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

Abstract

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.

Inter‐rater reliability and prospective validation of a clinical prediction rule for SARS‐CoV‐2 infection

“…Among the 425 patients who had the criterion standard of an rtPCR test for SARS‐CoV‐2 performed on a same‐day nasopharyngeal swab, the CORC rule negative (i.e., a CORC score of 0 or less) had a sensitivity of 88%, specificity of 52%, and LR− of 0.24 (95% CI = 0.10–0.50), which is similar to the LR− found in the initial validation, 0.22 (95% CI = 0.19–0.26). 15 Thus, assuming this finding is further validated by others, the CORC rule negative can lead to a very low posterior probability (e.g., <1%), if the underlying prevalence of infection is <5%. The CORC rule outperformed clinician gestalt, both in terms of inter‐rater reliability (κ = 0.534, 95% CI = 0.437–0.632) and in terms of diagnostic performance, as evidenced by the diagnostic sensitivity of only 51% (95% CI = 35%–68%), and a LR− of 0.57 (95% CI = 0.39–0.74).…”

“…The CORC score originated from a national sample of 19,850 patients, randomly split in half for derivation then validation procedures. 15 , 22 Other clinical prediction rules that were restricted to use of data available at the bedside have proposed three to 11 criteria in the rule, and all were derived on retrospective samples, but none have yet been tested for inter‐rater reliability or diagnostic accuracy in an independently and retrospectively collected validation sample. 12 , 13 , 14 , 15 , 16 …”

“… 9 , 10 , 11 Additionally, only a handful of studies have accompanied these SARS‐CoV‐2 prediction rules with specific cutoffs that represent low‐probability scenarios for which practitioners can forego diagnostic testing. 12 , 13 , 14 , 15 , 16 Fewer studies have examined the diagnostic accuracy of physician gestalt for SARS‐CoV‐2 infection. 17 , 18 The studies that have been published on prediction have been criticized for high bias, and none have undergone full stages of validation.…”

“…However, far fewer studies have reported structured prediction rules designed to estimate the pretest probability of SARS‐CoV‐2 at the bedside without the use of laboratory or radiological imaging 9–11 . Additionally, only a handful of studies have accompanied these SARS‐CoV‐2 prediction rules with specific cutoffs that represent low‐probability scenarios for which practitioners can forego diagnostic testing 12–16 . Fewer studies have examined the diagnostic accuracy of physician gestalt for SARS‐CoV‐2 infection 17,18 .…”

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

“…The evidence indicates that MLDA domain is the most used when dealing with the ED improvement objective (n = 41 articles; 63.07%). [61] Multivariate binary logistic regression Brendish et al [49]; Esposito et al [59] Cox proportional hazards regression Gordon et al [66] Mixed-effect logistic regression García de Guadiana-Romualdo et al [63] Multivariate regression Kline et al [72] Stepwise forward logistic regression Carlile et al [51] Deep learning Plante et al [86] Gradient boosting Hybrid Shamout et al [89] Deep neural network, gradient boosting Balbi et al [47] Poisson regression, logistic regression Van Klaveren et al [95] Logistic regression with post hoc uniform shrinkage…”