Skip to main content
SpringerLink
Log in

Machine learning improves prediction of severity and outcomes of acute pancreatitis: a prospective multi-center cohort study

  • Letter to the Editor
  • Published:
Science China Life Sciences Aims and scope Submit manuscript

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

References

  • Banks, P.A., Bollen, T.L., Dervenis, C., Gooszen, H.G., Johnson, C.D., Sarr, M.G., Tsiotos, G.G., and Vege, S.S. (2013). Classification of acute pancreatitis—2012: revision of the Atlanta classification and definitions by international consensus. Gut 62, 102–111.

    Article  PubMed  Google Scholar 

  • Kiss, S., Pintér, J., Molontay, R., Nagy, M., Farkas, N., Sipos, Z., Fehérvári, P., Pecze, L., Földi, M., Vincze, Á., et al. (2022). Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases. Sci Rep 12, 7827.

    Article  PubMed  PubMed Central  Google Scholar 

  • Lan, L., Guo, Q., Zhang, Z., Zhao, W., Yang, X., Lu, H., Zhou, Z., and Zhou, X. (2020). Classification of infected necrotizing pancreatitis for surgery within or beyond 4 weeks using machine learning. Front Bioeng Biotechnol 8, 541.

    Article  PubMed  PubMed Central  Google Scholar 

  • Mashayekhi, R., Parekh, V.S., Faghih, M., Singh, V.K., Jacobs, M.A., and Zaheer, A. (2020). Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis. Eur J Radiol 123, 108778.

    Article  PubMed  Google Scholar 

  • Mederos, M.A., Reber, H.A., and Girgis, M.D. (2021). Acute pancreatitis: a review. JAMA 325, 382–390.

    Article  CAS  PubMed  Google Scholar 

  • Mofidi, R., Duff, M.D., Madhavan, K.K., Garden, O.J., and Parks, R.W. (2007). Identification of severe acute pancreatitis using an artificial neural network. Surgery 141, 59–66.

    Article  PubMed  Google Scholar 

  • Shi, N., Lan, L., Luo, J., Zhu, P., Ward, T.R.W., Szatmary, P., Sutton, R., Huang, W., Windsor, J.A., Zhou, X., et al. (2022). Predicting the need for therapeutic intervention and mortality in acute pancreatitis: a two-center international study using machine learning. J Pers Med 12, 616.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Thapa, R., Iqbal, Z., Garikipati, A., Siefkas, A., Hoffman, J., Mao, Q., and Das, R. (2022). Early prediction of severe acute pancreatitis using machine learning. Pancreatology 22, 43–50.

    Article  PubMed  Google Scholar 

  • Xu, F., Chen, X., Li, C., Liu, J., Qiu, Q., He, M., Xiao, J., Liu, Z., Ji, B., Chen, D., et al. (2021). Prediction of multiple organ failure complicated by moderately severe or severe acute pancreatitis based on machine learning: a multicenter cohort study. Mediators Inflamm 2021, 5525118.

    Article  PubMed  PubMed Central  Google Scholar 

  • Yu, S., Wu, D., Jin, K., Yin, L., Fu, Y., Liu, D., Zhang, L., Yu, X., and Xu, J. (2019). Low serum ionized calcium, elevated high-sensitivity C-reactive protein, neutrophil-lymphocyte ratio, and body mass index (BMI) are risk factors for severe acute pancreatitis in patients with hypertriglyceridemia pancreatitis. Med Sci Monit 25, 6097–6103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (32170788), the National High Level Hospital Clinical Research Funding (2022-PUMCH-B-023), the National Key Clinical Specialty Construction Project (ZK108000) and Beijing Natural Science Foundation (7232123).

Author information

Author notes

  1. Contributed equally to this work

Authors and Affiliations

  1. Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China

    Jia-Ning Li & Dong Wu

  2. Department of Gastroenterology, General Hospital of Western Theater Command, Chengdu, 610083, China

    Dong Mu

  3. Department of Gastroenterology, West China Longquan Hospital Sichuan University, Chengdu, 610100, China

    Shi-Cheng Zheng

  4. Department of Gastroenterology, Fangshan Liangxiang Hospital of Beijing, Beijing, 102400, China

    Wei Tian

  5. Department of Gastroenterology, The Sixth Hospital of Beijing, Beijing, 100048, China

    Zuo-Yan Wu

  6. Department of Gastroenterology, Affiliated Hospital of Hebei University, Baoding, 071030, China

    Jie Meng

  7. Department of Gastroenterology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China

    Rui-Feng Wang

  8. Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221004, China

    Tian-Lei Zheng

  9. Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China

    Yue-Lun Zhang

  10. Surgical and Translational Research Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland, Auckland, 1142, New Zealand

    John Windsor

  11. Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225003, China

    Guo-Tao Lu

  12. State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China

    Dong Wu

Authors
  1. Jia-Ning Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dong Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shi-Cheng Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wei Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zuo-Yan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jie Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rui-Feng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tian-Lei Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yue-Lun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. John Windsor
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Guo-Tao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Dong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Guo-Tao Lu or Dong Wu.

Ethics declarations

Compliance and ethics The author(s) declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, JN., Mu, D., Zheng, SC. et al. Machine learning improves prediction of severity and outcomes of acute pancreatitis: a prospective multi-center cohort study. Sci. China Life Sci. 66, 1934–1937 (2023). https://doi.org/10.1007/s11427-022-2333-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11427-022-2333-8

Search

Navigation