Observational studyJournal of electrocardiology
Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome.
Observational study in Journal of electrocardiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Enhancing tertiary cardiology triage with vectorcardiographic features: a machine learning approach using real-world data.Clinics (Sao Paulo, Brazil) · 2026Article
- Sex-specific differences in the prognostic value of ischemic pre-hospital ECGs.Frontiers in cardiovascular medicine · 2026Article
- Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome.European heart journal · 2025Observational
- Electrocardiographic findings for predicting the left anterior descending artery chronic total occlusion in patients with inferior ST-segment elevation myocardial infarction.Scientific reports · 2024Article
- Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.Prehospital and disaster medicine · 2024Article
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation.BMC medical research methodology · 2023Article
- ECG Marker Evaluation for the Machine-Learning-Based Classification of Acute and Chronic Phases ofTropical medicine and infectious disease · 2023Article
- Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities.Journal of electrocardiologyArticle
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundNovel temporal-spatial features of the 12‑lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion.
methodsThis was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US. We obtained raw 10-s, 12‑lead ECGs (500 s/s, HeartStart MRx, Philips Healthcare) during prehospital transport and followed patients 30 days after the encounter to adjudicate clinical outcomes. A total of 557 global and lead-specific features of P-QRS-T waveform were harvested from the representative average beats. We used Recursive Feature Elimination and LASSO to identify 35/557, 29/557, and 51/557 most recurrent and important features for LAD, LCX, and RCA culprits, respectively. Using the union of these features, we built a random forest classifier with 10-fold cross-validation to predict the presence or absence of culprit lesions. We compared this model to the performance of a rule-based commercial proprietary software (Philips DXL ECG Algorithm).
resultsOur sample included 2400 patients (age 59 ± 16, 47% female, 41% Black, 10.7% culprit lesions). The area under the ROC curves of our random forest classifier was 0.85 ± 0.03 with sensitivity, specificity, and negative predictive value of 71.1%, 84.7%, and 96.1%. This outperformed the accuracy of the automated interpretation software of 37.2%, 95.6%, and 92.7%, respectively, and corresponded to a net reclassification improvement index of 23.6%. Metrics of ST80; Tpeak-Tend; spatial angle between QRS and T vectors; PCA ratio of STT waveform; T axis; and QRS waveform characteristics played a significant role in this incremental gain in performance.
conclusionsNovel computational features of the 12‑lead ECG can be used to build clinically relevant machine learning-based classifiers to detect culprit lesions, which has important clinical implications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.