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

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 < .01). The developed model had good discrimination (AUC = 0.726) and was internally validated (AUC = 0.713). The high-risk group had a 3.7-fold increased risk of advanced CRN compared to the low-risk group (1.1% vs. 4.0%, P < .001). The discrimination performance of the present model for high-risk patients with advanced CRN was better than that of the Asia-Pacific Colorectal Screening score (AUC = 0.678, P < .001) and Schroy's CAN index (AUC = 0.672, P < .001).

Conclusion: The present 5-item risk model can be calculated readily using a simple questionnaire and can identify the low- and high-risk groups of advanced CRN at the first screening colonoscopy. This model may increase colorectal cancer risk awareness and assist healthcare providers in encouraging the high-risk group to undergo a colonoscopy.

Fig 1. Process diagram of a Concept Extraction-based Text Analysis System.

Fig 2. Text pre-processing.

Fig 3. Concept extraction process.

Fig 4. Flow diagram of the study population.

Fig 5. Model performance.

Area under the receiver operating curve (AUC) was calculated to evaluate the discrimination power between the training set (line) and validation set (dot) in prediction model 1 (A) and model 2 (B).

Fig 6. Model calibration.

Cut-off values to discriminate between the high- and low-risk groups for advanced colorectal neoplasia were set at the point between the sixth and seventh deciles based on the risk of advanced colorectal neoplasia.

Fig 7. Comparison of the discrimination performance of the final model with previous published prediction models for advanced colorectal neoplasia.