T-MACS

Body, R., Carlton, E., Sperrin, M., Lewis, P.S., Burrows, G., Carley, S., McDowell, G., Buchan, I., Greaves, K. & Mackway-Jones, K. (2017). Troponin only Manchester Acute Coronary Syndromes (T-MACS) decision aid: single biomarker re-derivation and external validation in three cohorts. Emerg Med J 34 pp. 349-356

Background

The original Manchester Acute Coronary Syndromes model (MACS) ‘rules in’ and ‘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 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 Emergency Department (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 revascularization 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 97.3–99.9%) 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 34.0%). In the validation set (n=1,459), 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%, positive predictive value 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 hscTnT measurement on arrival. As a clinical decision aid, T-MACS could therefore help to conserve healthcare resources.

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