โก Quick Summary
A recent systematic review and meta-analysis examined risk prediction models for post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP), revealing a pooled incidence of 8.48% across 24 studies. The findings highlight the need for improved model calibration and external validation to enhance clinical applicability.
๐ Key Details
- ๐ Dataset: 24 studies, 26 models, nโ=โ38,016 patients
- ๐งฉ Key predictors: Pancreatic duct cannulation, pancreatic injection, previous pancreatitis
- โ๏ธ Methodology: PRISMA 2020-compliant systematic review and meta-analysis
- ๐ Performance metrics: Pooled odds ratio for model performance: 0.81 (AUC range: 0.560-0.915)
๐ Key Takeaways
- ๐ PEP is the most common complication of ERCP, with an incidence of 3.5-9.7% in general populations.
- ๐ก Strong predictors of PEP include pancreatic duct cannulation and previous pancreatitis.
- ๐ฉโ๐ฌ High risk of bias was noted in the analysis and participant domains of the included studies.
- ๐ Model performance showed moderate-to-high discrimination but poor calibration.
- ๐ Future research should follow TRIPOD guidelines and utilize multicenter designs.
- ๐ค Integration of AI with traditional modeling may enhance predictive accuracy.
- ๐ Study period: Included studies published from 2002 to 2024.
- ๐บ๏ธ Geographic focus: Predominantly East Asia, with 16 studies from this region.
๐ Background
Post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP) is a significant complication that can lead to increased morbidity and healthcare costs. Understanding the risk factors associated with PEP is crucial for improving patient outcomes and optimizing clinical practices. Despite the development of various prediction models, their effectiveness and applicability in clinical settings have been inconsistent.
๐๏ธ Study
This systematic review and meta-analysis aimed to evaluate existing multivariable risk prediction models for PEP. The researchers conducted a comprehensive search across nine databases, focusing on studies that developed or validated these models. The analysis included a wide range of patient data and model characteristics, assessing their performance and potential biases.
๐ Results
The analysis included 24 studies with a total of 38,016 patients, revealing a pooled PEP incidence of 8.48%. The strongest predictors identified were pancreatic duct cannulation and pancreatic injection, both with an odds ratio of 3.50. The overall model performance showed a pooled odds ratio of 0.81, indicating moderate-to-high discrimination, although calibration was often inadequately reported.
๐ Impact and Implications
The findings from this study underscore the importance of refining risk prediction models for PEP. By addressing the limitations of current models, such as poor calibration and lack of external validation, healthcare providers can better identify at-risk patients and implement preventive strategies. The integration of advanced technologies like artificial intelligence could further enhance the accuracy and utility of these models in clinical practice.
๐ฎ Conclusion
This systematic review highlights the critical need for improved risk prediction models for PEP. While current models demonstrate potential, their limitations must be addressed to enhance clinical applicability. Future research should focus on robust methodologies and the integration of innovative technologies to improve patient outcomes in the context of ERCP procedures.
๐ฌ Your comments
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Risk prediction model for post-endoscopic retrograde cholangiopancreatography pancreatitis: A systematic review and meta-analysis.
Abstract
BACKGROUND: Post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP) is the most common and clinically significant complication of ERCP, with an incidence of 3.5-9.7% in general populations and up to 14.7% in high-risk groups, leading to considerable morbidity, mortality, and healthcare costs. Although numerous multivariable prediction models have been developed, their predictor sets, methodological rigor, and clinical applicability remain highly variable.
METHOD: We conducted a PRISMA 2020-compliant systematic review and meta-analysis, prospectively registered in PROSPERO (CRD42024556967). Nine databases were searched to June 1, 2024, for studies developing or validating multivariable PEP risk prediction models. Data on study/model characteristics, predictors, and performance metrics were extracted. Risk of bias was assessed with PROBAST, and study quality with the Newcastle-Ottawa Scale. Random-effects meta-analyses pooled (i) PEP incidence, (ii) associations of individual predictors, and (iii) overall model performance.
RESULTS: Twenty-four studies (26 models; nโ=โ38,016) published from 2002-2024 were included, predominantly retrospective cohorts from East Asia (nโ=โ16). The pooled PEP incidence was 8.48% (95% CI: 6.90-10.39%; Iยฒโ=โ96.4%), highest in East Asia and retrospective cohorts. Strongest predictors included pancreatic duct cannulation (OR=3.50), pancreatic injection (OR=3.50), previous pancreatitis (OR=3.32), and pancreatic guidewire use (OR=2.63); additional consistent factors were female sex, difficult cannulation, elevated bilirubin, low albumin, choledocholithiasis, and prolonged procedure time. The pooled odds ratio for model performance was 0.81 (95% CI: 0.78-0.84; Iยฒโ=โ83.5%), with AUCs ranging 0.560-0.915, though calibration was infrequently reported (38%) and external validation undertaken in only 46%. PROBAST indicated high overall risk of bias, chiefly in the analysis (92%) and participants (100%) domains.
CONCLUSION: Current PEP prediction models generally demonstrate moderate-to-high discrimination but are limited by suboptimal calibration, inadequate external validation, and methodological heterogeneity. Future research should adhere to TRIPOD guidelines, employ multicenter large-sample designs, retain continuous predictors, address missing data with robust imputation methods, and conduct comprehensive temporal, geographic, and domain-specific validation. Integration of artificial intelligence/machine learning with conventional modeling and embedding validated tools into clinical workflows may enhance predictive accuracy and real-world utility.
Author: [‘Mao Y’, ‘Liu Q’, ‘Fan H’, ‘He W’, ‘Zhang C’, ‘Ouyang X’, ‘Li E’, ‘Wang X’, ‘Qiu L’, ‘Dong H’]
Journal: PLoS One
Citation: Mao Y, et al. Risk prediction model for post-endoscopic retrograde cholangiopancreatography pancreatitis: A systematic review and meta-analysis. Risk prediction model for post-endoscopic retrograde cholangiopancreatography pancreatitis: A systematic review and meta-analysis. 2025; 20:e0332378. doi: 10.1371/journal.pone.0332378