Risk factors for hospital-acquired infection during the SARS-CoV-2 pandemic.

OBJECTIVE: To evaluate risk factors for hospital-acquired infection (HAI) in patients during the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic, including historical and concurrent cohorts. DESIGN: Retrospective cohort. SETTING: Three Missouri hospitals, data from 1st January 2017 to 30th September 2020. PARTICIPANTS: Patients aged ≥18 years and admitted for ≥48 h. METHODS: Univariate and multi-variate Cox proportional hazards models incorporating the competing risk of death were used to determine risk factors for HAI. A-priori sensitivity analyses were performed to assess the robustness of the urine-, blood- and respiratory-culture-based HAI definition. RESULTS: The cohort included 254,792 admissions, with 7147 (2.8%) HAIs (1661 blood, 3407 urine, 2626 respiratory). Patients with SARS-CoV-2 had increased risk of HAI (adjusted hazards ratio 1.65, 95% confidence interval 1.38-1.96), and SARS-CoV-2 infection was one of the strongest risk factors for development of HAI. Other risk factors for HAI included certain admitting services, chronic comorbidities, intensive care unit stay during index admission, extremes of body mass index, hospital, and selected medications. Factors associated with lower risk of HAI included year of admission (declined over the course of the study), admitting service and medications. Risk factors for HAI were similar in sensitivity analyses restricted to patients with diagnostic codes for pneumonia/upper respiratory infection and urinary tract infection. CONCLUSIONS: SARS-CoV-2 was associated with significantly increased risk of HAI.

Effect of Inhaled Epoprostenol on P/F Ratio in Covid Acute Respiratory Distress Syndrome

Rationale: COVID-19 infection is well known to cause Acute Respiratory Distress Syndrome (ARDS). Patients with COVID ARDS have been found to have higher mortality than those with ARDS from other causes. Patients with increased oxygenation and ventilation requirements are being placed on inhaled vasodilators in hopes of increasing oxygenation and decreasing mortality of this disease, however, no large study in COVID-related ARDS has been done in patients requiring noninvasive positive pressure ventilation and/or mechanical ventilation evaluating effects of pulmonary vasodilators. Here we evaluated whether inhaled epoprostenol does improve P/F ratios in patients with COVID-ARDS and whether that is associated with a mortality benefit. Methods: Eligible patients aged ≥18 years of age with a positive COVID-19 PCR/antigen test within 14 days or 7 days after hospital admission admitted to one of eleven hospitals in a large integrated healthcare system who received inhaled epoprostenol between 3/2020 and 9/2021 were added for evaluation. P/F ratios were calculated before and up to 4 hours after starting epoprostenol. In patients with only SpO2, nonlinear imputation of PaO2/FiO2 from SpO2/Fio2 was used to determine P/F ratios. Patients were stratified as responders to epoprostenol (defined as improvement in P/F ratio by 10%) or non responders. Results: In total, 209 patients met inclusion criteria. Of those, 116 were male, 93 female. Age ranged between 35-90 (Median age 63, (IQR:55- 75)) 142 patients were mechanically ventilated, 23 were on NPPV, 43 were on high flow oxygen. Baseline P/F in survivors and nonsurvivors was not significantly different (82 versus 71 P = 0.47). Similarly, the change in P/F between survivors and nonsurvivors after epoprostenol was not significantly different (24.1% versus 23.8%;P = 0.902). Conclusions: Although there was an improvement in P/F ratio in this patient population, overall, there was no mortality benefit seen. Further evaluation of patients in smaller sub-groups may demonstrate a patient population that could see mortality benefit with the addition of inhaled epoprostenol.

Predictors of hospital outcomes and clinical subphenotypes differ between COVID-19 and influenza pneumonia

Introduction: Pneumonia due to SARS-CoV-2 (Coronavirus Disease 2019, COVID-19) has frequently been compared to other viral pneumonias, including influenza. While some data suggest significant differences in biological responses, dissimilarities in the clinical course and characteristics between SARS-COV-2 and influenza pneumonia remain unknown. We evaluated differences in clinical predictors of outcomes and early clinical subphenotypes in COVID-19 and influenza pneumonia. Methods: We performed a retrospective cohort study of all patients hospitalized for > 24 hours, requiring oxygen support, at Barnes-Jewish Hospital with COVID-19 (March-July 2020) or influenza (Jan 2012-Dec 2018). In-hospital mortality or hospice discharge was the primary outcome. First, supervised machine learning classifier models (XGBoost) were trained using bootstrap replications of each viral cohort to predict the primary outcome. 28 candidate predictor variables among the most extreme vital signs and laboratory values within 24 hours of hospitalization were preselected, excluding highly correlated variables. We compared each model's internal discrimination to its performance in the alternate cohort and evaluated differences in variable importance between the two viral pneumonia models. Next, we evaluated differences in clinical subphenotypes in two ways: 1) a previously-validated algorithm to group patients into four distinct subphenotypes based on temperature trajectories within 72 hours of hospitalization;2) latent class analysis (LCA) to identify unmeasured subgroups within each viral cohort based on the predictor variables described above. In both analyses, we compared frequency of subphenotype membership and each subphenotype's primary outcome between viral cohorts. Results: We evaluated 321 unique hospitalizations with COVID-19 and 535 with influenza. The primary outcome was experienced in 23% and 9.5% of patients, respectively. Influenza predictor model discriminated outcomes worse in COVID-19 than on internal evaluation (Panel A), suggesting prognostic variables differ between the viral pneumonias. Only one of the top five contributory variables was shared between the two models (Panel B). Prevalences of temperature trajectory subphenotype also differed significantly between viral pneumonias. All COVID-19 temperature trajectory subphenotypes experienced the primary outcome more frequently than their influenza counterparts (Panel C). LCA identified two distinct classes in each cohort, with each viral pneumonia's minority class experiencing worse outcomes than the majority class. Of each model's top 5 classdefining variables, only 2 were shared (Panel D). Conclusions: COVID-19 and influenza pneumonia differ markedly in predictors of outcome and in clinical subphenotypes. These findings emphasize observable pathogen-specific differential responses in viral pneumonias and suggest that distinct management approaches should be investigated for these diseases. (Table Presented).