The milestone for preventing post-ERCP pancreatitis using novel simplified predictive scoring system: a propensity score analysis

Chiba, Masafumi; Kato, Masayuki; Kinoshita, Y; Shimamoto, Nana; Tomita, Youichi; Abe, Takahiro; Kanazawa, Keisuke; Tsukinaga, Shintaro; Nakano, Masanori; Torisu, Yuichi; Toyoizumi, Hirobumi; Sumiyama, Kazuki

doi:10.1007/s00464-020-08173-4

Cited by 17 publications

(16 citation statements)

References 33 publications

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“…Widely used statistical methods, such as linear fitting and multiple regression, have been recently applied to predict the probabilities of stone recurrence or development of PEP. However, these studies tended to select fewer easy and feasible risk factors for clinical evaluation, and this could be a potential reason for the low reported accuracy of their models [25][26][27]. The advances in algorithms, such as recurrent neural networks and multimodal learning, and the availability of computing power may allow multimodal information to be integrated to train an AI model to predict subsequent recurrence and complications, and ultimately guide the endoscopist in selecting a strategy that will minimize these adverse clinical outcomes.…”

Section: Discussionmentioning

confidence: 99%

An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation

Huang

Chen

et al. 2022

Endoscopy

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Background: A computer-assisted (CAD) system was developed to assess and score the technical difficulty factors to classify the technical difficult degree of common bile duct (CBD) stones removal during Endoscopic Retrograde Cholangiopancreatography (ERCP). Subsequently, the efficacy of the CAD system was assessed through a multi-center, prospective, observational study. Method: All patients who met the inclusion criteria were included in this study. Based on cholangiogram images, the CAD system analyzed the extent of difficulty in stone removal and classified it into difficult and easy groups. Further, we compared the differences in clinical endpoints, including attempts of stone extraction, stone extraction time, the whole operation time, and stone clearance rates between the two groups. Results: In total, 173 patients with CBD stones from three hospitals were included in the study. Consequently, the difficult group had more extraction attempts (7.20 vs. 4.20, P<0.001), more frequent machine lithotripsy (30.43% vs. 7.09%, P<0.001), longer stone extraction time (16.59min vs. 7.65min, P<0.001), lower single-session stone clearance rate (73.91 % vs. 94.49 %, P<0.001) and lower total stone clearance rate (87.13% vs. 97.64%, P=0.019) compared to the easy group. Conclusion: The CAD system can effectively assess and classify the extent of technical difficulty in endoscopic stone extraction during ERCP. Besides, it automatically provides a quantitative evaluation of CBD and stones, which in turn would help endoscopists to apply suitable procedures and interventional methods to minimize the possible risks of endoscopic stone removal.

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Section: Discussionmentioning

confidence: 99%

An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation

Huang

Chen

et al. 2022

Endoscopy

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“…Thus, a comprehensive diagnosis in combination with other modalities is required in clinical practice, thereby providing awareness regarding atypical contrast patterns. Moreover, pathological diagnosis using the more invasive EUS-FNA or endoscopic retrograde cholangiopancreatography may be strongly considered as a next step if the malignant biliopancreatic disease is suspected in the presence of atypical contrast patterns [ 10 , 11 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Dilemma of Biliopancreatic Contrast-Enhanced Harmonic Endoscopic Ultrasonography

et al. 2022

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Background: The utility of contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) alone in the biliopancreatic region appears to be limited because it is highly dependent on the experience and skill of the endoscopist. Therefore, the present study aimed to validate the efficacy of CH-EUS in clinical practice. Methods: Between January 2018 and March 2019, 301 consecutive patients who underwent CH-EUS were prospectively enrolled in this study. The diagnostic performance of CH-EUS was compared with that of dynamic computed tomography (CT), magnetic resonance imaging (MRI), and all combinations (i.e., CH-EUS, dynamic CT, and MRI) using a Bonferroni correction. A multiple logistic regression analysis was performed to extract each disease that allowed the CH-EUS diagnosis to be consistent with the final diagnosis. Results: In multiple comparisons of diagnostic performance, no significant differences were observed among dynamic CT, MRI, and CH-EUS (p = 1.00), but the diagnostic performance was significantly higher when all modalities were combined (p < 0.001). Moreover, only intraductal papillary mucinous neoplasm comprising adenoma or carcinoma (IPMN, n = 161) showed significance with respect to the agreement with the final diagnosis (p = 0.006). Conclusions: Our results showed that CH-EUS-based diagnosis of IPMN may be possible in clinical practice. On the contrary, to accurately diagnose biliopancreatic diseases other than IPMN, comprehensive diagnosis using multiple modalities may be necessary, rather than relying on CH-EUS alone.

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“…Chiba et al . proposed a prediction model using a propensity score analysis 14 . Their model consisted of five factors: intact papilla, PGW‐assisted biliary cannulation, difficult cannulation, pancreatic injection, and the absence of a pancreatic stent, four of which are also examined in our model.…”

Section: Discussionmentioning

confidence: 99%

“…proposed a prediction model using a propensity score analysis. 14 Their model consisted of five factors: intact papilla, PGW‐assisted biliary cannulation, difficult cannulation, pancreatic injection, and the absence of a pancreatic stent, four of which are also examined in our model. Therefore, intact papilla, PGW‐assisted biliary cannulation, difficult cannulation, and pancreatic injection are regarded as key factors for predicting PEP.…”

Section: Discussionmentioning

confidence: 99%

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New practical scoring system to predict post‐endoscopic retrograde cholangiopancreatography pancreatitis: Development and validation

et al. 2021

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Background and Aim Post‐endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is the most serious adverse event of ERCP. Therefore, it is important to identify high‐risk patients who require prophylactic measures. The aim of this study was to develop a practical prediction model for PEP that identifies high‐risk patients. Methods Patients who underwent ERCP at three tertiary hospitals between April 2010 and September 2012 were enrolled. The dataset was divided into a training set (two centers) and validation set (one center). Using the training set, univariable and multivariable analyses were performed to identify predictive factors for PEP. We constructed a scoring system and evaluated reproducibility using the validation set. Results A total of 2719 ERCPs were analyzed. PEP developed in 133 cases (4.9%). Risk factors (adjusted odds ratios [OR]) in the training set were a history of PEP (OR: 4.2), intact papilla (OR: 2.4), difficult cannulation (OR: 1.9), pancreatic guidewire‐assisted biliary cannulation (OR: 2.2), pancreatic injection (OR: 2.1), pancreatic intraductal ultrasonography (IDUS)/sampling from the pancreatic duct (OR: 2.2), and biliary IDUS/sampling from the biliary tract (OR: 2.8). A scoring system was constructed using these seven clinical variables. The areas under the receiver‐operating characteristic curve of this model were 0.799 in the training set and 0.791 in the validation set. In the high‐risk group at a score of 3 or higher, the incidence of PEP was 13.4%, and all severe/fatal cases were in the high‐risk group. Conclusions This scoring system helps to predict each patient's risk and select preventive measures.

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The milestone for preventing post-ERCP pancreatitis using novel simplified predictive scoring system: a propensity score analysis

Cited by 17 publications

References 33 publications

An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation

An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation

Diagnostic Dilemma of Biliopancreatic Contrast-Enhanced Harmonic Endoscopic Ultrasonography

New practical scoring system to predict post‐endoscopic retrograde cholangiopancreatography pancreatitis: Development and validation

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