Key result
A focused 4-question chest pain model accurately identifies ACS with ~93% sensitivity.
Why the study?
Chest pain misinterpretation is the leading cause of pre-hospital delay in acute coronary syndrome (ACS), warranting identification of characteristics associated with ACS.
Does a 4-question focused chest pain assessment model accurately detect acute coronary syndrome in patients presenting to the ER with chest pain?
Case-Control (n=164)
No
Does a 4-question focused chest pain assessment model accurately detect acute coronary syndrome in patients presenting to the ER with chest pain?
A simple 4-question chest pain assessment model demonstrated high sensitivity and specificity for detecting acute coronary syndrome, offering a potential tool for early triage and digital health interventions.
Answers are grounded in this paper and cite their sources.
Should not yet change ER chest pain triage; leaves open prospective validation of the 4-question model.
Background: Chest pain misinterpretation is the leading cause of pre-hospital delay in acute coronary syndrome (ACS). This study aims to identify and differentiate the chest pain characteristics associated with ACS. Methods: A total of 164 patients with a primary complaint of chest pain in the ER were included in the study. ACS diagnosis was made by a cardiologist based on the WHO criteria, and the patients were interviewed 48 hours after their admission. Furthermore, every question was analysed using the crosstabs method to obtain the odds ratio, and logistic regression analysis was applied to identify the model of focused questions on chest pain assessment. Results: Among the samples, 50% of them had an ACS. Four questions fitted the final model of ACS chest pain focused questions: 1) Did the chest pain occur at the left/middle chest? 2) Did the chest pain radiate to the back? 3) Was the chest pain provoked by activity and relieved by rest? 4) Was the chest pain provoked by food ingestion, positional changes, or breathing? This model has 92.7% sensitivity, 84.1% specificity, 85% positive predictive value (PPV), 86% negative predictive value (NPV), and 86% accuracy. After adjusting for gender and diabetes mellitus (DM), the final model has a significant increase in Nagelkerke R-square to 0.737 and Hosmer and Lemeshow test statistic of 0.639. Conclusion: Focused questions on 1) left/middle chest pain, 2) retrosternal chest pain, 3) exertional chest pain that is relieved by rest, and 4) chest pain from food ingestion, positional changes, or breathing triggering can be used to rule out ACS with high predictive value. The findings from this study can be used in health promotion materials and campaigns to improve public awareness regarding ACS symptoms. Additionally, digital health interventions to triage patients' suffering with chest pain can also be developed.
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Lukitasari et al. (2023) conducted a case-control in Acute Coronary Syndrome (n=164). 4-question focused chest pain assessment model was evaluated on Diagnostic accuracy of the focused chest pain assessment model for detecting acute coronary syndrome (AUC 0.925, 95% CI 0.885-0.964, p=<0.001). A focused 4-question chest pain assessment model accurately identified acute coronary syndrome with 92.7% sensitivity, 84.1% specificity, and an area under the curve of 0.925.
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