The Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) Tool was developed and validated using a form of artificial intelligence of routine electronic medical records to predict which patients would develop particular types of cancer of the esophagus or stomach years in advance.
Background and Context: Tools that can automatically predict incident esophageal adenocarcinoma (EAC) and gastric cardia adenocarcinoma (GCA) using electronic health records (EHR) to guide screening decisions are needed.
New Findings: In this retrospective case-control analysis using machine learning, the Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) Tool was well calibrated and more accurate than available alternatives. While GERD was associated with EAC/GCA, other factors added more information to the model.
Limitations: K-ECAN was developed and validated within the Veterans Health Administration (VHA) population. Non-Veterans may differ in terms of the frequency of medical encounters and laboratory blood draws, and non-VHA settings may differ in their practice of diagnostic coding.
Clinical Research Relevance: The Kettles Esophageal and Cardia Adenocarcinoma predictioN Tool is an automated, valid tool predicting incident esophageal