Predictors of adverse events after endoscopic ultrasound-guided through-the-needle biopsy of pancreatic cysts: a recursive partitioning analysis

Abstract

Background and study aims Endoscopic ultrasound-guided through-the-needle biopsy (TTNB) of pancreatic cystic lesions (PCLs) is associated with a non-negligible risk for adverse events (AEs). We aimed to identify the hierarchic interaction among independent predictors for TTNB-related AEs and to generate a prognostic model using recursive partitioning analysis (RPA).

Patients and methods Multicenter retrospective analysis of 506 patients with PCLs who underwent TTNB. RPA of predictors for AEs was performed and the model was validated by means of bootstrap resampling.

Results Mean cysts size was 36.7 mm. Most common diagnoses were intraductal papillary mucinous neoplasm (IPMN, 45 %), serous cystadenoma (18.8 %), and mucinous cystadenoma (12.8 %). Fifty-eight (11.5 %) AEs were observed. At multivariate analysis, age (odds ratio [OR] 1.32, 1.09–2.14; p = 0.05), number of TTNB passes (OR from 2.17, 1.32–4.34 to OR 3.16, 2.03–6.34 with the increase of the number of passes), complete aspiration of the cyst (OR 0.56, 0.31–0.95; p = 0.02), and diagnosis of IPMN (OR 4.16, 2.27–7.69; p < 0.001) were found to be independent predictors of AEs, as confirmed by logistic regression and random forest analyses. RPA identified three risk classes: high-risk (IPMN sampled with multiple microforceps passes, 28 % AEs rate), low-risk (1.4 % AE rate, including patients < 64 years with other-than-IPMN diagnosis sampled with ≤ 2 microforceps passes and with complete aspiration of the cyst) and middle-risk class (6.1 % AEs rate, including the remaining patients).

Conclusion TTNB should be selectively used in the evaluation of patients with IPMN. The present model could be applied during patient selection as to optimize the benefit/risk of TTNB.

