Diagnostic Accuracy of Timed Barium Esophagram for Achalasia.

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Tác giả: Dustin A Carlson, Domenico A Farina, Ofer Z Fass, Nisha Ganesh, Eric Goudie, Linda C Kelahan, Ayesha Lat, John E Pandolfino, Jacob M Schauer

Ngôn ngữ: eng

Ký hiệu phân loại: 004.338 Systems analysis and design, computer architecture, performance evaluation of real-time computers

Thông tin xuất bản: United States : Gastroenterology , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 753613

 BACKGROUND & AIMS: Timed barium esophagram (TBE) is commonly used to evaluate esophageal motor disorders, though its accuracy is limited. The lack of standardized criteria for TBE interpretation, coupled with reliance on single measurements and outdated reference standards, limits its effectiveness. This study aimed to reexamine the accuracy of TBE interpretation using the Standards for Reporting of Diagnostic Accuracy Studies (STARD) approach and updated reference standards. METHODS: Adult patients with esophageal dysphagia were prospectively enrolled from 2019 to 2022 and underwent motility testing with esophageal manometry, functional lumen imaging probe (FLIP) panometry, and TBE. TBE accuracy for predicting achalasia/FLIP+ esophagogastric junction (EGJ) outflow obstruction, as defined by Chicago Classification 4.0 and FLIP, was assessed using 2 approaches: barium column height >
 2 cm at 5 minutes/impacted tablet and a classification tree model. RESULTS: The study included 290 participants: 121 (42%) with EGJ outflow disorders, 151 (52%) without, and 18 (6%) with inconclusive results. The optimal classification tree had 3 levels: maximum esophageal body width at the top, maximum EGJ diameter and barium height at the second level, and tablet passage at the bottom. The TBE column height and tablet approach had a sensitivity of 77.8%, specificity 86.0%, and accuracy 82.2%, whereas the classification tree model achieved a sensitivity of 84.2%, specificity 92.1%, and accuracy of 88.3%. CONCLUSIONS: TBE can accurately identify achalasia/FLIP+ EGJ outflow obstruction when using multiple metrics in a classification tree model. This provides a simple, standardized approach to TBE interpretation that is superior to traditional single-metric methods.
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