Enhancing predictive accuracy of the 13-item Acute Coronary Syndrome checklist: a novel approach to improving risk assessment and diagnosis.

Abstract

Objectives: This study aimed to evaluate the discriminatory capacity of the 13-Item ACS checklist and improve the accuracy of ACS diagnosis through the application of weighted regression analysis.

Materials and methods: This predictive correlation study enrolled 300 patients admitted to Emergency Department between February 2021 and January 2022. The ACS checklist was administered upon initial triage, followed by patient tracking over a one-month hospitalisation period, capturing ACS diagnoses. Data analysis employed STATA 17 and MEDCALC 20.0.13 software.

Results: Findings indicated that patients with sweating and shortness of breath symptoms had a heightened likelihood of true ACS diagnosis by 14% and 11%, respectively, compared to those without ACS (p = 0.005 and 0.019). Conversely, palpitations were associated with a 20% decreased likelihood of authentic ACS diagnosis (p < 0.001). Integration of significant regression coefficients - palpitation severity (-21), sweating severity (13.7), and shortness of breath severity (11) demonstrated significant discriminatory enhancements in the checklists. The weighted 13-item ACS checklist surpassed the unweighted version's performance, yielding superior discriminatory power for ACS diagnosis (p < 0.001 and p = 0.089). The weighted checklist elevated the AUC score from 55% to 70%.

Conclusions: Incorporating weighted factors - shortness of breath severity, sweating severity, and palpitations severity - into the checklist notably enhanced ACS identification. However, it's important to note that this tool, while showing promise, is not intended to serve as a standalone diagnostic tool for ACS. Instead, this tool has the potential to enhance risk assessment and aid in clinical decision-making.