Background The original Manchester Acute Coronary Syndromes model (MACS) 'rules in' and 'rules out' acute coronary syndromes (ACS) using high sensitivity cardiac troponin T (hs-cTnT) and heart-type fatty acid binding protein (H-FABP) measured at admission. The latter is not always available. We aimed to refine and validate MACS as Troponin-only Manchester Acute Coronary Syndromes (T-MACS), cutting down the biomarkers to just hs-cTnT. Methods We present secondary analyses from four prospective diagnostic cohort studies including patients presenting to the ED with suspected ACS. Data were collected and hs-cTnT measured on arrival. The primary outcome was ACS, defined as prevalent acute myocardial infarction (AMI) or incident death, AMI or coronary revascularisation within 30 days. T-MACS was built in one cohort (derivation set) and validated in three external cohorts (validation set). Results At the 'rule out' threshold, in the derivation set (n=703), T-MACS had 99.3% (95% CI 97.3% to 99.9%) negative predictive value (NPV) and 98.7% (95.3%-99.8%) sensitivity for ACS, 'ruling out' 37.7% patients (specificity 47.6%, positive predictive value (PPV) 34.0%). In the validation set (n=1459), T-MACS had 99.3% (98.3%-99.8%) NPV and 98.1% (95.2%-99.5%) sensitivity, 'ruling out' 40.4% (n=590) patients (specificity 47.0%, PPV 23.9%). T-MACS would 'rule in' 10.1% and 4.7% patients in the respective sets, of which 100.0% and 91.3% had ACS. C-statistics for the original and refined rules were similar (T-MACS 0.91 vs MACS 0.90 on validation). Conclusions T-MACS could 'rule out' ACS in 40% of patients, while 'ruling in' 5% at highest risk using a single hs-cTnT measurement on arrival. As a clinical decision aid, T-MACS could therefore help to conserve healthcare resources.
CITATION STYLE
Body, R., Carlton, E., Sperrin, M., Lewis, P. S., Burrows, G., Carley, S., … Mackway-Jones, K. (2017). Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid: Single biomarker re-derivation and external validation in three cohorts. Emergency Medicine Journal, 34(6), 349–356. https://doi.org/10.1136/emermed-2016-205983
Mendeley helps you to discover research relevant for your work.