95% CI 3–5)] of all SARS-CoV-2 patients had a bacterial co-infection at admission, whereas the proportion was 27% [209/775 (95% CI 24–30)] and 29% [69/242 (95% CI 23–35)] in influenza and RSV, respectively. S. pneumoniae and S. aureus constituted the most common bacterial findings for all three virus groups. Comparing SARS-CoV-2 positive patients with and without bacterial co-infection at admission, a relevant association could not be demonstrated nor excluded with regards to risk of ICU-admission (aHR 1.53, 95% CI 0.87–2.69) or 30-day mortality (aHR 1.28, 95% CI 0.66–2.46) in adjusted analyses. Bacterial co-infection was associated with increased inflammatory markers, but the diagnostic accuracy was not substantially different in a scoring system based on disease severity, age, co-morbidities and inflammatory parameters [C statistic 0.66 (95% CI 0.59–0.74)], compared to using disease severity, age and co-morbidities only [C statistic 0.63 (95% CI 0.56–0.70)]. Conclusions The prevalence of bacterial co-infections was significantly lower in patients with community-acquired SARS-CoV-2 positive pneumonia as compared to influenza and RSV positive pneumonia.

BackgroundAn understanding of differences in clinical phenotypes and outcomes COVID-19 compared with other respiratory viral infections is important to optimise the management of patients and plan healthcare. Herein we sought to investigate such differences in patients positive for SARS-CoV-2 compared with influenza, respiratory syncytial virus (RSV) and other respiratory viruses.MethodsWe performed a retrospective cohort study of hospitalised adults and children (≤15 years) who tested positive for SARS-CoV-2, influenza virus A/B, RSV, rhinovirus, enterovirus, parainfluenza viruses, metapneumovirus, seasonal coronaviruses, adenovirus or bocavirus in a respiratory sample at admission between 2011 and 2020.ResultsA total of 6321 adult (1721 SARS-CoV-2) and 6379 paediatric (101 SARS-CoV-2) healthcare episodes were included in the study. In adults, SARS-CoV-2 positivity was independently associated with younger age, male sex, overweight/obesity, diabetes and hypertension, tachypnoea as well as better haemodynamic measurements, white cell count, platelet count and creatinine values. Furthermore, SARS-CoV-2 was associated with higher 30-day mortality as compared with influenza (adjusted HR (aHR) 4.43, 95% CI 3.51 to 5.59), RSV (aHR 3.81, 95% CI 2.72 to 5.34) and other respiratory viruses (aHR 3.46, 95% CI 2.61 to 4.60), as well as higher 90-day mortality, ICU admission, ICU mortality and pulmonary embolism in adults. In children, patients with SARS-CoV-2 were older and had lower prevalence of chronic cardiac and respiratory diseases compared with other viruses.ConclusionsSARS-CoV-2 is associated with more severe outcomes compared with other respiratory viruses, and although associated with specific patient and clinical characteristics at admission, a substantial overlap precludes discrimination based on these characteristics.

Objective To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. Design Two stage individual participant data meta-analysis. Setting Secondary and tertiary care. Participants 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. Data sources Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ , and through PROSPERO, reference checking, and expert knowledge. Model selection and eligibility criteria Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. Methods Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. Main outcome measures 30 day mortality or in-hospital mortality. Results Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al’s model (0.96, 0.59 to 1.55, 0.21 to 4.28). Conclusion The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.