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OBJECTIVE: To investigate temporal trends and outcomes associated with early antibiotic prescribing in patients hospitalized with COVID-19. DESIGN: Retrospective propensity-matched cohort study using the National COVID Cohort Collaborative (N3C) database. SETTING: Sixty-six health systems throughout the United States that were contributing to the N3C database. Centers that had fewer than 500 admissions in their dataset were excluded. PATIENTS: Patients hospitalized with COVID-19 were included. Patients were defined to have early antibiotic use if they received at least 3 calendar days of intravenous antibiotics within the first 5 days of admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 322,867 qualifying first hospitalizations, 43,089 patients received early empiric antibiotics. Antibiotic use declined across all centers in the data collection period, from March 2020 (23%) to June 2022 (9.6%). Average rates of early empiric antibiotic use (EEAU) also varied significantly between centers (deviance explained 7.33% vs 20.0%, p < 0.001). Antibiotic use decreased slightly by day 2 of hospitalization and was significantly reduced by day 5. Mechanical ventilation before day 2 (odds ratio [OR] 3.57; 95% CI, 3.42-3.72), extracorporeal membrane oxygenation before day 2 (OR 2.14; 95% CI, 1.75-2.61), and early vasopressor use (OR 1.85; 95% CI, 1.78-1.93) but not region of residence was associated with EEAU. After propensity matching, EEAU was associated with an increased risk for in-hospital mortality (OR 1.27; 95% CI, 1.23-1.33), prolonged mechanical ventilation (OR 1.65; 95% CI, 1.50-1.82), late broad-spectrum antibiotic exposure (OR 3.24; 95% CI, 2.99-3.52), and late Clostridium difficile infection (OR 1.60; 95% CI, 1.37-1.87). CONCLUSIONS: Although treatment of COVID-19 patients with empiric antibiotics has declined during the pandemic, the frequency of use remains high. There is significant inter-center variation in antibiotic prescribing practices and evidence of potential harm. Our findings are hypothesis-generating and future work should prospectively compare outcomes and adverse events. [ABSTRACT FROM AUTHOR]

Objective: To determine the association of new-onset atrial fibrillation with outcomes, including ICU length of stay and survival.Design: Retrospective cohort of ICU admissions. We found atrial fibrillation using automated detection (≥ 90 s in 30 min) and classed as new-onset if there was no prior diagnosis of atrial fibrillation. We identified determinants of new-onset atrial fibrillation and, using propensity matching, characterized its impact on outcomes.Setting: Tertiary care academic center.Patients: A total of 8,356 consecutive adult admissions to either the medical or surgical/trauma/burn ICU with available continuous electrocardiogram data.Interventions: None.Measurements and Main Results: From 74 patient-years of every 15-minute observations, we detected atrial fibrillation in 1,610 admissions (19%), with median burden less than 2%. Most atrial fibrillation was paroxysmal; less than 2% of admissions were always in atrial fibrillation. New-onset atrial fibrillation was subclinical or went undocumented in 626, or 8% of all ICU admissions. Advanced age, acute respiratory failure, and sepsis were the strongest predictors of new-onset atrial fibrillation. In propensity-adjusted regression analyses, clinical new-onset atrial fibrillation was associated with increased hospital mortality (odds ratio, 1.63; 95% CI, 1.01-2.63) and longer length of stay (2.25 d; CI, 0.58-3.92). New-onset atrial fibrillation was not associated with survival after hospital discharge (hazard ratio, 0.99; 95% CI, 0.76-1.28 and hazard ratio, 1.11; 95% CI, 0.67-1.83, respectively, for subclinical and clinical new-onset atrial fibrillation).Conclusions: Automated analysis of continuous electrocardiogram heart rate dynamics detects new-onset atrial fibrillation in many ICU patients. Though often transient and frequently unrecognized, new-onset atrial fibrillation is associated with poor hospital outcomes. [ABSTRACT FROM AUTHOR]

Objectives: Patients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness.Design: We report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation).Setting: University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center.Patients: Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data.Interventions: None.Measurements and Main Results: We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar.Conclusions: Subacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes. [ABSTRACT FROM AUTHOR]