Performance and external validation of prediction models for gangrenous cholecystitis in emergency surgical patients: a systematic review
DOI:
https://doi.org/10.65759/h5kgtv41Keywords:
Gangrenous cholecystitis, acute cholecystitis, prediction models, external validation, emergency surgeryAbstract
Background: Gangrenous cholecystitis complicates emergency surgical assessment because routine clinical findings overlap with nongangrenous inflammation. Preoperative prediction offers a basis for risk stratificati n and operative planning. This review evaluated model performance and the strength of external validation. Methods: Two authors searched PubMed, Scopus, Web of Science, and the Cochrane Library through Jan 2026, screened studies, and extracted data. Original multivariable development and validation studies using operative or pathological gangrene outcomes were synthesized narratively. Discrimination, threshold accuracy, calibration, and validation design were examined separately. Results: eleven original articles described clinical equations, bedside scores, imaging approaches, and machine learning. Independent evaluations of established scores yielded areas under the receiver operating characteristic curve of 0.72-0.78. A six-factor clinicoradiological score reported an area of 0.83, although cohort-specific validation reporting required interpretation. A random forest achieved 0.818 in another-centre validation, while a fusion computed tomography model achieved 0.879 and 0.887 in two external cohorts. Same-centre temporal testing was distinguished from geographical validation. Conclusions: External validation supports several approaches, without establishing a universally transferable tool or improved clinical outcomes. Representative multicentre evaluation, transparent calibration, and prospective assessment of model-guided decisions remain priorities for emergency surgical implementation.
References
1. Yokoe M, Hata J, Takada T, Strasberg SM, Asbun HJ, Wakabayashi G, et al. Tokyo Guidelines 2018: diagnostic criteria and severity grading of acute cholecystitis (with videos). J Hepatobiliary Pancreat Sci. 2018;25(1):41-54. doi:10.1002/jhbp.515.
2. Pisano M, Allievi N, Gurusamy K, Borzellino G, Cimbanassi S, Boerna D, et al. 2020 World Society of Emergency Surgery updated guidelines for the diagnosis and treatment of acute calculus cholecystitis. World J Emerg Surg. 2020;15:61. doi:10.1186/s13017-020-00336-x.
3. Okamoto K, Suzuki K, Takada T, Strasberg SM, Asbun HJ, Endo I, et al. Tokyo Guidelines 2018: flowchart for the management of acute cholecystitis. J Hepatobiliary Pancreat Sci. 2018;25(1):55-72. doi:10.1002/jhbp.516.
4. Gutt CN, Encke J, Köninger J, Harnoss JC, Weigand K, Kipfmüller K, et al. Acute cholecystitis: early versus delayed cholecystectomy, a multicenter randomized trial (ACDC study, NCT00447304). Ann Surg. 2013;258(3):385-93. doi:10.1097/SLA.0b013e3182a1599b.
5. Gurusamy KS, Davidson C, Gluud C, Davidson BR. Early versus delayed laparoscopic cholecystectomy for people with acute cholecystitis. Cochrane Database Syst Rev. 2013;(6). doi:10.1002/14651858.CD005440.pub3.
6. Merriam LT, Kanaan SA, Dawes LG, Angelos P, Prystowsky JB, Rege RV, et al. Gangrenous cholecystitis: analysis of risk factors and experience with laparoscopic cholecystectomy. Surgery. 1999;126(4):680-6.
7. Fagan SP, Awad SS, Rahwan K, Hira K, Aoki N, Itani KMF, et al. Prognostic factors for the development of gangrenous cholecystitis. Am J Surg. 2003;186(5):481-5. doi:10.1016/j.amjsurg.2003.08.001.
8. Bennett GL, Rusinek H, Lisi V, Israel GM, Krinsky GA, Slywotzky CM, et al. CT findings in acute gangrenous cholecystitis. AJR Am J Roentgenol. 2002;178(2):275-81. doi:10.2214/ajr.178.2.1780275.
9. Nguyen L, Fagan SP, Lee TC, Aoki N, Itani KMF, Berger DH, et al. Use of a predictive equation for diagnosis of acute gangrenous cholecystitis. Am J Surg. 2004;188(5):463-6. doi:10.1016/j.amjsurg.2004.07.013.
10. Yacoub WN, Petrosyan M, Sehgal I, Ma Y, Chandrasoma P, Mason RJ. Prediction of patients with acute cholecystitis requiring emergent cholecystectomy: a simple score. Gastroenterol Res Pract. 2010;2010:901739. doi:10.1155/2010/901739.
11. Falor AE, Zobel M, Kaji A, Neville A, De Virgilio C. Admission variables predictive of gangrenous cholecystitis. Am Surg. 2012;78(10):1075-8. doi:10.1177/000313481207801014.
12. Wu B, Buddensick TJ, Ferdosi H, Narducci DM, Sautter A, Setiawan L, et al. Predicting gangrenous cholecystitis. HPB (Oxford). 2014;16(9):801-6. doi:10.1111/hpb.12226.
13. Chang WC, Sun Y, Wu EH, Kim SY, Wang ZJ, Huang GS, et al. CT findings for detecting the presence of gangrenous ischemia in cholecystitis. AJR Am J Roentgenol. 2016;207(2):302-9. doi:10.2214/AJR.15.15658.
14. Mangukiya DO, Bhatt KS, Desai PN, Arora PV, Patel KB, Parekh KK, et al. Predictive models of gangrenous cholecystitis in laparoscopic cholecystectomy treated patients. J Clin Diagn Res. 2019;13(9)-3. doi:10.7860/JCDR/2019/42231.13137.
15. Bouassida M, Madhioub M, Kallel Y, Zribi S, Slama H, Mighri MM, et al. Acute gangrenous cholecystitis: proposal of a score and comparison with previous published scores. J Gastrointest Surg. 2021;25(6):1479-86. doi:10.1007/s11605-020-04707-2.
16. Ma Y, Luo M, Guan G, Liu X, Cui X, Luo F. An explainable predictive machine learning model of gangrenous cholecystitis based on clinical data: a retrospective single center study. World J Emerg Surg. 2025;20:1. doi:10.1186/s13017-024-00571-6.
17. Guo Q, Li Y, Huang Y, Xie S, Li S, Zheng J, et al. Self-supervised learning model integrates plain and contrast-enhanced CT for preoperatively identifying gangrenous cholecystitis: a multicenter retrospective cohort study. Int J Surg. 2025;111(11):7622-32. doi:10.1097/JS9.0000000000003063.
18. Hu Y, Chen Y, Zhao H. Development and validation of an explainable machine learning model for gangrenous cholecystitis prediction: a multicenter retrospective study. J Inflamm Res. 2025;18:17747-58. doi:10.2147/JIR.S552926.
19. Moons KGM, Damen JAA, Kaul T, Hooft L, Andaur Navarro C, Dhiman P, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025;388. doi:10.1136/bmj-2024-082505.
20. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17:230. doi:10.1186/s12916-019-1466-7.
21. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26(6):565-74. doi:10.1177/0272989X06295361.
22. Riley RD, Archer L, Snell KIE, Ensor J, Dhiman P, Martin GP, et al. Evaluation of clinical prediction models (part 2): how to undertake an external validation study. BMJ. 2024;384. doi:10.1136/bmj-2023-074820.
23. Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385. doi:10.1136/bmj-2023-078378.
