Bloodstream Infection Risk, Incidence, and Deaths for Hospitalized Patients during Coronavirus Disease Pandemic.

Hospital-acquired infections are emerging major concurrent conditions during the coronavirus disease (COVID-19) pandemic. We conducted a retrospective review of hospitalizations during March‒October 2020 of adults tested by reverse transcription PCR for severe acute respiratory syndrome coronavirus 2. We evaluated associations of COVID-19 diagnosis with risk for laboratory-confirmed bloodstream infections (LCBIs, primary outcome), time to LCBI, and risk for death by using logistic and competing risks regression with adjustment for relevant covariates. A total of 10,848 patients were included in the analysis: 918 (8.5%) were given a diagnosis of COVID-19, and 232 (2.1%) had LCBIs during their hospitalization. Of these patients, 58 (25%) were classified as having central line‒associated bloodstream infections. After adjusting for covariates, COVID-19‒positive status was associated with higher risk for LCBI and death. Reinforcement of infection control practices should be implemented in COVID-19 wards, and review of superiority and inferiority ranking methods by National Healthcare Safety Network criteria might be needed.

Operational impact of decreased turnaround times for Candida auris screening tests in a tertiary academic medical center.

Objective: Assess turnaround time (TAT) and cost-benefit of on-site C. auris screening and its impact on length of stay (LOS) and costs compared to reference laboratories. Design: Before-and-after retrospective cohort study. Setting: Large-tertiary medical center. Methods: We validated an on-site polymerase chain reaction-based testing platform for C. auris and retrospectively reviewed hospitalized adults who screened negative before and after platform implementation. We constructed multivariable models to assess the association of screening negative with hospital LOS/cost in the pre and postimplementation periods. We adjusted for confounders such as demographics and indwelling device use, and compared TATs for all samples tested. Results: The sensitivity and specificity of the testing platform were 100% and 98.11%, respectively, compared to send-out testing. The clinical cohort included 287 adults in the pre and 1,266 postimplementation period. The TAT was reduced by more than 2 days (3 (interquartile range (IQR): 2.0, 7.0) vs 0.42 (IQR: 0.24, 0.81), p < 0.001). Median LOS was significantly lower in the postimplementation period; however, this was no longer evident after adjustment. In relation to total cost, the time period had an effect of $6,965 (95% CI: -$481, $14,412); p = 0.067) on reducing the cost. The median adjusted total cost per patient was $7,045 (IQR: $3,805, $13,924) less in the post vs the preimplementation period. Conclusions: Our assessment did not find a statistically significant change in LOS, nevertheless, on-site testing was not cost-prohibitive for the institution. The value of on-site testing may be supported if an institutional C. auris reduction strategy emphasizes faster TATs.

Association of care by a non-medical intensive care unit provider team with outcomes of medically critically ill patients.

OBJECTIVE: To determine the association of boarding of critically ill medical patients on non-medical intensive care unit (ICU) provider teams with outcomes. DESIGN: A retrospective cohort study. SETTING: ICUs in a tertiary academic medical center. PATIENTS: Patients with medical critical illness. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: We compared outcomes for critically ill medical patients admitted to a non-medical specialty ICU team (April 1 - August 30, 2020) with those admitted to the medical ICU team (January 1, 2018 - March 31, 2020). The primary outcome was hospital mortality; secondary outcomes were hospital length of stay (LOS) and hospital disposition for survivors. Our cohort consisted of 1241 patients admitted to the medical ICU team and 230 admitted to non-medical ICU teams. Unadjusted hospital mortality (medical ICU, 38.8% vs non-medical ICU, 42.2%, p = 0.33) and hospital LOS (7.4 vs 7.4 days, p = 0.96) were similar between teams. Among survivors, more non-medical ICU team patients were discharged home (72.6% vs 82.0%, p = 0.024). After multivariable adjustment, we found no difference in mortality, LOS, or home discharge between teams. However, among hospital survivors, admission to a non-medical ICU team was associated with a longer LOS (regression coefficient [95% CI] for log-transformed hospital LOS: 0.23 [0.05,0.40], p = 0.022). Certain subgroups-patients aged 50-64 years (odds-ratio [95% CI]: 4.22 [1.84,9.65], p = 0.001), with ≤10 comorbidities (0-5: 2.78 (1.11,6.95], p = 0.029; 6-10: 6.61 [1.38,31.71], p = 0.018), without acute respiratory failure (1.97 [1.20,3.23], p = 0.008)-had higher mortality when admitted to non-medical ICU teams. CONCLUSIONS: We found no association between admission to non-medical ICU team and mortality for medically critically ill patients. However, survivors experienced longer hospital LOS when admitted to non-medical ICU teams. Middle-aged patients, those with low comorbidity burden, and those without respiratory failure had higher mortality when admitted to non-medical ICU teams.

Simulating COVID19 transmission from observed movement.

Current models of COVID-19 transmission predict infection from reported or assumed interactions. Here we leverage high-resolution observations of interaction to simulate infectious processes. Ultra-Wide Radio Frequency Identification (RFID) systems were employed to track the real-time physical movements and directional orientation of children and their teachers in 4 preschool classes over a total of 34 observations. An agent-based transmission model combined observed interaction patterns (individual distance and orientation) with CDC-published risk guidelines to estimate the transmission impact of an infected patient zero attending class on the proportion of overall infections, the average transmission rate, and the time lag to the appearance of symptomatic individuals. These metrics highlighted the prophylactic role of decreased classroom density and teacher vaccinations. Reduction of classroom density to half capacity was associated with an 18.2% drop in overall infection proportion while teacher vaccination receipt was associated with a 25.3% drop. Simulation results of classroom transmission dynamics may inform public policy in the face of COVID-19 and similar infectious threats.

