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

Author:

Takahashi Kazuya1ORCID,Sato Hiroki1ORCID,Shimamura Yuto2ORCID,Abe Hirofumi3,Shiwaku Hironari4,Shiota Junya5ORCID,Sato Chiaki6,Hamada Kenta7ORCID,Ominami Masaki8,Hata Yoshitaka9,Fukuda Hisashi10,Ogawa Ryo11,Nakamura Jun12,Tatsuta Tetsuya13,Ikebuchi Yuichiro14ORCID,Yokomichi Hiroshi15,Terai Shuji1,Inoue Haruhiro2

Affiliation:

1. Division of Gastroenterology and Hepatology, Graduate School of Medical and Dental Sciences Niigata University Niigata Japan

2. Digestive Diseases Center Showa University Koto‐Toyosu Hospital Tokyo Japan

3. Department of Gastroenterology Kobe University Hospital Kobe Japan

4. Department of Gastroenterological Surgery Fukuoka University Faculty of Medicine Fukuoka Japan

5. Department of Gastroenterology and Hepatology Nagasaki University Hospital Nagasaki Japan

6. Division of Advanced Surgical Science and Technology Tohoku University School of Medicine Miyagi Japan

7. Department of Practical Gastrointestinal Endoscopy, Faculty of Medicine, Dentistry and Pharmaceutical Sciences Okayama University Okayama Japan

8. Department of Gastroenterology Osaka Metropolitan University Graduate School of Medicine Osaka Japan

9. Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences Kyushu University Fukuoka Japan

10. Division of Gastroenterology, Department of Medicine Jichi Medical University Tochigi Japan

11. Department of Gastroenterology, Faculty of Medicine Oita University Oita Japan

12. Department of Endoscopy Fukushima Medical University Hospital Fukushima Japan

13. Department of Gastroenterology and Hematology Hirosaki University Graduate School of Medicine Aomori Japan

14. Division of Gastroenterology and Nephrology, Department of Multidisciplinary Internal Medicine Tottori University Faculty of Medicine Tottori Japan

15. Department of Health Sciences University of Yamanashi Yamanashi Japan

Abstract

ObjectivesHigh‐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.MethodsIn 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.ResultsMachine 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.ConclusionAchalasia 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.

Publisher

Wiley

Subject

Gastroenterology,Radiology, Nuclear Medicine and imaging

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