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There are only a few models developed for risk-stratifying COVID-19 patients with suspected pneumonia in the emergency department (ED). We aimed to develop and validate a model, the COVID-19 ED pneumonia mortality index (CoV-ED-PMI), for predicting mortality in this population. We retrospectively included adult COVID-19 patients who visited EDs of five study hospitals in Texas and who were diagnosed with suspected pneumonia between March and November 2020. The primary outcome was 1-month mortality after the index ED visit. In the derivation cohort, multivariable logistic regression was used to develop the CoV-ED-PMI model. In the chronologically split validation cohort, the discriminative performance of the CoV-ED-PMI was assessed by the area under the receiver operating characteristic curve (AUC) and compared with other existing models. A total of 1678 adult ED records were included for analysis. Of them, 180 patients sustained 1-month mortality. There were 1174 and 504 patients in the derivation and validation cohorts, respectively. Age, body mass index, chronic kidney disease, congestive heart failure, hepatitis, history of transplant, neutrophil-to-lymphocyte ratio, lactate dehydrogenase, and national early warning score were included in the CoV-ED-PMI. The model was validated with good discriminative performance (AUC: 0.83, 95% confidence interval [CI]: 0.79–0.87), which was significantly better than the CURB-65 (AUC: 0.74, 95% CI: 0.69–0.79, p-value

Introduction: Diverse coronavirus disease 2019 (COVID-19) mortalities have been reported but focused on identifying susceptible patients at risk of more severe disease or death. This study aims to investigate the mortality variations of COVID-19 from different hospital settings during different pandemic phases. Methods: We retrospectively included adult (≥18 years) patients who visited emergency departments (ED) of five hospitals in the state of Texas and who were diagnosed with COVID-19 between March–November 2020. The included hospitals were dichotomized into urban and suburban based on their geographic location. The primary outcome was mortality that occurred either during hospital admission or within 30 days after the index ED visit. We used multivariable logistic regression to investigate the associations between independent variables and outcome. Generalized additive models were employed to explore the mortality variation during different pandemic phases. Results: A total of 1,788 adult patients who tested positive for COVID-19 were included in the study. The median patient age was 54.6 years, and 897 (50%) patients were male. Urban hospitals saw approximately 59.5% of the total patients. A total of 197 patients died after the index ED visit. The analysis indicated visits to the urban hospitals (odds ratio [OR] 2.14, 95% confidence interval [CI], 1.41, 3.23), from March to April (OR 2.04, 95% CI, 1.08, 3.86), and from August to November (OR 2.15, 95% CI, 1.37, 3.38) were positively associated with mortality. Conclusion: Visits to the urban hospitals were associated with a higher risk of mortality in patients with COVID-19 when compared to visits to the suburban hospitals. The mortality risk rebounded and showed significant difference between urban and suburban hospitals since August 2020. Optimal allocation of medical resources may be necessary to bridge this gap in the foreseeable future.

Objective To evaluate the accuracy of the Brugada algorithm for analysis of wide-complex tachycardia (WCT) when applied by board-certified emergency physicians and board-certified cardiologists. Methods A database consisting of 157 electrocardiograms of WCTs were evaluated in a blinded fashion using the Brugada criteria to determine the presence of ventricular tachycardia (VT) or supraventricular tachycardia with aberrancy. These results were then compared with the electrophysiologically proven diagnosis for each tracing. Sensitivity and specificity of the Brugada criteria for diagnosis of VT were calculated. Two board-certified emergency physicians and two board-certified cardiologists analyzed each tracing, and interobserver agreement was determined using the kappa statistic. Results Sensitivity and specificity for the determination of VT using the Brugada algorithm were 85% [95% confidence interval (95% CI) = 79% to 91%] and 60% (95% CI = 43% to 78%) for cardiologist 1 (C 1) and 91% (95% CI = 86% to 96%) and 55% (95% CI = 37% to 72%) for C 2. Emergency physician (EP 1) achieved a sensitivity of 83% (95% CI = 78% to 91%) and a specificity of 43% (95% CI = 25% to 59%), while EP 2 attained 79% (95% CI = 73% to 87%) and 70% (95% CI = 51% to 84%), respectively. The original authors achieved a sensitivity of 98.7% and specificity of 96.5% when determining VT in their study population. Interobserver agreement for the emergency physicians and the cardiologists in determining VT was 82% and 81%, respectively. Conclusions Neither the emergency physicians nor the cardiologists were able to achieve a sensitivity or specificity as high as that reported by the original investigators when using the Brugada algorithm to determine the presence of VT.