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Updated: Aug 16, 2025

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
Development and validation of a prediction model for histologic progression in patients with nondysplastic Barrett's
Noam Peleg1,2, Yehuda Ringel2,3, Steven Shamah1,2
1Division of Gastroenterology, Rabin Medical Center, Beilinson and Hasharon Hospitals, Petah Tikva, Israel.
Objectives:
Patients with Barrett's esophagus (BE) are at risk of progression to esophageal adenocarcinoma (EAC). We developed a model to predict histologic progression in patients with nondysplastic BE (NDBE).
Methods:
A longitudinal study in three referral centers was performed between January 2010 and December 2019. As progression to low-grade dysplasia (LGD) can be considered an indication for ablative therapy, the study end-point was histopathologic progression to LGD, high-grade dysplasia, or EAC at 3 years after diagnosis. We used logistic regression to create the model. Seventy percent of the cohort were used to stem the model and the remaining 30% for internal validation.
Results:
A total of 542 patients were included, 69.4% of whom were male, mean age 62.2 years. Long-segment BE at index endoscopy was diagnosed in 20.8% of the patients. After a mean follow-up of 6.7 years, 133 patients (24.5%) had histologic progression. Our model identified a neutrophil-to-lymphocyte ratio (odds ratio [OR] 2.08, 95% confidence interval [CI] 1.77-2.32, P < 0.001), BE length (OR 1.22, 95% CI 1.09-1.36, P < 0.001), age (OR 1.03, 95% CI 1.02-1.05, P = 0.02), smoking (OR 1.66, 95% CI 1.09-2.75, P = 0.04), and renal failure (OR 1.51, 95% CI 0.93-2.43, P = 0.07) as predictors of histologic progression at 3 years. The areas under the receiver operating characteristic curves of this model were 0.88 and 0.76 in the training and validation cohorts, respectively.
Conclusion:
This novel, internally validated model may predict histologic progression, even in patients with NDBE who generally have low rates of progression over time, and may contribute to enhanced patient selection for more intense surveillance programs.
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