Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome.
Background: Novel 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 detecti...
| Published in: | Journal of Electrocardiology Vol. 69; pp. 31 - 38 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | research Journal Article |
| Published: |
W B Saunders
2021 Supplement
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153954904&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153954904 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00220736 1276 jtl: Journal of Electrocardiology issn: 00220736 maglogo: N pubinfo: dt: 2021 Supplement vid: 69 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 153954904 153954904 NLM34332752 153954904 10.1016/j.jelectrocard.2021.07.012 NLM34332752 153954904 ppf: 31 ppct: 7 formats: tig: atl: Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome. aug: au: Bouzid, Zeineb Faramand, Ziad Gregg, Richard E. Helman, Stephanie Martin-Gill, Christian Saba, Samir Callaway, Clifton Sejdić, Ervin Al-Zaiti, Salah affil: Department of Electrical & Computer Engineering, PA, USA sug: subj: Acute Coronary Syndrome Diagnosis Middle Age Electrocardiography Adult Male Algorithms Prospective Studies Female Aged Human Middle Aged: 45-64 years Adult: 19-44 years Aged: 65+ years Male Female ab: Background: Novel 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.Methods: This 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).Results: Our 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.Conclusions: Novel 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|