AN ELECTRONIC MEDICAL RECORD PREDICTION MODEL TO IDENTIFY INADEQUATE BOWEL PREPARATION IN PATIENTS AT OUTPATIENT COLONOSCOPY.

Background and Aims: Inadequate bowel preparation during colonoscopy is associated with decreased adenoma detection, increased costs, and patient procedural risks. This study aimed to develop a prediction model for identifying patients at high risk of inadequate bowel preparation for potential clinical integration into the EMR.
Methods: A retrospective study was conducted using outpatient screening/surveillance colonoscopies at the University of North Carolina (UNC) from 2017 to 2022. Data were extracted from the EMRs of Epic and ProVation , including demographic, socioeconomic, and clinical variables. Logistic regression, LASSO regression, and gradient boosting machine (GBM) models were evaluated and validated in a held-out testing set.
Results: The dataset included 23,456 colonoscopies, of which 6.25% had inadequate bowel preparation. The reduced LASSO regression model demonstrated an area under the curve (AUC) of 0.65 [95% CI 0.63-0.67] in the held-out testing set. The relative risk of inadequate bowel prep in the high-risk group determined by the model was 2.42 (95% CI 2.07-2.82), compared to patients identified as low risk. The model calibration in the testing set revealed that among patients categorized as having 0-11%, 11-22%, and 22-33% predicted risk of inadequate prep, the respective proportions of patients with inadequate prep were 5.5%, 19.3%, and 33.3%. Using the reduced LASSO model, a rudimentary code for a potential Epic FHIR application called PrepPredict was developed.
Conclusions: This study developed a prediction model for inadequate bowel preparation with the potential to integrate into the EMR for clinical use and optimize bowel preparation to improve patient care.
Competing Interests: Conflict of Interest: The authors disclose no conflicts.