Evidence map›Paper›PMID 33209879›Full record

ArticleAnnals of translational medicine2020

Development and validation of a risk prediction model and scoring system for post-endoscopic retrograde cholangiopancreatography pancreatitis.

Ruhua Zheng, Mengjie Chen, Xuying Wang, Bingqiang Li, Ting He, Lei Wang, Guifang Xu, Yuling Yao, Jun Cao, Yonghua Shen and 6 more

Abstract read
In one paragraph

Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Trial
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  8. Review
  9. Article
  10. Predictive models for post-ERCP pancreatitis: a systematic review and meta-analysis.Frontiers in gastroenterology (Lausanne, Switzerland) · 2025
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Ruhua ZhengDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Mengjie ChenDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Xuying WangDepartment of Artificial intelligence, Ewell Technology Enterprise Institute, Hangzhou, China.
Bingqiang LiDepartment of Artificial intelligence, Ewell Technology Enterprise Institute, Hangzhou, China.
Ting HeDepartment of Artificial intelligence, Ewell Technology Enterprise Institute, Hangzhou, China.
Lei WangDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Guifang XuDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Yuling YaoDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Jun CaoDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Yonghua ShenDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Yi WangDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Hao ZhuDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Bin ZhangDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Han WuDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Xiaoping ZouDepartment of Gastroenterology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.
Guoping HeDepartment of Artificial intelligence, Ewell Technology Enterprise Institute, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA few models have been proposed for the prediction of the risk of post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP), but many include factors that are not assessed routinely. Herein, we intend to develop and validate a predictive model for the occurrence of PEP.

methodsData of patients who underwent endoscopic retrograde cholangiopancreatography (ERCP) from January 01, 2016 to May 16, 2019 (training set and internal test set) and from May 17, 2019 to December 25, 2019 (external test set) were retrospectively collected. The performance of the model was validated in the two validation cohorts.

resultsA total of 342 patients were included for the external test set, and 47 (13.7%) developed PEP. The variables included in the scoring system were gastrectomy history, high direct bilirubin (DBIL), high albumin (ALB), villous type of papillary orifice, nodular type of papillary orifice, pancreatic guidewire passages, precut sphincterotomy, and high operator experience. A total score >5 indicated high risk. In the external test set, the area under the curve (AUC) was 0.718, the sensitivity was 0.723, and the specificity was 0.676. In the external test set, the probability of PEP was 6.1%, 17.0%, and 37.5% in patients with low (<0), moderate (0-5), and high (>5) risk scores, respectively.

conclusionsThis study established a scoring system for predicting the risk of PEP using routinely measured clinical variables. Its application in routine work warrants further investigation.

Indexed as

Cholangiopancreatographyendoscopic retrogrademodelspancreatitisrisk factorsstatistical

Identifiers

PMID33209879
PMCPMC7661903

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.