Increased Body Mass Index and Metabolic Syndrome Are Associated with Poor Outcomes in SARS-CoV-2-Positive Emergency Department Patients.

Background: Increased body mass index (BMI) and metabolic syndrome (MetS) have been associated with adverse outcomes in viral syndromes. We sought to examine associations of increased BMI and MetS on several clinical outcomes in patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: The registry of suspected COVID-19 in emergency care (RECOVER) is an observational study of SARS-CoV-2-tested patients (n=27,051) across 155 United States emergency departments (EDs). We used multivariable logistic regression to test for associations of several predictor variables with various clinical outcomes. Results: We found that a BMI ≥30 kg/m2 increased odds of SARS-CoV-2 test positivity (odds ratio [OR], 1.30; 95% confidence interval [CI], 1.23-1.38), while MetS reduced odds of testing positive for SARS-CoV-2 (OR, 0.76; 95% CI, 0.71-0.82). Adjusted multivariable analysis found that MetS was significantly associated with the need for admission (OR, 2.11; 95% CI, 1.89-2.37), intensive care unit (ICU) care (OR, 1.58; 95% CI, 1.40-1.78), intubation (OR, 1.46; 95% CI, 1.28-1.66), mortality (OR, 1.29; 95% CI, 1.13-1.48), and venous thromboembolism (OR, 1.51; 95% CI, 1.07-2.13) in SARS-CoV-2-positive patients. Similarly, BMI ≥40 kg/m2 was significantly associated with ICU care (OR, 1.97; 95% CI, 1.65-2.35), intubation (OR, 2.69; 95% CI, 2.22-3.26), and mortality (OR, 1.50; 95% CI, 1.22-1.84). Conclusion: In this large nationwide sample of ED patients, we report a significant association of both high BMI and composite MetS with poor outcomes in SARS-CoV-2-positive patients. Findings suggest that composite MetS profile may be a more universal predictor of adverse disease outcomes, while the impact of BMI is more heavily modulated by SARS-CoV-2 status.

SARS-CoV-2 Positivity in Ambulatory Symptomatic Patients Is Not Associated With Increased Venous or Arterial Thrombotic Events in the Subsequent 30 Days.

BACKGROUND: COVID-19 has been associated with increased risk of thromboembolism in critically ill patients. OBJECTIVE: We sought to examine the association of SARS-CoV-2 test positivity and subsequent acute vascular thrombosis, including venous thromboembolism (VTE) or arterial thrombosis (AT), in a large nationwide registry of emergency department (ED) patients tested with a nucleic acid test for suspected SARS-CoV-2. METHODS: The RECOVER (Registry of Potential COVID-19 in Emergency Care) registry includes 155 EDs across the United States. We performed a retrospective cohort study to produce odds ratios (ORs) for COVID-19-positive vs. COVID-19-negative status as a predictor of 30-day VTE or AT, adjusting for age, sex, active cancer, intubation, hospital length of stay, and intensive care unit (ICU) care. RESULTS: Comparing 14,056 COVID-19-positive patients with 12,995 COVID-19-negative patients, the overall 30-day prevalence of VTE events was 1.4% vs. 1.3%, respectively (p = 0.44, χ2). Multivariable analysis identified that testing positive for SARS-CoV-2 status was negatively associated with both VTE (OR 0.76; 95% confidence interval [CI] 0.61-0.94) and AT (OR 0.51; 95% CI 0.32-0.80), whereas intubation, ICU care, and age 50 years or older were positively associated with both VTE and AT. CONCLUSIONS: In contrast to other reports, results from this large, hetereogenous national sample of ED patients tested for SARS-CoV-2, showed no association between vascular thrombosis and COVID-19 test positivity.

Predicting 30-day return hospital admissions in patients with COVID-19 discharged from the emergency department: A national retrospective cohort study.

OBJECTIVES: Identification of patients with coronavirus disease 2019 (COVID-19) at risk for deterioration after discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies patients with COVID-19 at risk for return and hospital admission within 30 days of ED discharge. METHODS: We performed a retrospective cohort study of discharged adult ED patients (n = 7529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the National Registry of Suspected COVID-19 in Emergency Care. The primary outcome was return hospital admission within 30 days. Models were developed using classification and regression tree (CART), gradient boosted machine (GBM), random forest (RF), and least absolute shrinkage and selection (LASSO) approaches. RESULTS: Among patients with COVID-19 discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine-learning (ML) models (GBM, RF, and LASSO) performed similarly. The RF model yielded a test area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI], 0.71-0.78), with a sensitivity of 0.46 (95% CI, 0.39-0.54) and a specificity of 0.84 (95% CI, 0.82-0.85). Predictive variables, including lowest oxygen saturation, temperature, or history of hypertension, diabetes, hyperlipidemia, or obesity, were common to all ML models. CONCLUSIONS: A predictive model identifying adult ED patients with COVID-19 at risk for return for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods (eg, GBM, RF, and LASSO) outperform the single-tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize the allocation of follow-up resources.

