JOURNAL ARTICLE

Validation of Tools for Predicting Incident Adenocarcinoma of the Esophagus or Esophagogastric Junction

Joel H Rubenstein, Trivellore Raghunathan, Cecilia Doan, Jennifer Schneider, Wei Zhao, Valbona Metko, Kimberly Nofz, Maryam Khodadost, Douglas A Corley
American Journal of Gastroenterology 2021 April 13
33852454

INTRODUCTION: Guidelines suggest screening of individuals who are at increased risk of esophageal adenocarcinoma (EAC). Tools for identifying patients at risk of Barrett's esophagus have been validated. Here, we aimed to compare and validate the tools for the primary outcomes of interest: EAC and esophagogastric junction adenocarcinoma (EGJAC).

METHODS: Retrospective longitudinal analysis of the Kaiser Permanente Northern California Multiphasic Health Checkup Cohort, a community-based cohort including 206,974 patients enrolled between 1964 and 1973 followed through 2016. Baseline questionnaires and anthropometrics classified predictor variables for each tool and were linked to cancer registry outcomes. Analyses used logistic regression, Cox proportional hazards regression, and Kaplan-Meier survival curves.

RESULTS: We identified 168 incident EAC cases and 151 EGJAC cases at a mean of 32 years after enrollment (mean follow-up among controls 26 years). Gastroesophageal reflux disease (GERD) symptoms predicted incident EAC (hazard ratio 2.66; 95% confidence interval 1.01, 7.00), but not EGJAC. The Nord-Trøndelag Health Study tool, Kunzmann tool, and Michigan Barrett's Esophagus pREdiction Tool were more accurate than GERD for predicting EAC, with individuals in the fourth quartile of Kunzmann having 17-fold the risk of those in the 1st quartile (hazard ratio = 16.7, 95% confidence interval = 4.72, 58.8). Each tool also predicted incident EGJAC with smaller magnitudes of effect.

DISCUSSION: The study independently validated 4 tools for predicting incident EAC and EGJAC in a large community-based population. The Kunzmann tool appears best calibrated; all appear preferable to using GERD alone for risk stratification. Future studies should determine how best to implement such tools into clinical practice.

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