Beyond chest pain: Incremental value of other variables to identify patients for an early ECG

Bunney, Gabrielle; Sundaram, Vandana; Graber-Naidich, Anna; Miller, Katharine; Brown, Ian; McCoy, Allison B.; Freeze, Brian; Berger, David; Wright, Adam; Yiadom, Maame Yaa A. B.

doi:10.1016/j.ajem.2023.01.054

Cited by 2 publications

(1 citation statement)

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“…Prior work has shown that predictive modeling can augment current screening practices to shorten the time to diagnosis for more patients [ 13 ]. This was evidenced by higher sensitivity for identifying ACS and an increased proportion of those subsequently diagnosed with STEMI due to earlier screening without increasing the number of ECGs performed [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients

et al. 2023

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We compared four methods to screen emergency department (ED) patients for an early electrocardiogram (ECG) to diagnose ST-elevation myocardial infarction (STEMI) in a 5-year retrospective cohort through observed practice, objective application of screening protocol criteria, a predictive model, and a model augmenting human practice. We measured screening performance by sensitivity, missed acute coronary syndrome (ACS) and STEMI, and the number of ECGs required. Our cohort of 279,132 ED visits included 1397 patients who had a diagnosis of ACS. We found that screening by observed practice augmented with the model delivered the highest sensitivity for detecting ACS (92.9%, 95%CI: 91.4–94.2%) and showed little variation across sex, race, ethnicity, language, and age, demonstrating equity. Although it missed a few cases of ACS (7.6%) and STEMI (4.4%), it did require ECGs on an additional 11.1% of patients compared to current practice. Screening by protocol performed the worst, underdiagnosing young, Black, Native American, Alaskan or Hawaiian/Pacific Islander, and Hispanic patients. Thus, adding a predictive model to augment human practice improved the detection of ACS and STEMI and did so most equitably across the groups. Hence, combining human and model screening––rather than relying on either alone––may maximize ACS screening performance and equity.

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Section: Introductionmentioning

confidence: 99%