{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:01:12Z","timestamp":1775325672934,"version":"3.50.1"},"reference-count":44,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"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":["Digestive Endoscopy"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>High\u2010resolution manometry (HRM) and esophagography are used for achalasia diagnosis; however, achalasia phenotypes combining esophageal motility and morphology are unknown. Moreover, predicting treatment outcomes of peroral endoscopic myotomy (POEM) in treatment\u2010na\u00efve patients remains an unmet need.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this multicenter cohort study, we included 1824 treatment\u2010na\u00efve patients diagnosed with achalasia. In total, 1778 patients underwent POEM. Clustering by machine learning was conducted to identify achalasia phenotypes using patients' demographic data, including age, sex, disease duration, body mass index, and HRM\/esophagography findings. Machine learning models were developed to predict persistent symptoms (Eckardt score \u22653) and reflux esophagitis (RE) (Los Angeles grades A\u2013D) after POEM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Machine learning identified three achalasia phenotypes: phenotype 1, type I achalasia with a dilated esophagus (<jats:italic>n<\/jats:italic>\u2009=\u2009676; 37.0%); phenotype 2, type II achalasia with a dilated esophagus (<jats:italic>n<\/jats:italic>\u2009=\u2009203; 11.1%); and phenotype 3, late\u2010onset type I\u2013III achalasia with a nondilated esophagus (<jats:italic>n<\/jats:italic>\u2009=\u2009619, 33.9%). Types I and II achalasia in phenotypes 1 and 2 exhibited different clinical characteristics from those in phenotype 3, implying different pathophysiologies within the same HRM diagnosis. A predictive model for persistent symptoms exhibited an area under the curve of 0.70. Pre\u2010POEM Eckardt score \u22656 was the greatest contributing factor for persistent symptoms. The area under the curve for post\u2010POEM RE was 0.61.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Achalasia phenotypes combining esophageal motility and morphology indicated multiple disease pathophysiologies. Machine learning helped develop an optimal risk stratification model for persistent symptoms with novel insights into treatment resistance factors.<\/jats:p><\/jats:sec>","DOI":"10.1111\/den.14714","type":"journal-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T09:07:48Z","timestamp":1698397668000},"page":"789-800","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Achalasia phenotypes and prediction of peroral endoscopic myotomy outcomes using machine learning"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3097-9841","authenticated-orcid":false,"given":"Kazuya","family":"Takahashi","sequence":"first","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences Niigata University  Niigata 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