Association Between Baseline Use of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers and Death Among Patients Tested for COVID-19.

Angiotensin-converting enzyme inhibitor (ACEI) and angiotensin receptor blocker (ARB) drugs may modify risk associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, we assessed whether baseline therapy with ACEIs or ARBs was associated with lower mortality, respiratory failure (noninvasive ventilation or intubation), and renal failure (new renal replacement therapy) in SARS-CoV-2-positive patients. This retrospective registry-based observational cohort study used data from a national database of emergency department patients tested for SARS-CoV-2. Symptomatic emergency department patients were accrued from January to October 2020, across 197 hospitals in the United States. Multivariable analysis using logistic regression evaluated end points among SARS-CoV-2-positive cases, focusing on ACEIs/ARBs and adjusting for covariates. Model performance was evaluated using the c statistic for discrimination and Cox plotting for calibration. A total of 13 859 (99.9%) patients had known mortality status, of whom 2045 (14.8%) died. Respiratory failure occurred in 2485/13 880 (17.9%) and renal failure in 548/13 813 (4.0%) patients with available data. ACEI/ARB status was associated with a 25% decrease in mortality odds (odds ratio [OR], 0.75; 95%CI, 0.59-0.94; P = .011; c = .82). ACEIs/ARBs were not significantly associated with respiratory failure (OR, 0.89; 95%CI, 0.78-1.06; P = .206) or renal failure (OR, 0.75; 95%CI, 0.55-1.04; P = .083). Adjusting for covariates, baseline ACEI/ARB was associated with 25% lower mortality in SARS-CoV-2-positive patients. The potential mechanism for ACEI/ARB mortality modification requires further exploration.

Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021.

OBJECTIVES: Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. METHODS: Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. RESULTS: Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), negative likelihood ratio of 0.22 (0.19-0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). CONCLUSION: Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.

Derivation With Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients.

OBJECTIVES: The COVID-19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratifying patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID-19. METHODS: All patients greater than 18 years presenting to a single academic ED who were tested for COVID-19 during this index ED evaluation were included. Outcome was defined as the result of COVID-19 polymerase chain reaction (PCR) testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease-specific screening questions, and laboratory data. Three models were developed with a split-sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient-boosted decision tree methods. Model discrimination was evaluated comparing area under the receiver operator curve (AUC) and point statistics at a predefined threshold. RESULTS: A total of 1,026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% confidence interval [CI] = 0.84 to 0.94) when including four features: exposure history, temperature, white blood cell count (WBC), and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI = 0.79 to 0.92) and gradient boosting had an AUC of 0.85 (95% CI = 0.79 to 0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2-0.39) compared to 0.2 (0.14-0.28) for random forest and 0.22 (0.15-0.3) for the gradient-boosted method. CONCLUSION: The derived predictive models offer good discriminating capacity for COVID-19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and chest X-ray result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model-based approach to COVID-19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.

Multicenter registry of United States emergency department patients tested for SARS-CoV-2.

This paper summarizes the methodology for the registry of suspected COVID-19 in emergency care (RECOVER), a large clinical registry of patients from 155 United States (US) emergency departments (EDs) in 27 states tested for SARS-CoV-2 from March-September 2020. The initial goals are to derive and test: (1) a pretest probability instrument for prediction of SARS-CoV-2 test results, and from this instrument, a set of simple criteria to exclude COVID-19 (the COVID-19 Rule-Out Criteria-the CORC rule), and (2) a prognostic instrument for those with COVID-19. Patient eligibility included any ED patient tested for SARS-CoV-2 with a nasal or oropharyngeal swab. Abstracted clinical data included 204 variables representing the earliest manifestation of infection, including week of testing, demographics, symptoms, exposure risk, past medical history, test results, admission status, and outcomes 30 days later. In addition to the primary goals, the registry will provide a vital platform for characterizing the course, epidemiology, clinical features, and prognosis of patients tested for COVID-19 in the ED setting.