Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome - 19/12/22
Abstract |
Study objective |
Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis.
Methods |
This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis.
Results |
Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation.
Conclusion |
In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.
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| Please see page 58 for the Editor’s Capsule Summary of this article. |
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| Supervising editor: Stephen Schenkel, MD, MPP. Specific detailed information about possible conflict of interest for individual editors is available at editors. |
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| Author contributions: ZB helped with data acquisition, analysis, and interpretation, statistical and machine learning modeling, technical support, and drafting of the manuscript. ZF helped with study concept and design, data acquisition and analysis, and drafting of the manuscript. CMG helped with study concept and design, data acquisition, administrative support and study supervision, and critical revision of the manuscript for important intellectual content. SMS helped with data analysis and statistical modeling, interpretation of results, and critical revision of the manuscript for important intellectual content. CWC helped with study concept and design, data acquisition, study supervision, and critical revision of the manuscript for important intellectual content. SS helped with study concept and design, study supervision, and critical revision of the manuscript for important intellectual content. RG helped with ECG data acquisition and preprocessing, data analysis and interpretation, technical support, and critical revision of the manuscript for important intellectual content. FB helped with ECG data preprocessing, technical support, and critical revision of the manuscript for important intellectual content. ES helped with study concept and design, data acquisition, analysis, and interpretation, statistical and machine learning modeling, technical support, and critical revision of the manuscript for important intellectual content. SSA helped with study concept and design, funding acquisition, administrative support and study supervision, data collection and analysis, statistical and machine learning modeling, data visualization, drafting the manuscript, and critical revision of the manuscript for important intellectual content. SSA takes responsibility for the paper as a whole. |
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| Authorship: All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. |
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| Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The authors have stated that no such relationships exist. Funded by National Institute of Health grant # R01HL137761. |
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| Trial registration number: NCT04237688 |
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| The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. |
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Vol 81 - N° 1
P. 57-69 - janvier 2023 Retour au numéro
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