Validation and Application of Predictive Models for Inadequate Bowel Preparation in Colonoscopies in a Tertiary Hospital Population.

Abstract

Background: Bowel preparation is a major quality criterion for colonoscopies. Models developed to identify patients with inadequate preparation have not been validated in external cohorts. We aim to validate these models and determine their applicability.

Methods: Colonoscopies between April and November 2019 were retrospectively included. Boston Bowel Preparation Scale ≥2 per segment was considered adequate. Insufficient data, incomplete colonoscopies, and total colectomies were excluded. Two models were tested: model 1 (tricyclic antidepressants, opioids, diabetes, constipation, abdominal surgery, previous inadequate preparation, inpatient status, and American Society of Anesthesiology [ASA] score ≥3); model 2 (co-morbidities, tricyclic antidepressants, constipation, and abdominal surgery).

Results: We included 514 patients (63% males; age 61.7 ± 15.6 years), 441 with adequate preparation. The main indications were inflammatory bowel disease (26.1%) and endoscopic treatment (24.9%). Previous surgery (36.2%) and ASA score ≥3 (23.7%) were the most common comorbidities. An ASA score ≥3 was the only identified predictor for inadequate preparation in this study (p < 0.001, OR 3.28). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of model 1 were 60.3, 64.2, 21.8, and 90.7%, respectively. Model 2 had a sensitivity, specificity, PPV, and NPV of 57.5, 67.4, 22.6, and 90.5%, respectively. The AUC for the ROC curves was 0.62 for model 1, 0.62 for model 2, and 0.65 for the ASA score.

Conclusions: Although both models accurately predict adequate bowel preparation, they are still unreliable in predicting inadequate preparation and, as such, new models, or further optimization of current ones, are needed. Utilizing the ASA score might be an appropriate approximation of the risk for inadequate bowel preparation in tertiary hospital populations.