Background Bacterial infections may complicate viral pneumonias. Recent reports suggest that bacterial co-infection at time of presentation is uncommon in coronavirus disease 2019 (COVID-19); however, estimates were based on microbiology tests alone. We sought to develop and apply consensus definitions, incorporating clinical criteria to better understand the rate of co-infections and antibiotic use in COVID-19. Methods A total of 1016 adult patients admitted to 5 hospitals in the Johns Hopkins Health System between March 1, 2020, and May 31, 2020, with COVID-19 were evaluated. Adjudication of co-infection using definitions developed by a multidisciplinary team for this study was performed. Both respiratory and common nonrespiratory co-infections were assessed. The definition of bacterial community-acquired pneumonia (bCAP) included proven (clinical, laboratory, and radiographic criteria plus microbiologic diagnosis), probable (clinical, laboratory, and radiographic criteria without microbiologic diagnosis), and possible (not all clinical, laboratory, and radiographic criteria met) categories. Clinical characteristics and antimicrobial use were assessed in the context of the consensus definitions. Results Bacterial respiratory co-infections were infrequent (1.2%); 1 patient had proven bCAP, and 11 (1.1%) had probable bCAP. Two patients (0.2%) had viral respiratory co-infections. Although 69% of patients received antibiotics for pneumonia, the majority were stopped within 48 hours in patients with possible or no evidence of bCAP. The most common nonrespiratory infection was urinary tract infection (present in 3% of the cohort). Conclusions Using multidisciplinary consensus definitions, proven or probable bCAP was uncommon in adults hospitalized due to COVID-19, as were other nonrespiratory bacterial infections. Empiric antibiotic use was high, highlighting the need to enhance antibiotic stewardship in the treatment of viral pneumonias.

In a multicenter cohort of 963 adults hospitalized due to COVID-19, 5% had a proven hospital-acquired infection (HAI) and 21% had a proven/probable or possible HAI. Risk factors for proven/probable HAIs included intensive care unit admission, dexamethasone use, severe COVID-19, heart failure and antibiotic exposure upon admission.

In a cross-sectional evaluation of healthcare worker reuse of their own 3M N95 respirators, 83% (76/92) passed the seal check and the fit-test after a median of 40 repeated donnings. User seal-check failure correlated with fit-test failure, and it may help HCWs recognize when to appropriately discard a reused N95.

Background The burden of vancomycin-associated acute kidney injury (V-AKI) is unclear because it is not systematically monitored. The objective of this study was to develop and validate an electronic algorithm to identify cases of V-AKI, and to determine its incidence. Methods Adults and children admitted to one of five health system hospitals from January 2018 to December 2019 who received at least one dose of intravenous (IV) vancomycin were included. A subset of charts was reviewed using a V-AKI assessment framework to classify cases as unlikely, possible, or probable events. Based on review, an electronic algorithm was developed and then validated using another subset of charts. Percent agreement and kappa coefficients were calculated. Sensitivity and specificity were determined at various cutoffs, using chart review as the reference standard. For courses ≥48 hours, the incidence of possible or probable V-AKI events was assessed. Results The algorithm was developed using 494 cases and validated using 200 cases. The percent agreement between the electronic algorithm and chart review was 92.5% and the weighted kappa was 0.95. The electronic algorithm was 89.7% sensitive and 98.2% specific in detecting possible or probable V-AKI events. For the 11,073 courses of ≥48 hours of vancomycin among 8,963 patients, the incidence of possible or probable V-AKI events was 14.0%; the V-AKI incidence rate was 22.8 per 1,000 days of IV vancomycin therapy. Conclusions An electronic algorithm demonstrated substantial agreement with chart review and had excellent sensitivity and specificity in detecting possible or probable V-AKI events. The electronic algorithm may be useful for informing future interventions to reduce V-AKI.