Clinical Prediction Models to Guide the Selection of Patients for Colon Capsule Endoscopy Versus Colonoscopy...

IntroductionIncreasing demand for colonoscopy continues to strain healthcare systems worldwide. Colon capsule endoscopy (CCE) offers a minimally invasive alternative, but its adoption is limited by high re-investigation rates. The aim of this study is to develop and evaluate clinical prediction models for selecting faecal immunochemical test (FIT) positive patients most suitable for CCE versus colonoscopy.MethodsWe conducted a secondary analysis of data from CareForColon2015 randomized controlled trial (2020-2022), including individuals aged 50-74 years with a positive FIT. Logistic regression models were developed to predict CCE transit, bowel cleansing, completeness, and colonoscopy indication. Sixty candidate predictors were assessed, including demographics, lifestyle factors, FIT values, medications, perceived stress, and health literacy. Models were validated using repeated random subsampling and evaluated on a 10% hold-out set using the area under the receiver-operating-characteristic curve (AUC), Cohen's K, and accuracy. Decision curve analysis (DCA) was performed to assess clinical utility.ResultsCCE achieved complete transit in 92.1% and acceptable bowel cleansing in 71.3% of participants, with 69.6% of investigations deemed complete. Colonoscopy was indicated in 68.0% of cases, based on broad inclusion criteria, and 55.9%, based on more stringent criteria. Models predicting colonoscopy indication showed moderate performance (AUC 0.69-0.71; accuracy 65-67%; Cohen's K 0.28-0.30). DCA indicated positive net benefit for both models within threshold probabilities of 0.5-0.75, supporting their potential to identify FIT-positive patients unlikely to benefit from immediate colonoscopy.ConclusionsClinical prediction models may assist in post-FIT triage between CCE and colonoscopy. DCA suggests potential to reduce unnecessary colonoscopies by identifying low-risk patients suitable for initial CCE. External validation is needed before clinical implementation.