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The derivation and validation of the Manchester Acute Coronary Syndrome Electrocardiograph model for the identification of non-ST-elevation myocardial ischaemia in the Emergency Department
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The derivation and validation of the Manchester Acute Coronary Syndrome Electrocardiograph model for the identification of non-ST-elevation myocardial ischaemia in the Emergency Department
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The derivation and validation of the Manchester Acute Coronary Syndrome Electrocardiograph model for the identification of non-ST-elevation myocardial ischaemia in the Emergency Department
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Journal Article
The derivation and validation of the Manchester Acute Coronary Syndrome Electrocardiograph model for the identification of non-ST-elevation myocardial ischaemia in the Emergency Department
2022
Overview
AbstractObjectiveAcute coronary syndromes (ACS) are a diagnostic challenge for Emergency Medicine (EM) clinicians. To help clinicians assess patients with non-ST-elevation ACS (NSTEACS), clinical decision aids have been developed, combining clinical history, cardiac troponin and the electrocardiograph (ECG). These models ask the clinician to subjectively assess the ECG variable, introducing reliability issues. We set out to derive an ECG model that would provide an objective measure for ischaemia using non-ST-elevation myocardial infarction (NSTEMI) as the primary outcome. MethodsWe derived an ECG model in a retrospective Emergency Department cohort using logistic regression with a primary outcome of NSTEMI. All patients presented with signs or symptoms suggestive of an ACS. The model was validated in a multi-centre prospective Emergency Department cohort. ResultsDerivation included 1246 patients, 156 (12.5%) had the primary outcome; validation included 1139 patients, 170 (14.9%) had the primary outcome. Derivation demonstrated Sn 25.6% (95% CI 19.0–33.2), Sp 96.3% (95% CI 95.0–97.4), PPV 50.0% (95% CI 40.0–60.0) and NPV 90.1% (95% CI 89.2–90.9). Validation demonstrated Sn 23.5% (95% CI 17.4% to 30.6%), Sp 95.2% (95% CI 93.6% to 96.4%), PPV 46.0% (95% CI 36.6% to 55.7%) and NPV 87.6% (95% CI 86.7% to 88.5%). ConclusionWe have derived and validated an ECG model that is highly specific for NSTEMI and may be suitable for integration into existing clinical decision aids.
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