Dysplastic Recurrence After Successful Treatment for Early Barrett's Neoplasia: Development and Validation of a...

The combination of endoscopic resection and radiofrequency ablation is the treatment of choice for eradication of Barrett's esophagus (BE) with dysplasia and/or early cancer. Currently, there are no evidence-based recommendations on how to survey patients after successful treatment, and most patients undergo frequent follow-up endoscopies. We aimed to develop and externally validate a prediction model for visible dysplastic recurrence, which can be used to personalize surveillance after treatment.

We collected data from the Dutch Barrett Expert Center Registry, a nationwide registry that captures outcomes from all patients with BE undergoing endoscopic treatment in the Netherlands in a centralized care setting. We used predictors related to demographics, severity of reflux, histologic status at baseline, and treatment characteristics. We built a Fine and Gray survival model with least absolute shrinkage and selection operator penalization to predict the incidence of visible dysplastic recurrence after initial successful treatment. The model was validated externally in patients with BE treated in Switzerland and Belgium.

A total of 1154 patients with complete BE eradication were included for model building. During a mean endoscopic follow-up of 4 years, 38 patients developed recurrent disease (1.0%/person-year). The following characteristics were independently associated with recurrence (strongest to weakest predictor): a new visible lesion during treatment phase, higher number of endoscopic resection treatments, male sex, increasing BE length, high-grade dysplasia or cancer at baseline, and younger age. External validation showed a C-statistic of 0.91 (95% confidence interval, 0.86-0.94) with good calibration.

This is the first externally validated model to predict visible dysplastic recurrence after successful endoscopic eradication treatment of BE with dysplasia or early cancer. On external validation, our model has good discrimination and calibration. This model can help clinicians and patients to determine a personalized follow-up strategy.