• References

• 1 Stark A, Donahue TR, Reber HA. et al. Pancreatic cyst disease: a review. JAMA 2016; 315: 1882-1893
• 2 Crinò SF, Bernardoni L, Gabbrielli A. et al. Beyond pancreatic cyst epithelium: evidence of ovarian-like stroma in EUS-guided through-the-needle micro-forceps biopsy specimens. Am J Gastroenterol 2018; 113: 1059-1060
• 3 Facciorusso A, Del Prete V, Antonino M. et al. Diagnostic yield of EUS-guided through-the-needle biopsy in pancreatic cysts: a meta-analysis. Gastrointest Endosc 2020; 92: 1-8
• 4 Tacelli M, Celsa C, Magro B. et al. Diagnostic performance of endoscopic ultrasound through-the-needle microforceps biopsy of pancreatic cystic lesions: Systematic review with meta-analysis. Dig Endosc 2020; 32: 1018-1030
• 5 Kovacevic B, Klausen P, Rift CV. et al. Clinical impact of endoscopic ultrasound-guided through-the-needle microbiopsy in patients with pancreatic cysts. Endoscopy 2021; 53: 44-52
• 6 Crinò SF, Bernardoni L, Brozzi L. et al. Association between macroscopically visible tissue samples and diagnostic accuracy of EUS-guided through-the-needle microforceps biopsy sampling of pancreatic cystic lesions. Gastrointest Endosc 2019; 90: 933-943
• 7 Yang D, Trindade AJ, Yachimski P. et al. Histologic analysis of endoscopic ultrasound-guided through the needle microforceps biopsies accurately identifies mucinous pancreas cysts. Clin Gastroenterol Hepatol 2019; 17: 1587-1596
• 8 Barresi L, Crinò SF, Fabbri C. et al. Endoscopic ultrasound-through-the-needle biopsy in pancreatic cystic lesions: A multicenter study. Dig Endosc 2018; 30: 760-770
• 9 Crinò SF. Through-the-needle microforceps biopsy: a powerful tool but for selected patients. Endoscopy 2021; 53: 53-54
• 10 Tanaka M, Fernández-del Castillo C, Adsay V. et al. International consensus guidelines 2012 for the management of IPMN and MCN of the pancreas. Pancreatology 2012; 12: 183-197
• 11 Tanaka M, Fernández-Del Castillo C, Kamisawa T. et al. Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatology 2017; 17: 738-753
• 12 European Study Group on Cystic Tumours of the Pancreas. European evidence-based guidelines on pancreatic cystic neoplasms. Gut 2018; 67: 789-804
• 13 Cotton PB, Eisen GM, Aabakken L. et al. A lexicon for endoscopic adverse events: report of an ASGE workshop. Gastrointest Endosc 2010; 71: 446-454
• 14 Wani S, Muthusamy VR, McGrath CM. et al. AGA White Paper: Optimizing endoscopic ultrasound-guided tissue acquisition and future directions. Clin Gastroenterol Hepatol 2018; 16: 318-327
• 15 Rift CV, Kovacevic B, Toxværd A. et al. EUS-guided through-the-needle biopsy sampling of pancreatic cystic lesions: a pathologist's guide for the endoscopist. Gastrointest Endosc 2020; 92: 252-258
• 16 Cheesman AR, Zhu H, Liao X. et al. Impact of EUS-guided microforceps biopsy sampling and needle-based confocal laser endomicroscopy on the diagnostic yield and clinical management of pancreatic cystic lesions. Gastrointest Endosc 2020; 91: 1095-1104
• 17 Larghi A, Manfrin E, Fabbri C. et al. Interobserver agreement among expert pathologists on through-the-needle microforceps biopsy samples for evaluation of pancreatic cystic lesions. Gastrointest Endosc 2019; 90: 784-792.e4
• 18 Strobl C, Boulesteix AL, Zeileis A. et al. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 2007; 8: 25
• 19 Breiman L. Random forests. Mach Learn 2001; 45: 5-32
• 20 Strobl C, Malley J, Tutz G. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 2009; 14: 323-348
• 21 Steyerberg EW, Harrell Jr. FE, Borsboom GJ. et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001; 54: 774-781
• 22 Hosmer DW, Hosmer T, Le Cessie S. et al. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 1997; 16: 965-980
• 23 Barresi L, Tarantino I, Traina M. et al. Endoscopic ultrasound-guided fine needle aspiration and biopsy using a 22-gauge needle with side fenestration in pancreatic cystic lesions. Dig Liver Dis 2014; 46: 45-50
• 24 Facciorusso A, Licinio R, Di Leo A. Machine learning methods in gastroenterology. Gastroenterology 2015; 149: 1128-1129
• 25 Rodriguez JR, Salvia R, Crippa S. et al. Branch-duct intraductal papillary mucinous neoplasms: observations in 145 patients who underwent resection. Gastroenterology 2007; 133: 72e9
• 26 Schmidt CM, White PB, Waters JA. et al. Intraductal papillary mucinous neoplasms: predictors of malignant and invasive pathology. Ann Surg 2007; 246: 644-651
• 27 Waters JA, Schmidt CM, Pinchot JW. et al. CT vs MRCP: optimal classification of IPMN type and extent. J Gastrointest Surg 2008; 12: 101e9
• 28 Kim JH, Eun HW, Park HJ. et al. Diagnostic performance of MRI and EUS in the differentiation of benign from malignant pancreatic cyst and cyst communication with the main duct. Eur J Radiol 2012; 81: 2927-2935
• 29 Shimizu Y, Hijioka S, Hirono S. et al. New model for predicting malignancy in patients with intraductal papillary mucinous neoplasm. Ann Surg 2020; 272: 155-162
• 30 Attiyeh MA, Fernández-Del Castillo C, Al Efishat M. et al. Development and validation of a multi-institutional preoperative nomogram for predicting grade of dysplasia in intraductal papillary mucinous neoplasms (IPMNs) of the pancreas: a report from The Pancreatic Surgery Consortium. Ann Surg 2018; 267: 157-163
• 31 Cheesman AR, Zhu H, Liao X. et al. Impact of EUS-guided microforceps biopsy sampling and needle-based confocal laser endomicroscopy on the diagnostic yield and clinical management of pancreatic cystic lesions. Gastrointest Endosc 2020; 91: 1095-1104
• 32 Singhi AD, McGrath K, Brand RE. et al. Preoperative next-generation sequencing of pancreatic cyst fluid is highly accurate in cyst classification and detection of advanced neoplasia. Gut 2018; 67: 2131-2141
• 33 Facciorusso A, Mohan BP, Tacelli M. et al. Use of antibiotic prophylaxis is not needed for endoscopic ultrasound-guided fine-needle aspiration of pancreatic cysts: a meta-analysis. Expert Rev Gastroenterol Hepatol 2020; 14: 999-1005