Diagnostic performance of a new ECG algorithm for reducing false positive cases in patients suspected acute coronary
Yama Fakhri1, Hedvig Andersson2, Richard E Gregg3
1Department of Cardiology, The Heart Centre, Rigshospitalet, Copenhagen, Denmark; Department of Medicine, Nykøbing Falster Hospital, Nykøbing F, Denmark; Department of Cardiology, Zealand University Hospital, Roskilde, Denmark.
Insights
A new high specificity automated electrocardiogram (ECG) algorithm reduced false positive ST-segment elevation myocardial infarction (STEMI) calls. However, this approach increased false negatives, meaning automated ECGs alone are insufficient for diagnosing myocardial infarction (MI).
Area of Science:
- Cardiology
- Medical Diagnostics
- Health Informatics
Background:
- Early diagnosis of ST-segment elevation myocardial infarction (STEMI) is critical for timely reperfusion therapy.
- Electrocardiogram (ECG) interpretation can be challenging due to confounding factors, leading to false positive STEMI activations.
- Automated ECG algorithms aim to improve diagnostic accuracy and reduce unnecessary procedures.
Purpose of the Study:
- To evaluate the performance of a standard automated ECG algorithm versus a high specificity setting for reducing false positive STEMI diagnoses.
- To assess the impact of the high specificity setting on both false positive and false negative STEMI identification.
Main Methods:
- Retrospective analysis of 2256 consecutive patients with prehospital ECGs triaged for acute coronary angiography.
- Comparison of STEMI diagnosis using a standard (STD) automated algorithm and a high specificity (HiSpec) setting against adjudicated discharge diagnoses.
- Evaluation of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for both algorithm settings.
Main Results:
- The high specificity (HiSpec) setting reduced false positive STEMI cases by 12.6% compared to the standard (STD) setting.
- However, the HiSpec setting also increased false negative STEMI results by 33%.
- While specificity improved with HiSpec, overall predictive values for STEMI identification remained moderate in this STEMI-predominant population.
Conclusions:
- Automated ECG algorithms with high specificity settings can decrease false positive STEMI alerts.
- The trade-off includes an increased rate of false negatives, potentially delaying critical diagnoses.
- Automated ECG interpretations should not be the sole basis for diagnosing acute myocardial infarction (AMI).
Background:
Early and correct diagnosis of ST-segment elevation myocardial infarction (STEMI) is crucial for providing timely reperfusion therapy. Patients with ischemic symptoms presenting with ST-segment elevation on the electrocardiogram (ECG) are preferably transported directly to a catheterization laboratory (Cath-lab) for primary percutaneous coronary intervention (PPCI). However, the ECG often contains confounding factors making the STEMI diagnosis challenging leading to false positive Cath-lab activation. The objective of this study was to test the performance of a standard automated algorithm against an additional high specificity setting developed for reducing the false positive STEMI calls.
Methods:
We included consecutive patients with an available digital prehospital ECG triaged directly to Cath-lab for acute coronary angiography between 2009 and 2012. An adjudicated discharge diagnosis of STEMI or no myocardial infarction (no-MI) was assigned for each patient. The new automatic algorithm contains a feature to reduce false positive STEMI interpretation. The STEMI performance with the standard setting (STD) and the high specificity setting (HiSpec) was tested against the adjudicated discharge diagnosis in a retrospective manner.
Results:
In total, 2256 patients with an available digital prehospital ECG (mean age 63 ± 13 years, male gender 71%) were included in the analysis. The discharge diagnosis of STEMI was assigned in 1885 (84%) patients. The STD identified 165 true negative and 1457 true positive (206 false positive and 428 false negative) cases (77.3%, 44.5%, 87.6% and 17.3% for sensitivity, specificity, PPV and NPV, respectively). The HiSpec identified 191 true negative and 1316 true positive (180 false positive and 569 false negative) cases (69.8%, 51.5%, 88.0% and 25.1% for sensitivity, specificity, PPV and NPV, respectively). From STD to HiSpec, false positive cases were reduced by 26 (12,6%), but false negative results were increased by 33%.
Conclusions:
Implementing an automated ECG algorithm with a high specificity setting was able to reduce the number of false positive STEMI cases. However, the predictive values for both positive and negative STEMI identification were moderate in this highly selected STEMI population. Finally, due the reduced sensitivity/increased false negatives, a negative AMI statement should not be solely based on the automated ECG statement.
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