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The purpose of this study was to improve the accuracy of PG\n                    <jats:sub>AV<\/jats:sub>\n                    measurements by echocardiography in small annuli and to evaluate the influence of PG\n                    <jats:sub>AV<\/jats:sub>\n                    on prognosis. The internal derivation cohort included 273 consecutive patients with aortic stenosis and a small annulus (computed tomographic scan showing an annulus circumference\u2009&lt;\u200972\u00a0mm or area\u2009&lt;\u2009400 mm\n                    <jats:sup>2<\/jats:sup>\n                    ) who underwent TAVR. Patients completed transthoracic echocardiography (TTE) and LHC measurements during TAVR, and an extreme gradient boosting (XGBoost) algorithm was trained. The primary outcome was a composite end point of all-cause mortality and readmission for heart failure. The mean PG\n                    <jats:sub>AV<\/jats:sub>\n                    level measured by TTE was overestimated compared to the LHC measurement {52.5 [interquartile range: 47.5\u201357.0] mmHg vs. 42.5 (interquartile range: 38.0\u201346.0) mmHg,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u20090.001}. After adjustments to the XGBoost, the mean PG\n                    <jats:sub>AV<\/jats:sub>\n                    measured by TTE could be significantly improved [Pearson correlation coefficient\u2009=\u20090.94,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u20090.001]. Importantly, patients with a predicted mean PG\n                    <jats:sub>AV<\/jats:sub>\n                    \u2265 68.6 mmHg showed a significantly increased incidence of composite end points at 2 years after the procedures (40.7% vs. 16.0%,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u20090.001). The XGBoost model could effectively improve the accuracy of the mean PG\n                    <jats:sub>AV<\/jats:sub>\n                    measured by TTE during TAVR, and the predicted mean increase in the PG\n                    <jats:sub>AV<\/jats:sub>\n                    level may lead to a worse prognosis.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:italic>Clinical Trial Registration:\u00a0<\/jats:italic>\n                    ClinicalTrials.gov Protocol Registration System (NCT05044338).\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s12928-025-01215-5","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T19:11:27Z","timestamp":1767640287000},"page":"402-413","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Clinical significance of machine learning algorithm in predicting PPM during TAVR in small annuli"],"prefix":"10.1007","volume":"41","author":[{"given":"Yu","family":"Mao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengen","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangyao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gejun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingqiang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangbin","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongjian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"1215_CR1","first-page":"171","volume":"19","author":"G Melina","year":"2010","unstructured":"Melina G, Angeloni E, Benedetto U, et al. 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