Achalasia phenotypes and prediction of peroral endoscopic myotomy outcomes using machine learning.

Autor: Takahashi K; Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan., Sato H; Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan., Shimamura Y; Digestive Diseases Center, Showa University Koto-Toyosu Hospital, Tokyo, Japan., Abe H; Department of Gastroenterology, Kobe University Hospital, Kobe, Japan., Shiwaku H; Department of Gastroenterological Surgery, Fukuoka University Faculty of Medicine, Fukuoka, Japan., Shiota J; Department of Gastroenterology and Hepatology, Nagasaki University Hospital, Nagasaki, Japan., Sato C; Division of Advanced Surgical Science and Technology, Tohoku University School of Medicine, Miyagi, Japan., Hamada K; Department of Practical Gastrointestinal Endoscopy, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan., Ominami M; Department of Gastroenterology, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan., Hata Y; Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan., Fukuda H; Division of Gastroenterology, Department of Medicine, Jichi Medical University, Tochigi, Japan., Ogawa R; Department of Gastroenterology, Faculty of Medicine, Oita University, Oita, Japan., Nakamura J; Department of Endoscopy, Fukushima Medical University Hospital, Fukushima, Japan., Tatsuta T; Department of Gastroenterology and Hematology, Hirosaki University Graduate School of Medicine, Aomori, Japan., Ikebuchi Y; Division of Gastroenterology and Nephrology, Department of Multidisciplinary Internal Medicine, Tottori University Faculty of Medicine, Tottori, Japan., Yokomichi H; Department of Health Sciences, University of Yamanashi, Yamanashi, Japan., Terai S; Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Japan., Inoue H; Digestive Diseases Center, Showa University Koto-Toyosu Hospital, Tokyo, Japan.
Jazyk: angličtina
Zdroj: Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society [Dig Endosc] 2024 Jul; Vol. 36 (7), pp. 789-800. Date of Electronic Publication: 2023 Dec 01.
DOI: 10.1111/den.14714
Abstrakt: Objectives: High-resolution 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-naïve patients remains an unmet need.
Methods: In this multicenter cohort study, we included 1824 treatment-naïve 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 ≥3) and reflux esophagitis (RE) (Los Angeles grades A-D) after POEM.
Results: Machine learning identified three achalasia phenotypes: phenotype 1, type I achalasia with a dilated esophagus (n = 676; 37.0%); phenotype 2, type II achalasia with a dilated esophagus (n = 203; 11.1%); and phenotype 3, late-onset type I-III achalasia with a nondilated esophagus (n = 619, 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-POEM Eckardt score ≥6 was the greatest contributing factor for persistent symptoms. The area under the curve for post-POEM RE was 0.61.
Conclusion: 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.
(© 2023 Japan Gastroenterological Endoscopy Society.)
Databáze: MEDLINE
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