In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical...
| Publicado en: | Journal of the American Heart Association Vol. 10; no. 3; pp. 1 - 14 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/2/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148572011&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148572011 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20479980 FSQ9 jtl: Journal of the American Heart Association issn: 20479980 maglogo: Y pubinfo: dt: 2/2/2021 vid: 10 iid: 3 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 148572011 148572011 NLM33459029 148572011 10.1161/JAHA.120.017871 NLM33459029 148572011 ppf: 1 ppct: 13 formats: tig: atl: In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department. aug: au: Bouzid, Zeineb Faramand, Ziad Gregg, Richard E. Frisch, Stephanie O. Martin-Gill, Christian Saba, Samir Callaway, Clifton Sejdić, Ervin Al-Zaiti, Salah affil: Department of Electrical & Computer Engineering, Swanson School of Engineering, PA. sug: subj: Acute Coronary Syndrome Diagnosis Decision Support Systems, Clinical Emergency Service Statistics and Numerical Data Electrocardiography Methods Algorithms Human Prospective Studies Middle Age Female Acute Coronary Syndrome Physiopathology Male Predictive Value of Tests Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Funding Source Middle Aged: 45-64 years Female Male ab: Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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