Predictive Value of Sequential Organ Failure Assessment Score across Patients with and without COVID-19 Infection.

Rationale: Sequential organ failure assessment (SOFA) scores are commonly used in crisis standards of care policies to assist in resource allocation. The relative predictive value of SOFA by coronavirus disease (COVID-19) infection status and among racial and ethnic subgroups within patients infected with COVID-19 is unknown. Objectives: To evaluate the accuracy and calibration of SOFA in predicting hospital mortality by COVID-19 infection status and across racial and ethnic subgroups. Methods: We performed a retrospective cohort study of adult admissions to the University of Miami Hospital and Clinics inpatient wards (July 1, 2020-April 1, 2021). We primarily considered maximum SOFA within 48 hours of hospitalization. We assessed accuracy using the area under the receiver operating characteristic curve (AUROC) and created calibration belts. Considered subgroups were defined by COVID-19 infection status (by severe acute respiratory syndrome coronavirus 2 polymerase chain reaction testing) and prevalent racial and ethnic minorities. Comparisons across subgroups were made with DeLong testing for discriminative accuracy and visualization of calibration belts. Results: Our primary cohort consisted of 20,045 hospitalizations, of which 1,894 (9.5%) were COVID-19 positive. SOFA was similarly accurate for COVID-19-positive (AUROC, 0.835) and COVID-19-negative (AUROC, 0.810; P = 0.15) admissions but was slightly better calibrated in patients who were positive for COVID-19. For those with critical illness, maximum SOFA score accuracy at critical illness onset also did not differ by COVID-19 status (AUROC, COVID-19 positive vs. negative: intensive care unit admissions, 0.751 vs. 0.775; P = 0.46; mechanically ventilated, 0.713 vs. 0.792, P = 0.13), and calibration was again better for patients positive for COVID-19. Among patients with COVID-19, SOFA accuracy was similar between the non-Hispanic White population (AUROC, 0.894) and racial and ethnic minorities (Hispanic White population: AUROC, 0.824 [P vs. non-Hispanic White = 0.05]; non-Hispanic Black population: AUROC, 0.800 [P = 0.12]; Hispanic Black population: AUROC, 0.948 [P = 0.31]). This similar accuracy was also found for those without COVID-19 (non-Hispanic White population: AUROC, 0.829; Hispanic White population: AUROC, 0.811 [P = 0.37]; Hispanic Black population: AUROC, 0.828 [P = 0.97]; non-Hispanic Black population: AUROC, 0.867 [P = 0.46]). SOFA was well calibrated for all racial and ethnic groups with COVID-19 but estimated mortality more variably and performed less well across races and ethnicities without COVID-19. Conclusions: SOFA accuracy does not differ by COVID-19 status and is similar among racial and ethnic groups both with and without COVID-19. Calibration is better for COVID-19-infected patients and, among those without COVID-19, varies by race and ethnicity.

Association of Race and Ethnicity with COVID-19 Test Positivity and Hospitalization Is Mediated by Socioeconomic Factors.

Rationale: Black race and Hispanic ethnicity are associated with increased risks for coronavirus disease (COVID-19) infection and severity. It is purported that socioeconomic factors may drive this association, but data supporting this assertion are sparse. Objectives: To evaluate whether socioeconomic factors mediate the association of race/ethnicity with COVID-19 incidence and outcomes. Methods: We conducted a retrospective cohort study of adults tested for (cohort 1) or hospitalized with (cohort 2) COVID-19 between March 1, 2020, and July 23, 2020, at the University of Miami Hospital and Clinics. Our primary exposure was race/ethnicity. We considered socioeconomic factors as potential mediators of our exposure's association with outcomes. We used standard statistics to describe our cohorts and multivariable regression modeling to identify associations of race/ethnicity with our primary outcomes, one for each cohort, of test positivity (cohort 1) and hospital mortality (cohort 2). We performed a mediation analysis to see whether household income, population density, and household size mediated the association of race/ethnicity with outcomes. Results: Our cohorts included 15,473 patients tested (29.0% non-Hispanic White, 48.1% Hispanic White, 15.0% non-Hispanic Black, 1.7% Hispanic Black, and 1.6% other) and 295 patients hospitalized (9.2% non-Hispanic White, 56.9% Hispanic White, 21.4% non-Hispanic Black, 2.4% Hispanic Black, and 10.2% other). Among those tested, 1,256 patients (8.1%) tested positive, and, of the hospitalized patients, 47 (15.9%) died. After adjustment for demographics, race/ethnicity was associated with test positivity-odds-ratio (95% confidence interval [CI]) versus non-Hispanic White for Non-Hispanic Black: 3.21 (2.60-3.96), Hispanic White: 2.72 (2.28-3.26), and Hispanic Black: 3.55 (2.33-5.28). Population density mediated this association (percentage mediated, 17%; 95% CI, 11-31%), as did median income (27%; 95% CI, 18-52%) and household size (20%; 95% CI, 12-45%). There was no association between race/ethnicity and mortality, although this analysis was underpowered. Conclusions: Black race and Hispanic ethnicity are associated with an increased odds of COVID-19 positivity. This association is substantially mediated by socioeconomic factors.