BMJ open gastroenterology · 2026
Development and validation of multivariable prognostic machine learning models to identify patients at risk for inadequate bowel preparation when undergoing colonoscopy: a prospective, multicentre study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Schramm Christoph, Hehn Carolin, Curth Harald, Scheller Ingo, Weingaertner Jan-Hinnerk, Reichert Matthias Christian, Kasper Philipp, Bozal Dilan, Allo Gabriel, Casper Markus, Adams Thomas, Jochheim Leonie, Zeller Amos, Rahe Gilbert, Zeeh Joerg, Neumann-Haefelin Christoph, Lammert Frank, Schmidt Hartmut, Friedrich Christoph M, Buerger Martin
Аннотация
OBJECTIVE: Inadequate bowel preparation impairs the accuracy of colonoscopy and increases the burden on patients and healthcare systems. Consequently, the quality of bowel preparation is an important quality indicator. We aim to develop and validate multivariable prognostic models for the identification of patients at risk of inadequate bowel preparation using machine learning (ML).
METHODS: Demographic and clinical data from consecutive patients ≥18 years of age who underwent colonoscopy at six centres in Germany were prospectively collected. Adequate bowel preparation was defined as Boston Bowel Preparation Scale ≥6 with a value ≥2 in each colonic segment. We used statistical and ML methods to build prognostic models and to compare them to published models.
RESULTS: Overall, we analysed 2652 patients, including 699 (26.4%) inpatient procedures. The mean patient age was 57.6 years (SD 16 years), and 48.9% were women. In 1401 (52.8%) patients, the indication was screening or surveillance, and 1035 (39%) patients had a first-time colonoscopy. The rate of inadequate bowel preparation was 16%. Sensitivities, specificities and areas under the curve of predictive models obtained by generalised boosting models, Ranger, support vector machine (radial), CatBoost and Net Regularised Generalised Linear Models were 0.51-0.86, 0.52-0.83 and 0.71-0.74, respectively. They were only marginally superior to a logistic regression model. All models had high negative predictive values >0.9 for inadequate bowel preparation. To detect one patient with inadequate bowel preparation, 8-10 patients need to be evaluated.
CONCLUSIONS: Predictive models to identify patients at risk for inadequate bowel preparation obtained by ML showed comparable results compared with a logistic regression model.
TRIAL REGISTRATION NUMBER: DRKS00018878.
METHODS: Demographic and clinical data from consecutive patients ≥18 years of age who underwent colonoscopy at six centres in Germany were prospectively collected. Adequate bowel preparation was defined as Boston Bowel Preparation Scale ≥6 with a value ≥2 in each colonic segment. We used statistical and ML methods to build prognostic models and to compare them to published models.
RESULTS: Overall, we analysed 2652 patients, including 699 (26.4%) inpatient procedures. The mean patient age was 57.6 years (SD 16 years), and 48.9% were women. In 1401 (52.8%) patients, the indication was screening or surveillance, and 1035 (39%) patients had a first-time colonoscopy. The rate of inadequate bowel preparation was 16%. Sensitivities, specificities and areas under the curve of predictive models obtained by generalised boosting models, Ranger, support vector machine (radial), CatBoost and Net Regularised Generalised Linear Models were 0.51-0.86, 0.52-0.83 and 0.71-0.74, respectively. They were only marginally superior to a logistic regression model. All models had high negative predictive values >0.9 for inadequate bowel preparation. To detect one patient with inadequate bowel preparation, 8-10 patients need to be evaluated.
CONCLUSIONS: Predictive models to identify patients at risk for inadequate bowel preparation obtained by ML showed comparable results compared with a logistic regression model.
TRIAL REGISTRATION NUMBER: DRKS00018878.
Предметные рубрики MeSH
HumansColonoscopyFemaleMiddle AgedProspective StudiesMalePredictive Learning ModelsAgedGermanyCatharticsPrognosisSensitivity and SpecificityMachine LearningBoosting Machine Learning AlgorithmsRisk AssessmentAdultColorectal NeoplasmsMultivariate AnalysisPredictive Value of TestsRisk FactorsPrediction Algorithms