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Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model.

Michelle Kang Kim1, Carol Rouphael1, Sarah Wehbe1

  • 1Department of Gastroenterology, Hepatology, and Nutrition, Cleveland Clinic, Cleveland, Ohio.

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Summary

A new logistic regression model using electronic health records can identify individuals at high risk for noncardia gastric cancer (NCGC). This tool enables targeted screening for this deadly disease.

Keywords:
Cancer DisparityElectronic Health RecordHigh-Risk IndividualsLogistic Regression ModelNoncardia Gastric CancerScreening

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Area of Science:

  • Oncology
  • Medical Informatics
  • Epidemiology

Background:

  • Gastric cancer (GC) poses a significant global health burden, contributing to high incidence and mortality rates.
  • Screening for GC in the United States is challenging due to its low incidence and prevalence.
  • Developing a risk prediction algorithm is crucial for targeted GC screening strategies.

Purpose of the Study:

  • To assess the feasibility and performance of a logistic regression model.
  • To identify individuals at high risk for noncardia gastric cancer (NCGC) using electronic health records (EHRs).

Main Methods:

  • A logistic regression model was developed using EHR data from 614 patients diagnosed with NCGC (ages 40-80).
  • Controls without NCGC were randomly selected at a 1:10 ratio.
  • Multiple imputation handled missing data, and logistic regression estimated NCGC probability. Model discrimination was assessed using the 0.632 estimator.

Main Results:

  • The model demonstrated robust performance with a 0.632 estimator value of 0.731.
  • Factors increasing NCGC probability included older age, male sex, Black or Asian race, tobacco use, anemia, and pernicious anemia.

Conclusions:

  • An EHR-based logistic regression model is feasible and performs well in estimating NCGC probability.
  • Further research will refine and validate this model for identifying high-risk individuals for NCGC screening.