切换至 "中华医学电子期刊资源库"
高级检索
中华肝脏外科手术学电子杂志

中华肝脏外科手术学电子杂志 ›› 2026, Vol. 15 ›› Issue (05) : 760 -767. doi: 10.3877/cma.j.issn.2095-3232.2026.05.011

临床研究

急性坏疽性胆囊炎临床风险预测模型的建立与验证
王平1, 郝天骄1, 陈兴宇1, 翟俊2, 朱文伟3, 陈季松1,†()   
  1. 1　225300　江苏省泰州市第四人民医院肝胆胰外科
    2　225300　江苏省泰州市第四人民医院病理科
    3　200040　上海，复旦大学附属华山医院普外科肝胆外科中心
  • 收稿日期:2026-05-08 出版日期:2026-10-10
  • 通信作者: 陈季松
  • 基金资助:
    国家自然科学基金面上项目(82472951); 泰州市海陵区科技发展计划项目(HLKF-2023-4)

Development and validation of a clinical risk prediction model for acute gangrenous cholecystitis

Ping Wang1, Tianjiao Hao1, Xingyu Chen1, Jun Zhai2, Wenwei Zhu3, Jisong Chen1,†()   

  1. 1 Department of Hepatobiliary and Pancreatic Surgery, Taizhou Fourth People's Hospital, Taizhou 225300, China
    2 Department of Pathology, Taizhou Fourth People's Hospital, Taizhou 225300, China
    3 Hepatobiliary Surgery Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai 200040, China
  • Received:2026-05-08 Published:2026-10-10
  • Corresponding author: Jisong Chen
全文 ( ) 下载PDF ( ) 导出引用
引用本文：

王平, 郝天骄, 陈兴宇, 翟俊, 朱文伟, 陈季松. 急性坏疽性胆囊炎临床风险预测模型的建立与验证[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(05): 760-767.

Ping Wang, Tianjiao Hao, Xingyu Chen, Jun Zhai, Wenwei Zhu, Jisong Chen. Development and validation of a clinical risk prediction model for acute gangrenous cholecystitis[J/OL]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(05): 760-767.

目的

探讨急性坏疽性胆囊炎（AGC）的临床危险因素，在此基础上构建AGC风险预测模型并进行验证。

方法

回顾性分析2022年1月至2024年6月江苏省泰州市第四人民医院收治的265例急性胆囊炎患者（建模组）的临床资料。患者均签署知情同意书，符合医学伦理学规定。其中男125例，女140例；年龄46~68岁，中位年龄57岁。收集患者围手术期临床病理学资料，进行单因素及Logistic多因素回归分析，并结合术后病理建立AGC风险预测模型；在此基础上，进一步收集2024年7月至2024年12月的108例急性胆囊炎患者（验证组），对构建的预测模型进行验证。

结果

Logistic多因素回归分析显示，男性、高BMI、胆囊积气积液、胆囊结石、结石嵌顿、WBC升高为AGC发生独立危险因素（OR=3.705，1.202，10.005，9.718，3.559，1.239；P<0.05）。构建AGC预测列线图模型，列线图模型分析显示，当总分超过80分时，胆囊坏疽风险显著增加；而当总分攀升至280分时，该风险概率高达99%。经Hosmer-Lemeshow检验显示该模型拟合效果较好。ROC曲线分析显示，建模组与验证组的AGC预测的AUC分别为0.848和0.709，该模型对胆囊坏疽预测效能可靠。通过Hosmer-Lemeshow拟合优度检验对两组数据集分析，校准曲线均与理想曲线高度吻合，显示该模型在预测能力方面表现优异。临床应用数据显示，基于列线图的预测模型在实际临床场景中展现出显著的实用性。

结论

男性、高BMI、胆囊积气积液、胆囊结石、结石嵌顿及WBC升高是AGC的独立危险因素。基于关键临床病理特征构建的AGC预测模型具有较高的诊断价值，可为早期诊断及干预AGC提供简易和便捷的临床工具。

关键词: 急性胆囊炎, 坏疽, 危险因素, 列线图, 预测
Objective

To investigate the clinical risk factors for acute gangrenous cholecystitis (AGC), and to develop and validate an AGC risk prediction model based on these factors.

Methods

Clinical data of 265 patients with acute cholecystitis (modeling cohort) admitted to Taizhou Fourth People's Hospital from January 2022 to June 2024 were retrospectively analyzed. The informed consents of all patients were obtained and the local ethical committee approval was received. Among them, 125 patients were male and 140 female, aged from 46 to 68 years, with a median age of 57 years. Perioperative clinicopathological data were collected and analyzed using univariate and multivariate logistic regression. An AGC risk prediction model was established in combination with postoperative pathological findings. An independent cohort of 108 patients with acute cholecystitis (validation cohort) enrolled from July 2024 to December 2024 was subsequently collected to validate the prediction model.

