Skip to main content
Log in

Prediction of post-operative acute pancreatitis in children with pancreaticobiliary maljunction using machine learning model

  • Original Article
  • Published:
Pediatric Surgery International Aims and scope Submit manuscript

Abstract

Purpose

This study aimed to develop a prediction model to identify risk factors for post-operative acute pancreatitis (POAP) in children with pancreaticobiliary maljunction (PBM) by pre-operative analysis of patient variables.

Methods

Logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were established using the prospectively collected databases of patients with PBM undergoing surgery which was reviewed in the period comprised between August 2015 and August 2022, at the Children’s Hospital of Soochow University. Primarily, the area beneath the receiver-operating curves (AUC), accuracy, sensitivity, and specificity were used to evaluate the model performance. The model was finally validated using the nomogram and clinical impact curve.

Results

In total, 111 children with PBM met the inclusion criteria, and 21 children suffered POAP. In the validation dataset, LR models showed the highest performance. The risk nomogram and clinical effect curve demonstrated that the LR model was highly predictive.

Conclusion

The prediction model based on the LR with a nomogram could be used to predict the risk of POAP in patients with PBM. Protein plugs, age, white blood cell count, and common bile duct diameter were the most relevant contributing factors to the models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Kamisawa T, Ando H, Hamada Y, Fujii H, Koshinaga T, Urushihara N, Itoi T, Shimada H (2014) Diagnostic criteria for pancreaticobiliary maljunction 2013. J Hepatobiliary Pancreat Sci 21:159–161. https://doi.org/10.1002/jhbp.57

    Article  PubMed  Google Scholar 

  2. Shimotakahara A, Yamataka A, Yanai T, Kobayashi H, Okazaki T, Lane GJ, Miyano T (2005) Roux-en-Y hepaticojejunostomy or hepaticoduodenostomy for biliary reconstruction during the surgical treatment of choledochal cyst: which is better? Pediatr Surg Int 21:5–7. https://doi.org/10.1007/s00383-004-1252-1

    Article  PubMed  Google Scholar 

  3. Shi LB, Peng SY, Meng XK, Peng CH, Liu YB, Chen XP, Ji ZL, Yang DT, Chen HR (2001) Diagnosis and treatment of congenital choledochal cyst: 20 years’ experience in China. World J Gastroenterol 7:732–734. https://doi.org/10.3748/wjg.v7.i5.732

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Li L, Feng W, Jing-Bo F, Qi-Zhi Y, Gang L, Liu-Ming H, Yu L, Jun J, Ping W (2004) Laparoscopic-assisted total cyst excision of choledochal cyst and Roux-en-Y hepatoenterostomy. J Pediatr Surg 39:1663–1666. https://doi.org/10.1016/j.jpedsurg.2004.07.01

    Article  PubMed  Google Scholar 

  5. Roberts SE, Morrison-Rees S, John A, Williams JG, Brown TH, Samuel DG (2017) The incidence and aetiology of acute pancreatitis across Europe. Pancreatology 17:155–165. https://doi.org/10.1016/j.pan.2017.01.005

    Article  PubMed  Google Scholar 

  6. Deo RC (2015) Machine learning in medicine. Circulation 132:1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593

    Article  PubMed  PubMed Central  Google Scholar 

  7. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2(4):230–243. https://doi.org/10.1136/svn-2017-000101

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380:1347–1358. https://doi.org/10.1056/NEJMra1814259

    Article  PubMed  Google Scholar 

  9. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  CAS  PubMed  Google Scholar 

  10. Han X, Geng J, Zhang XX, Zhao L, Wang J, Guo WL (2022) Using machine learning models to predict acute pancreatitis in children with pancreaticobiliary maljunction. Surg Today. https://doi.org/10.1007/s00595-022-02571-y

    Article  PubMed  Google Scholar 

  11. Todani T, Watanabe Y, Narusue M, Tabuchi K, Okajima K (1977) Congenital bile duct cysts: classification, operative procedures, and review of thirty-seven cases including cancer arising from choledochal cyst. Am J Surg 134:263–269. https://doi.org/10.1016/0002-9610(77)90359-2

    Article  CAS  PubMed  Google Scholar 

  12. Morinville VD, Husain SZ, Bai H, Barth B, Alhosh R, Durie PR, Freedman SD, Himes R, Lowe ME, Pohl J et al (2012) Definitions of pediatric pancreatitis and survey of present clinical practices. J Pediatr Gastroenterol Nutr 55:261–265. https://doi.org/10.1097/MPG.0b013e31824f1516

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yuan KC, Tsai LW, Lee KH, Cheng YW, Hsu SC, Lo YS, Chen RJ (2020) The development anartificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. Int J Med Inform 141:104176. https://doi.org/10.1016/j.ijmednf.2020.104176

    Article  PubMed  Google Scholar 

  14. Sidey-Gibbons J, Sidey-Gibbons CJ (2019) Machine learning in medicine: a practical introduction. BMC Med Res Methodol 19:64. https://doi.org/10.1186/s12874-019-0681-4

