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INTRODUCTION: The appropriate use of empiric antibiotics for patients with severe COVID-19 presents a clinical challenge. Bacterial coinfection can be difficult to exclude, sometimes resulting in empiric antibiotic therapy. However, antibiotics alter the respiratory tract microbiome and these changes in the lung microbiome have been associated with prolonged ARDS in COVID-19. We hypothesized that early antibiotic use increase the risk of prolonged mechanical ventilation in patients hospitalized with COVID-19. METHOD(S): We used the National Covid Cohort Collaborative (N3C) to identify a retrospective cohort of patients admitted between March 2020 and May 2022 with a positive COVID-19 PCR or antigen test 15 days prior or within 48 hours of admission. We collected demographics, Charlson comorbidity index, month of hospitalization, antibiotics received, surgical procedures, details of mechanical ventilation, and diagnoses. We defined early empiric antibiotic use (EEAU) as administration of IV antibiotics for at least three calendar days before the sixth hospital day. Prolonged mechanical ventilation was defined as 14 consecutive days of mechanical ventilation. Our primary analysis used logistic regression after propensity score matching (PSM) with multiple imputation via chained equations for missing data. Sensitivity analyses included varying the required days of antibiotic exposure, using PSM with complete cases only, and using inverse probability of treatment weighting. RESULT(S): Our final cohort included 283,314 admissions. Prolonged mechanical ventilation and EEAU was observed in 1.4% and 13.9% of cases, respectively. In the unadjusted cohort, patients who received EEAU were more likely to be older, obese, and have more comorbidities. These patients were also more likely to have had mechanical ventilation, ECMO, major surgery, or a traumatic diagnosis during the first days of their hospitalization. After PSM, the standardized mean difference for all variables was less than 0.05. Early antibiotic use was associated with an increased risk of prolonged mechanical ventilation (OR 1.86, 95% CI 1.71 - 2.03). This finding was robust to all approaches in our sensitivity analysis. CONCLUSION(S): In our retrospective cohort, EEAU is independently associated with increased risk of prolonged mechanical ventilation.
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INTRODUCTION: The appropriate use of empiric antibiotics is a clinical challenge for patients with severe COVID-19. Early in the pandemic, there was concern that bacterial coinfection would influence morbidity and mortality. This concern often led to treating patients empirically with antibiotics. Fortunately, early data from the COVID-19 pandemic suggests bacterial coinfection is uncommon. However, there has been little published data on the antibiotic prescribing practices over the course of the pandemic. This study aims to investigate the inter-center variation and temporal trends of early antibiotic prescribing in patients hospitalized with COVID-19. METHOD(S): We performed a retrospective analysis using the National COVID Cohort Collaborative database. We identified patients admitted between March 2020 and December 2021 who had a positive COVID-19 PCR or antigen test 15 days prior or within 48 hours of admission. Age at time of COVID-19 diagnosis, gender, race/ethnicity, Charlson comorbidity index, the month of hospitalization, antibiotics received, labs at the time of hospital admission, and center identifier were collected. A chi-square test was used for categorical data and Wilcoxon rank-sum test for continuous data. Mixed effects logistic regression was used to evaluate predictors of early empiric antibiotic use. RESULT(S): Of 280,601 qualifying first hospitalizations, 30,469 patients received early empiric antibiotics. Antibiotic use declined across all centers over time from the first month (23%) to the last month in (8.1%) in the data collection period (p < 0.01). Antibiotic use decreased slightly by day 2 of hospitalization and was significantly reduced by day 5. Mechanical ventilation before day 2 (OR 2.25, 95% CI 2.14 - 2.36) and ECMO before day 2 (OR 1.60, 95% CI 1.25 - 2.05) but not region of residence was associated with early empiric antibiotic use. CONCLUSION(S): Although treatment of COVID-19 patients with empiric intravenous antibiotics has declined during the pandemic, the frequency of use remains higher than the reported incidence of bacterial superinfection. There is significant inter-center variation in antibiotic prescribing practices. Future research should focus on comparing outcomes and adverse events among COVID-19 patients treated with and without empiric antibiotics.
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The COVID-19 pandemic is an ongoing pandemic of coronavirus disease since 2019. Millions of cases and deaths attributed to it have been confirmed in the world. So far the detection of COVID-19 heavily relies on the specialized tests (e.g., based on saliva or respiratory swabs). Some approaches use smart devices (e.g., Whoop) for coronavirus infection detection using respiratory rate. Machine learning (ML) techniques have become a promising approach for the coronavirus infection detection. Therefore, in this paper, we introduce a machine learning based COVID infection predictor. We measure the prediction accuracy of five ML models. We use Chi-square test and knowledge-based manual feature selection to select important features for prediction to reduce prediction time overhead without compromising prediction accuracy. We also study the accuracy with different input features (those that can be measured by medical devices and by smart devices) and find that removing some features has no or slight influence on the prediction accuracy. Since insufficient or unbalanced training data decreases the prediction accuracy, we further propose a Generative Adversarial Network (GAN) ML based predictor that produces synthetic data (close to real data) for ML training. Our extensive experiments show the effectiveness of our methods in improving the detection accuracy. Our study results can provide guidance on developing the coronavirus infection predictors based on different data sources and devices. We open sourced our code in GitHub. © 2021 IEEE.
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Background: In response to the COVID-19 pandemic, a dedicated intensive care unit for patients infected with SARS-CoV-2 was created at our institution. We noticed a marked increase in the number of blood cultures positive for coagulase-negative Staphylococcus species (CoNS) that highlights unique challenges that arise with the creation of new units and workflows. Methods: We reviewed all blood culture results from the COVID-19 intensive care unit (CoVICU) from April 15 to May 29. We reviewed all blood cultures taken from the oncology ward, medical intensive care unit (MICU), and emergency department (ED) for the same time frame as a comparison. We calculated contamination rates, using the clinical microbiology laboratory criteria for possible contaminants based on species and number of positive blood cultures. Results: There were 324 total blood cultures collected from the CoVICU with 27/324 (8.3%) positive for organisms deemed contaminant, 10/324 (3.1%) were positive considered bloodstream infections (BSI);the ratio of BSI:contaminant was 1:2.7. For the MICU, ED, and oncology units contamination rates were 2/197 (1%), 33/747 (4.4%), and 2/334 (0.6%), respectively;and the ratio of BSI:contaminant was 5:1, 2.2:1, and 17.5:1, respectively. There was a significant relationship between contamination rates and unit, X2(3, N = 1602) = 30.85, p < 0.001. Conclusion: Upon investigation, peripheral blood draw kits were not stocked in the CoVICU. Additionally, certain components of standard work for blood culture collection (e.g. glove exchange) could not be performed per usual practice due to isolation precautions. Peripheral blood draws were routinely performed by nurses in CoVICU and MICU while phlebotomy performed these in other comparison units. We suspect that lack of availability of blood draw kits and disruption of typical workflow in isolation rooms contributed to an unusually high number of contaminated blood cultures among patients admitted to the CoVICU. Notably, the CoVICU and MICU providers were the same pool of caregivers, further supporting a process issue related to isolation precautions. Institutions should be aware of the need for extra attention to supply chain management and examination of disruption to standard work that arise in the management of COVID-19 patients.