Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.
Background: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utility of pre‐ and post‐TAVR ECG data and compare mach...
| Publicado en: | Pacing & Clinical Electrophysiology Vol. 44; no. 2; pp. 334 - 341 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2021
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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=148723249&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148723249 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01478389 4F8 jtl: Pacing & Clinical Electrophysiology issn: 01478389 maglogo: Y pubinfo: dt: Feb2021 vid: 44 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 148723249 148330635 148723249 148723249 10.1111/pace.14163 148723249 ppf: 334 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement. aug: au: Truong, Vien T. Beyerbach, Daniel Mazur, Wojciech Wigle, Matthew Bateman, Emma Pallerla, Akhil Ngo, Tam N.M. Shreenivas, Satya Tretter, Justin T. Palmer, Cassady Kereiakes, Dean J. Chung, Eugene S. affil: The Christ Hospital Health Network and The Lindner Research Center, Cincinnati Ohio,, USA sug: subj: Machine Learning Methods Transcatheter Aortic Valve Implantation Pacemaker, Artificial Risk Assessment Methods Human Electrocardiography Logistic Regression Aortic Valve Stenosis Surgery Algorithms Aged Aged, 80 and Over Male Female Aged: 65+ years Aged, 80 & over Male Female ab: Background: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utility of pre‐ and post‐TAVR ECG data and compare machine learning approaches with traditional logistic regression in predicting pacemaker risk following TAVR. Methods: Five hundred fifity seven patients in sinus rhythm undergoing TAVR for severe aortic stenosis (AS) were included in the analysis. Baseline demographics, clinical, pre‐TAVR ECG, post‐TAVR data, post‐TAVR ECGs (24 h following TAVR and before PPI), and echocardiographic data were recorded. A Random Forest (RF) algorithm and logistic regression were used to train models for assessing the likelihood of PPI following TAVR. Results: Average age was 80 ± 9 years, with 52% male. PPI after TAVR occurred in 95 patients (17.1%). The optimal cutoff of delta PR (difference between post and pre TAVR PR intervals) to predict PPI was 20 ms with a sensitivity of 0.82, a specificity of 0.66. With regard to delta QRS, the optimal cutoff was 13 ms with a sensitivity of 0.68 and a specificity of 0.59. The RF model that incorporated post‐TAVR ECG data (AUC 0.81) more accurately predicted PPI risk compared to the RF model without post‐TAVR ECG data (AUC 0.72). Moreover, the RF model performed better than logistic regression model in predicting PPI risk (AUC: 0.81 vs. 0.69). Conclusions: Machine learning using RF methodology is significantly more powerful than traditional logistic regression in predicting PPI risk following TAVR. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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