Key result
MACS-ECG model detects NSTEMI with ~68% higher sensitivity and greater specificity than existing computer algorithms.
Why the study?
The MACS-ECG model was developed to provide an objective measure of ECG ischemia in suspected NSTEMI and to improve existing clinical decision aids, but its diagnostic performance compared to existing computer algorithms was unknown.
Does the MACS-ECG model improve diagnostic accuracy for NSTEMI compared to existing computer algorithms in adults with suspected ACS?
Observational
Yes
Does the MACS-ECG model improve diagnostic accuracy for NSTEMI compared to existing computer algorithms in adults with suspected ACS?
The MACS-ECG algorithm provides improved sensitivity and specificity over contemporary computer algorithms for identifying NSTEMI in emergency department patients with suspected ACS.
Background The Manchester Acute Coronary Syndromes ECG (MACS-ECG) model was derived and validated with the aim of providing an objective measure of ECG ischemia in the setting of suspected non-ST-elevation myocardial infarction. We wanted to produce an objective measure to improve existing clinical decision aids such as TMACS or the HEART score. To establish whether the MACS-ECG model warrants further study we compared the diagnostic performance against existing computer algorithms. The MACS-ECG model was derived in a single center cohort and combined a nuanced interpretation of repolarization abnormalities with novel signs of ischemia. These variables were selected using backward logistic regression using SPSS with the primary outcome being NSTEMI. This model was validated in a secondary analysis of the Bedside Evaluation of Sensitive Troponin study (BEST). We recruited patients from 17 Emergency Departments. We included adults with a suspected ACS. We excluded patients with STEMI or obvious non-cardiac cause. The primary outcome was NSTEMI, using the Fourth universal definition of myocardial infarction. In the validation study we also coded the ECG machines interpretation as acute ischemia, infarction or no statement. When the existing ECG algorithms produce an ‘acute ischemia’ statement, the sensitivity (Sn) was 13.3% (8.2–20.0%), specificity (Sp) 93.9% (92.1–95.5%), positive predictive value (PPV) 27.9% (19.1–39.0%) and negative predictive value (NPV) 86.0% (85.2 86.8%). An ‘infarction’ statement produced Sn 21.0% (14.6–28.6), Sp 88.3% (85.8–90.4%), PPV 24.0% (17.9–31.4%) and NPV 86.3% (85.3–87.3%). Combining infarction and ischemia produces the following diagnostic characteristics: Sn 34.3% (26.5–42.7%), Sp 82.2% (79.4–84.8%), PPV 25.4% (20.6–30.9%) and NPV 87.6% (86.2–88.9%). The MACS-ECG model was more specific and sensitive than existing computer algorithims. When it produced an ‘acute ischemia’ statement, the Sn was 22.4% (15.8–30.1%), Sp 95.2% (93.5–96.6%), PPV 45.1% (34.8–55.8%) and NPV 87.4% (86.4–88.4%).
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Morris et al. (2019) conducted an observational in Suspected acute coronary syndrome. MACS-ECG model vs. Existing computer algorithms was evaluated on NSTEMI, using the Fourth universal definition of myocardial infarction. The MACS-ECG model was more specific and sensitive than existing computer algorithms for diagnosing NSTEMI (sensitivity 22.4% vs 13.3%; specificity 95.2% vs 93.9%).
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