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Volume 13
Issue 12
10.3390/diagnostics13122053
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Article

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

by
Gabrielle Bunney
1,
Sean M. Bloos
1,2,
Anna Graber-Naidich
3,
Melissa A. Pasao
1,
Rana Kabeer
1,
David Kim
1,
Kate Miller
3 and
Maame Yaa A. B. Yiadom
1,*
1
Department of Emergency Medicine, Stanford University, Palo Alto, CA 94304, USA
2
Tulane University School of Medicine, New Orleans, LA 70112, USA
3
Quantitative Sciences Unit, Stanford University, Palo Alto, CA 94304, USA
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(12), 2053; https://doi.org/10.3390/diagnostics13122053
Submission received: 17 April 2023 / Revised: 22 May 2023 / Accepted: 6 June 2023 / Published: 14 June 2023
(This article belongs to the Special Issue Cardiovascular Diseases: Diagnosis and Management)
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Abstract

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.
Keywords: acute coronary syndrome; ACS; screening; diagnosis; emergency; risk; prediction; equity; sensitivity; specificity; electrocardiogram; ECG; EKG; augment; predictive model acute coronary syndrome; ACS; screening; diagnosis; emergency; risk; prediction; equity; sensitivity; specificity; electrocardiogram; ECG; EKG; augment; predictive model

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MDPI and ACS Style

Bunney, G.; Bloos, S.M.; Graber-Naidich, A.; Pasao, M.A.; Kabeer, R.; Kim, D.; Miller, K.; Yiadom, M.Y.A.B. Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients. Diagnostics 2023, 13, 2053. https://doi.org/10.3390/diagnostics13122053

AMA Style

Bunney G, Bloos SM, Graber-Naidich A, Pasao MA, Kabeer R, Kim D, Miller K, Yiadom MYAB. Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients. Diagnostics. 2023; 13(12):2053. https://doi.org/10.3390/diagnostics13122053

Chicago/Turabian Style

Bunney, Gabrielle, Sean M. Bloos, Anna Graber-Naidich, Melissa A. Pasao, Rana Kabeer, David Kim, Kate Miller, and Maame Yaa A. B. Yiadom. 2023. "Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients" Diagnostics 13, no. 12: 2053. https://doi.org/10.3390/diagnostics13122053

APA Style

Bunney, G., Bloos, S. M., Graber-Naidich, A., Pasao, M. A., Kabeer, R., Kim, D., Miller, K., & Yiadom, M. Y. A. B. (2023). Maximizing Equity in Acute Coronary Syndrome Screening across Sociodemographic Characteristics of Patients. Diagnostics, 13(12), 2053. https://doi.org/10.3390/diagnostics13122053

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