Introduction

Aims & Methods

Results

Conclusion

References

Introduction

Endoscopic retrograde cholangiopancreatography (ERCP) is a key procedure in managing biliary and pancreatic disorders. In cases of difficult cannulation (DC), when standard cannulation techniques (SCT) fail, precut sphincterotomy (PS) may be used as an alternative technique, often by experienced endoscopists. PS has been shown to improve cannulation success rates, but its use has been associated with an increased risk of adverse events, including post-ERCP pancreatitis (PEP), perforation, and bleeding. Despite this it is argued that it might be safer to proceed with PS early rather than to traumatise the papilla with continuous attempts with SCT. DC is defined as cannulation taking more than 5 minutes, more than 5 attempts or accidently entering the pancreatic duct with a guidewire more than once and is known to be associated with more complications than non-DC. The aim of this study was to compare outcomes of PS versus SCT in cases of DC, regarding complication rates and successful cannulation.

Aims & Methods

Data were retrieved from the Swedish National Register for Gallstone surgery and ERCP, GallRiks from September 2016 to December 2023. A total of 30,561 ERCPs, performed as index ERCPs without rendezvous technique, were identified and cross-checked. Logistic regression analyses were conducted, adjusting for endoscopist and clinic volume using generalized estimating equations.

Results

Altogether 9,909 procedures were classified as DC among which 3,005 (30%) were performed with PS, compared to 434 (2%) of 20,218 in the non-DC group. In DC the rate of pancreatitis was 9.4%, cholangitis 2.3%, bleeding 1.4%, bile leakage 1.9% and perforation 0.7%. PS was not associated with increased risk of procedure related complications in DC. The rate of successful cannulation in DC was 87.9% when SCT were used and 70.4% when PS was performed.

Conclusion

PS is a safe technique in ERCP when cannulation is difficult and is not associated with an increased risk of complications compared to SCT.

Introduction

Chronic liver disease is a major global health concerns, contributing to over 1.4 million annual deaths worldwide¹. Its primary fatal outcomes, hepatic complications^2,3, arise from liver cirrhosis, hepatocellular carcinoma and hepatic decompensation. The early, reversible stages of chronic liver disease are frequently asymptomatic and overlooked, leading to delayed diagnoses and irreversible complications that treatment options are limited.^4,5. Therefore, reliable tools to identify whether a person will develop hepatic complications in the future have become a public health priority, enabling early diagnosis and timely intervention in high-risk populations.

Aims & Methods

This study aimed to employ machine learning to develop proteomics-based models for the early identification of hepatic complications. In this study, 37,178 adults without hepatic complications at baseline were analyzed from a prospective cohort from United Kingdom, with plasma levels of 2,736 proteins measured using Olink technology. Cox regression was used to identify significant proteins associated with incident hepatic complications. Models based on importance-top-ranking proteins were constructed using LightGBM learners of Automated Machine Learning (AutoML) and internal validated through 5-fold cross validation in the derivation set (n = 37,178). Independent validation was then conducted in a geographically distinct UKB cohort (n = 15,767).

Results

Over a median follow-up of 13.8 years, 338 participants developed hepatic complications. Cox regression identified 593 proteins significantly associated with incident hepatic complications. Among them, GDF15, PROC, GGT1, ANXA10, IL18, MFAP4, COL4A1, CDCP1 and IL6 were ranked highest in protein importance ordering. The protein model showed a considerable predictive accuracy for hepatic complications under different time scenarios in the UKB validation set: all-year (area under the curve [AUC] = 0.89), within 5 years (AUC = 0.88), within 10 years (AUC = 0.91), over 10 years (AUC = 0.81), and achieved superior predictive performance compared to models with demographic predictors, laboratory indicators, polygenic risk score (PRS) and traditional risk score models (Protein 0.89 vs. Demographics 0.83, Laboratory 0.85, PRS 0.49; Fibrosis-4 index [FIB-4] 0.76, Aspartate aminotransferase to platelet ratio index [APRI] 0.78; all P < 0.05). Combined with clinical data and PRS, the predictor performance is further enhanced: all-year (AUC = 0.91), within 5 years (AUC = 0.91), within 10 years (AUC = 0.90), over 10 years (AUC = 0.83). Individuals in the high-risk group stratified by the protein model were 16.27 times more likely to develop hepatic complications.

Conclusion

Our study constructed a novel protein model for hepatic complications risk stratification and could be used to predict individuals who will develop hepatic complications up to 16 years in advance in the general population.

References

1. Tham EKJ, Tan DJH, Danpanichkul P, et al. The Global Burden of Cirrhosis and Other Chronic Liver Diseases in 2021. Liver Int 2025;45:e70001.
2. Åberg F, Asteljoki J, Männistö V, et al. Combined use of the CLivD score and FIB-4 for prediction of liver-related outcomes in the population. Hepatol Baltim Md 2024;80:163–172.
3. Innes H, Morling JR, Buch S, et al. Performance of routine risk scores for predicting cirrhosis-related morbidity in the community. J Hepatol 2022;77:365–376.
4. Karlsen TH, Sheron N, Zelber-Sagi S, et al. The EASL–Lancet Liver Commission: protecting the next generation of Europeans against liver disease complications and premature mortality. The Lancet 2022;399:61–116.
5. Serra-Burriel M, Juanola A, Serra-Burriel F, et al. Development, validation, and prognostic evaluation of a risk score for long-term liver-related outcomes in the general population: a multicohort study. Lancet Lond Engl 2023;402:988–996.