Introduction

Malignant biliary obstruction (MBO) often necessitates endoscopic retrograde cholangiopancreatography (ERCP) for biliary drainage. Although ERCP is standard treatment, post-procedural infections such as cholangitis and cholecystitis remain clinically important. Current guidelines recommend prophylactic antibiotics only for high-risk cases. Clinically, MBO is categorized into distal malignant biliary obstruction (DMBO) and hilar malignant biliary obstruction (HMBO), depending on whether the obstruction involves the extrahepatic duct or the intrahepatic duct/hilar confluence. While several studies have supported the use of prophylactic antibiotics in HMBO, evidence remains limited for DMBO. This study aimed to evaluate the effect of prophylactic antibiotics on post-ERCP infectious complications in DMBO.

Aims & Methods

We retrospectively reviewed patients who underwent initial ERCP with stent placement for DMBO at five Japanese centers between January 2020 and September 2024. Patients were grouped based on the use or non-use of prophylactic antibiotics for ERCP. Propensity score matching (1:1) was performed using clinically relevant covariates. In addition, a sensitivity analysis was conducted via multivariable logistic regression on the before matched data to further assess the robustness of the findings. The primary outcome was the incidence of infectious complications (cholangitis or cholecystitis) within five days of ERCP.

Results

A total of 508 patients (304 in the antibiotic group, 204 in the non-antibiotic group) were included in the study. After PSM, 165 patients were selected for each group. Characteristics of the antibiotic vs. non-antibiotic groups included: median age 74 years in both groups; sex 92 (55.8%) males vs. 94 (57.0%) males; pancreatic cancer 129 (77.2%) cases vs. 133 (79.6%) cases; plastic stents 72 (43.6%) cases vs. 69 (41.8%) cases.
In the matched population, the incidence of overall infectious complications (cholangitis or cholecystitis) was significantly lower in the prophylactic antibiotic group than in the non-antibiotic group (3.6% vs. 10.3%; risk difference (RD), 6.7%; p=0.029), corresponding to a number needed to treat (NNT) of 14.9. No significant difference was observed in the rate of post-ERCP pancreatitis: 10 patients (6.1%) in the non-antibiotic group and 16 patients (9.7%) in the antibiotic group (p = 0.307). Sensitivity analysis using multivariable logistic regression confirmed that absence of prophylactic antibiotics was independently associated with a higher risk of infectious complications (odds ratio: 2.45, 95% confidence interval: 1.22–4.92).

Conclusion

Prophylactic antibiotics significantly reduce post-ERCP infectious complications in patients with DMBO.

Introduction

Bowel preparation quality is a key determinant of colonoscopy effectiveness and adenoma detection^1,2. Manual evaluation of free-text colonoscopy reports is labor-intensive and limits quality monitoring³. Developing a natural language processing (NLP) application for measuring colonoscopy quality demonstrates the potential to automate this process by extracting key quality indicators⁴.

Aims & Methods

We aimed to develop and validate a NLP approach to automatically classify bowel cleanliness and to comprehensively structure colonoscopy reports by extracting procedural and pathological variables. A total of 11,374 reports from our institutional endoscopy unit were analyzed. An annotated dataset of 1,018 reports labeled as “good,” “intermediate,” or “poor” preparation was used to train a TF-IDF–based logistic regression classifier. Cross-validation and test performance were evaluated using accuracy, precision, recall, and F1-scores, and the model was applied to the full dataset. Additional variables (cecal intubation, polyp presence, number, size, localization, polypectomy method, and hemostasis application) were extracted using rule-based NLP heuristics. Adenoma detection rate (ADR) was calculated in a predefined subset (January 2023–July 2025) with complete pathology data, and multivariable logistic regression identified independent predictors.

Results

The NLP classifier achieved a cross-validated accuracy of 92%. Precision and recall were highest for “good” preparation (0.98 and 0.99) and acceptable for “intermediate” (0.85 and 0.82) and “poor” (0.85 and 0.87). Application to the full cohort yielded 5,787 (50.9%) good, 2,176 (19.1%) intermediate, and 3,411 (30.0%) poor preparations. Leveraging these structured data, ADR analysis in the subset of 5,420 patients showed an overall rate of 20.4%. Multivariable analysis demonstrated that ADR was independently associated with older age (OR 1.05 per year, 95% CI 1.05–1.06, p<0.001), male sex (OR 1.74, 95% CI 1.51–2.01, p<0.001), adequate bowel preparation (OR 1.46, 95% CI 1.21–1.75, p<0.001), cecal intubation (OR 1.76, 95% CI 1.43–2.16, p<0.001), and procedures performed by specialists compared with trainees (24.0% vs 20.3%; OR 1.25, 95% CI 1.05–1.47, p=0.010).

Conclusion

This large-scale real-world analysis demonstrates the feasibility of NLP-based automated classification of bowel preparation quality and its direct utility in generating structured colonoscopy datasets for quality monitoring. By integrating bowel cleanliness with other performance indicators (age, sex, cecal intubation, operator expertise), our framework not only achieved high classification accuracy but also provided clinically relevant benchmarks for ADR, supporting targeted quality improvement in endoscopy units.

References

1. Clark BT, Protiva P, Nagar A, et al. Quantification of Adequate Bowel Preparation for Screening or Surveillance Colonoscopy in Men. Gastroenterology. 2016;150(2):396-e15. doi:10.1053/j.gastro.2015.09.041
2. Clark BT, Rustagi T, Laine L. What level of bowel prep quality requires early repeat colonoscopy: systematic review and meta-analysis of the impact of preparation quality on adenoma detection rate. Am J Gastroenterol. 2014;109(11):1714-1724. doi:10.1038/ajg.2014.232
3. Raju GS, Lum PJ, Slack RS, et al. Natural language processing as an alternative to manual reporting of colonoscopy quality metrics. Gastrointest Endosc. 2015;82(3):512-519. doi:10.1016/j.gie.2015.01.049
4. Harkema H, Chapman WW, Saul M, Dellon ES, Schoen RE, Mehrotra A. Developing a natural language processing application for measuring the quality of colonoscopy procedures. J Am Med Inform Assoc. 2011;18 Suppl 1(Suppl 1):i150-i156. doi:10.1136/amiajnl-2011-000431