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yamataka A, Ohshiro K, Okada Y, Hosoda Y, Fujiwara T, Kohno S, Sunagawa M, Futagawa S, Sakakibara N, Miyano T (1997) Complications after cyst excision with hepaticoenterostomy for choledochal cysts and their surgical management in children versus adults. J Pediatr Surg 32:1097–1102. https://doi.org/10.1016/s0022-3468(97)90407-3

    Article  CAS  PubMed  Google Scholar 

  16. Miyano T, Yamataka A, Kato Y, Segawa O, Lane G, Takamizawa S, Kohno S, Fujiwara T (1996) Hepaticoenterostomy after excision of choledochal cyst in children: a 30-year experience with 180 cases. J Pediatr Surg 31:1417–1421. https://doi.org/10.1016/s0022-3468(96)90843-x

    Article  CAS  PubMed  Google Scholar 

  17. Gross V, Leser HG, Heinisch A, Schölmerich J (1993) Inflammatory mediators and cytokines–new aspects of the pathophysiology and assessment of severity of acute pancreatitis? Hepatogastroenterology 40:522–530. https://doi.org/10.1055/s-2008-1066049

    Article  CAS  PubMed  Google Scholar 

  18. Afzal S, Kleinhenz J (2021) Acute pancreatitis in children. Pediatr Ann 50:e330–e335. https://doi.org/10.3928/19382359-20210713-01

    Article  PubMed  Google Scholar 

  19. Qiu Q, Nian YJ, Guo Y, Tang L, Lu N, Wen LZ, Wang B, Chen DF, Liu KJ (2019) Development and validation of three machine-learning models for predicting multiple organ failure in moderately severe and severe acute pancreatitis. BMC Gastroenterol 19:118. https://doi.org/10.1186/s12876-019-1016-y

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lan L, Guo Q, Zhang Z, Zhao W, Yang X, Lu H, Zhou Z, Zhou X (2020) Classification of infected necrotizing pancreatitis for surgery within or beyond 4 weeks using machine learning. Front Bioeng Biotechnol 8:541. https://doi.org/10.3389/fbioe.2020.00541

    Article  PubMed  PubMed Central  Google Scholar 

  21. Diao M, Li L, Li Q, Ye M, Cheng W (2014) Challenges and strategies for single-incision laparoscopic Roux-en-Y hepaticojejunostomy in managing giant choledochal cysts. Int J Surg 12:412–417. https://doi.org/10.1016/j.ijsu.2014.03.007

    Article  PubMed  Google Scholar 

  22. Wu S, Wu H, Xu G, Zhao Y, Xue F, Dong S, Han L, Wang Z, Wu Z (2022) Risk factors and clinical impacts of post-pancreatectomy acute pancreatitis after pancreaticoduodenectomy: a single-center retrospective analysis of 298 patients based on the ISGPS definition and grading system. Front Surg 9:916486. https://doi.org/10.3389/fsurg.2022.916486

    Article  PubMed  PubMed Central  Google Scholar 

  23. Li SL, Zhang DR, Li YC, Li ZD, Niu AG (2000) Prevention and treatment for pancreatic duct injury during the excision of choledochal cyst. Chin J Pediatr Surg 21:211–213. https://doi.org/10.3760/cma.j.issn.0253-3006.2000.04.007

    Article  Google Scholar 

  24. Czerwonko ME, Pekolj J, Uad P, Mazza O, Sanchez-Claria R, Arbues G, de Santibañes E, de Santibañes M, Palavecino M (2018) Acute pancreatitis after laparoscopic transcystic common bile duct exploration: an analysis of predisposing factors in 447 patients. World J Surg 42:3134–3142. https://doi.org/10.1007/s00268-018-4611-0

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was partially supported by National Natural Science Foundation of China (No. 81971685), Scientific Research Project of Jiangsu Provincial Health Commission (No. ZD2022015), Science and Technology Development Project of Suzhou (SKY2022054), Suzhou Clinical Medical Center (SZLCYXZX202104), and Suzhou Key Discipline of Medicine (SZXK202105).

Funding

This work was supported by the National Natural Science Foundation of China, under Grant No. 81971685.

Author information

Authors and Affiliations

Authors

Contributions

WG Study conception and design. TC, YY and HM Data acquisition. SH, TC and WG Analysis and data interpretation. TC, HM, YY and SH Drafting of the manuscript. TC and WG Critical revision.

Corresponding author

Correspondence to Wan-liang Guo.

Ethics declarations

Conflict of interest

The authors report no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, Tn., Huang, Sg., Yang, Y. et al. Prediction of post-operative acute pancreatitis in children with pancreaticobiliary maljunction using machine learning model. Pediatr Surg Int 39, 158 (2023). https://doi.org/10.1007/s00383-023-05441-x

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00383-023-05441-x

Keywords

Navigation