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.

Receiving hospital characteristics associated with survival after out-of-hospital cardiac arrest.

AIM: Survival after out-of-hospital cardiac arrest (OOHCA) varies between regions, but the contribution of different factors to this variability is unknown. This study examined whether survival to hospital discharge was related to receiving hospital characteristics, including bed number, capability of performing cardiac catheterization and hospital volume of OOHCA cases. MATERIAL AND METHODS: Prospective observational database of non-traumatic OOHCA assessed by emergency medical services was created in 8 US and 2 Canadian sites from December 1, 2005 to July 1, 2007. Subjects received hospital care after OOHCA, defined as either (1) arriving at hospital with pulses, or (2) arriving at hospital without pulses, but discharged or died > or =1 day later. RESULTS: A total of 4087 OOHCA subjects were treated at 254 hospitals, and 32% survived to hospital discharge. A majority of subjects (68%) were treated at 116 (46%) hospitals capable of cardiac catheterization. Unadjusted survival to discharge was greater in hospitals performing cardiac catheterization (34% vs. 27%, p=0.001), and in hospitals that received > or =40 patients/year compared to those that received <40 (37% vs. 30%, p=0.01). Survival was not associated with hospital bed number, teaching status or trauma center designation. Length of stay (LOS) for surviving subjects was shorter at hospitals performing cardiac catheterization (p<0.01). After adjusting for all variables, there were no independent associations between survival or LOS and hospital characteristics. CONCLUSIONS: Some subsets of hospitals displayed higher survival and shorter LOS for OOHCA subjects but there was no independent association between hospital characteristics and outcome.

Differences in triage thresholds for patients presenting with possible acute coronary syndromes: more than meets the eye.

BACKGROUND: Many studies have shown differences in cardiac care by racial/ethnic groups without accounting for institutional factors at the location of care. OBJECTIVE: Exploratory analysis of the effect of hospital funding status (public vs private) on emergency department (ED) triage decision making for patients with symptoms suggestive of acute coronary syndromes (ACSs) and on the likelihood of ED discharge for patients with confirmed ACS. STUDY DESIGN AND SETTING: Secondary analysis of data from a randomized controlled trial of 10,659 ED patients with possible ACS in five urban academic public and five private hospitals. The main outcome measures were the sensitivity and specificity of hospital admission for the presence of ACS at public and private hospitals and the adjusted odds of a patient with ACS not being hospitalized at public versus private hospitals. RESULTS: Of 10,659 ED patients, 1,856 had confirmed ACS. For patients with suspected ACS, triage decisions at private hospitals were considerably more sensitive (99 vs 96%; p<.001) but less specific (30 vs 48%; p<.001) than at public hospitals. The difference between hospital types persisted after adjustment for multiple patient-level and hospital-level characteristics. CONCLUSION: Significant differences in triage for patients with suspected ACS exist between public and private hospital EDs, even after adjustment for multiple patient demographic, clinical, and institutional factors. Further studies are needed to clarify the causes of the differences.