Performance of Manchester Acute Coronary Syndromes decision rules in acute coronary syndrome: a systematic review and meta-analysis

Abstract

Background and importance Multiple decision-aiding models are available to help physicians identify acute coronary syndrome (ACS) and accelerate the decision-making process in emergency departments (EDs). Objective This study evaluates the diagnostic performance of the Manchester Acute Coronary Syndrome (MACS) rule and its derivations, enhancing the evidence for their clinical use. Design Systematic review and meta-analysis. Settings and participants Medline, Embase, Scopus, and Web of Science were searched from inception until October 2023 for studies including adult ED patients with suspected cardiac chest pain and inconclusive findings requiring ACS risk-stratification. Outcome measures and analysis The predictive value of MACS, Troponin-only MACS (T-MACS), or History and Electrocardiogram-only MACS (HE-MACS) decision aids for diagnosing acute myocardial infarction (AMI) and 30-day major adverse cardiac outcomes (MACEs) among patients admitted to ED with chest pain suspected of ACS. Overall sensitivity and specificity were synthesized using the ‘Diagma’ package in STATA statistical software. Applicability and risk of bias assessment were performed using the QUADAS-2 tool. Main results For AMI detection, MACS has a sensitivity of 99% [confidence interval (CI): 97–100], specificity of 19% (CI: 10–32), and AUC of 0.816 (CI: 0.720–0.885). T-MACS shows a sensitivity of 98% (CI: 98–99), specificity of 35% (CI: 29–42), and AUC of 0.859 (CI: 0.824–0.887). HE-MACS exhibits a sensitivity of 99% (CI: 98–100), specificity of 9% (CI: 3–21), and AUC of 0.787 (CI: 0.647–0.882). For MACE detection, MACS demonstrates a sensitivity of 98% (CI: 94–100), specificity of 22% (CI: 10–42), and AUC of 0.804 (CI: 0.659–0.897). T-MACS displays a sensitivity of 96% (CI: 94–98), specificity of 36% (CI: 30–43), and AUC of 0.792 (CI: 0.748–0.830). HE-MACS maintains a sensitivity of 99% (CI: 97–99), specificity of 10% (CI 6–16), and AUC of 0.713 (CI: 0.625–0.787). Conclusion Of all the MACS models, T-MACS displayed the highest overall accuracy due to its high sensitivity and significantly superior specificity. T-MACS exhibits very good diagnostic performance in predicting both AMI and MACE. This makes it a highly promising tool for managing patients with acute chest pain.