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A prediction model for advanced colorectal neoplasia in an asymptomatic screening population

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Listed:
  • Sung Noh Hong
  • Hee Jung Son
  • Sun Kyu Choi
  • Dong Kyung Chang
  • Young-Ho Kim
  • Sin-Ho Jung
  • Poong-Lyul Rhee

Abstract

Background: An electronic medical record (EMR) database of a large unselected population who received screening colonoscopies may minimize sampling error and represent real-world estimates of risk for screening target lesions of advanced colorectal neoplasia (CRN). Our aim was to develop and validate a prediction model for assessing the probability of advanced CRN using a clinical data warehouse. Methods: A total of 49,450 screenees underwent their first colonoscopy as part of a health check-up from 2002 to 2012 at Samsung Medical Center, and the dataset was constructed by means of natural language processing from the computerized EMR system. The screenees were randomized into training and validation sets. The prediction model was developed using logistic regression. The model performance was validated and compared with existing models using area under receiver operating curve (AUC) analysis. Results: In the training set, age, gender, smoking duration, drinking frequency, and aspirin use were identified as independent predictors for advanced CRN (adjusted P

Suggested Citation

  • Sung Noh Hong & Hee Jung Son & Sun Kyu Choi & Dong Kyung Chang & Young-Ho Kim & Sin-Ho Jung & Poong-Lyul Rhee, 2017. "A prediction model for advanced colorectal neoplasia in an asymptomatic screening population," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    • Handle: RePEc:plo:pone00:0181040
    DOI: 10.1371/journal.pone.0181040
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    References listed on IDEAS

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    1. Aesun Shin & Jungnam Joo & Hye-Ryung Yang & Jeongin Bak & Yunjin Park & Jeongseon Kim & Jae Hwan Oh & Byung-Ho Nam, 2014. "Risk Prediction Model for Colorectal Cancer: National Health Insurance Corporation Study, Korea," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-8, February.
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