{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:52:49Z","timestamp":1791237169276,"version":"4.1.0"},"reference-count":27,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pacing Clinical Electrophis"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>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\u2010 and post\u2010TAVR ECG data and compare machine learning approaches with traditional logistic regression in predicting pacemaker risk following TAVR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Five hundred fifity seven patients in sinus rhythm undergoing TAVR for severe aortic stenosis (AS) were included in the analysis. Baseline demographics, clinical, pre\u2010TAVR ECG, post\u2010TAVR data, post\u2010TAVR ECGs (24\u00a0h 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Average age was 80 \u00b1 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\u00a0ms with a sensitivity of 0.82, a specificity of 0.66. With regard to delta QRS, the optimal cutoff was 13\u00a0ms with a sensitivity of 0.68 and a specificity of 0.59. The RF model that incorporated post\u2010TAVR ECG data (AUC 0.81) more accurately predicted PPI risk compared to the RF model without post\u2010TAVR ECG data (AUC 0.72). Moreover, the RF model performed better than logistic regression model in predicting PPI risk (AUC: 0.81\u00a0vs. 0.69).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Machine learning using RF methodology is significantly more powerful than traditional logistic regression in predicting PPI risk following TAVR.<\/jats:p><\/jats:sec>","DOI":"10.1111\/pace.14163","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T09:28:23Z","timestamp":1610702903000},"page":"334-340","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement"],"prefix":"10.1111","volume":"44","author":[{"given":"Vien T.","family":"Truong","sequence":"first","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"},{"name":"The Sue and Bill Butler Research Fellow The Linder Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Beyerbach","sequence":"additional","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wojciech","family":"Mazur","sequence":"additional","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Wigle","sequence":"additional","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma","family":"Bateman","sequence":"additional","affiliation":[{"name":"University of Kentucky  Lexington Kentucky USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akhil","family":"Pallerla","sequence":"additional","affiliation":[{"name":"University of Pittsburgh  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tam N.M.","family":"Ngo","sequence":"additional","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satya","family":"Shreenivas","sequence":"additional","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin T.","family":"Tretter","sequence":"additional","affiliation":[{"name":"Heart Institute Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cassady","family":"Palmer","sequence":"additional","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dean J.","family":"Kereiakes","sequence":"additional","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9443-3274","authenticated-orcid":false,"given":"Eugene S.","family":"Chung","sequence":"additional","affiliation":[{"name":"The Christ Hospital Health Network and The Lindner Research Center  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1136\/heartjnl-2012-302265"},{"key":"e_1_2_9_3_1","first-page":"340","article-title":"2018 AATS\/ACC\/SCAI\/STS Expert Consensus Systems of Care Document: operator and Institutional Recommendations and Requirements for Transcatheter Aortic Valve Replacement","volume":"73","author":"Bavaria JE","year":"2019","journal-title":"Catheterization and Cardiovascular Interventions"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.117.028352"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.113.005479"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2019.07.014"},{"key":"e_1_2_9_7_1","first-page":"500","article-title":"Machine learning for prediction of all\u2010cause mortality in patients with suspected 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