Results

Multivariate logistic regression analysis showed that male sex, high body mass index (BMI), gas and fluid accumulation in the gallbladder on CT, gallbladder stones, impacted stones, and elevated white blood cell count were independent risk factors for AGC (OR=3.705, 1.202, 10.005, 9.718, 3.559, and 1.239, respectively; P<0.05). An AGC prediction nomogram was constructed. Nomogram analysis showed that the risk of gallbladder gangrene increased significantly when the total score exceeded 80 points and reached 99% when the total score increased to 280 points. The Hosmer-Lemeshow test showed good model fit. Receiver operating characteristic (ROC) curve analysis showed that the areas under the curve (AUC) for predicting AGC were 0.848 in the modeling cohort and 0.709 in the validation cohort, indicating reliable predictive performance for gallbladder gangrene. The Hosmer-Lemeshow goodness-of-fit test was performed for both datasets, and the calibration curves were highly consistent with the ideal curve, demonstrating the good predictive ability of the model. Clinical application data showed that the nomogram-based prediction model had substantial practical utility in real-world clinical settings.

Conclusions

Male sex, high BMI, gas and fluid accumulation in the gallbladder on CT, gallbladder stones, impacted stones, and elevated white blood cell count are independent risk factors for AGC. The prediction model based on key clinicopathological characteristics has high diagnostic value and can provide a simple and convenient clinical tool for the early diagnosis and intervention of AGC.

Key words: Cholecystitis, acute, Gangrene, Risk factor, Nomogram, Prediction
表1 建模组和验证组AC患者基线特征比较
指标 建模组 验证组 统计值 P值
性别(例, 男/女) 125/140 56/52 χ2=0.67 0.412
年龄[岁, M (Q1, Q3)] 57(46, 68) 61(50, 71) Z=-1.19 0.235
BMI(kg/m2,
±s)		 24±4 25±4 t=-0.25 0.803
糖尿病(例) 33 17 χ2=0.71 0.398
发病至手术时间[d, M (Q1, Q3)] 2(1, 5) 2(1, 4) Z=-0.84 0.400
体温[℃, M (Q1, Q3)] 36.5(36.3, 36.6) 36.5(36.4, 36.6) Z=-1.01 0.311
胆囊积气积液(例) 9 5 χ2=0.07 0.789
胆囊结石(例) 234 94 χ2=0.12 0.734
结石颈部嵌顿 (例) 103 42 χ2=0.00 0.997
WBC[×109/L, M (Q1, Q3)] 9.0(6.2, 11.8) 10.0(7.0, 12.1) Z=-1.27 0.203
ALT[U/L, M (Q1, Q3)] 29(20, 57) 28(22, 42) Z=-0.02 0.985
AST[U/L, M (Q1, Q3)] 33(24, 51) 31(27, 37) Z=-1.04 0.297
TB[μmol/L, M (Q1, Q3)] 16(12, 26) 18(13, 28) Z=-1.24 0.216
肌酸激酶[U/L, M (Q1, Q3)] 62(44, 87) 62(49, 91) Z=-1.63 0.103
CA19-9[kU/L, M (Q1, Q3)] 10(5, 18) 10(5, 21) Z=-1.24 0.214
D-二聚体[mg/L, M (Q1, Q3)] 0.4(0.2, 0.8) 0.4(0.3, 1.0) Z=-1.47 0.143
表2 建模组AGC相关因素分析
指标 AGC组 非AGC组 统计值 P值
性别 (例, 男/女) 36/15 89/125 χ2=13.899 <0.001
年龄[岁, M (Q1, Q3)] 57(45, 68) 57(46, 68) Z=-1.867 0.062
BMI(kg/m2,
±s)		 26±3 24±4 t=-4.508 <0.001
糖尿病 (例) 6 27 χ2=0.027 0.868
发病至手术时间[d, M (Q1, Q3)] 2 (1, 5) 2 (1, 5) Z=-0.387 0.699
体温[℃, M (Q1, Q3)] 36.5(36.3, 36.6) 36.5(36.3, 36.6) Z=-0.850 0.396
胆囊积气积液(例) 6 3 χ2=13.480 <0.001
胆囊结石(例) 50 184 χ2=5.797 0.016
结石颈部嵌顿(例) 30 73 χ2=10.585 0.001
WBC[×109/L, M (Q1, Q3)] 12(9, 14) 8(6, 11) Z=-4.919 <0.001
ALT[U/L, M (Q1, Q3)] 29(20, 46) 28(20, 58) Z=-0.241 0.81
AST[U/L, M (Q1, Q3)] 37(30, 50) 32(24, 51) Z=-1.161 0.246
TB[μ mol/L, M (Q1, Q3)] 18(13, 31) 16(12, 24) Z=-1.796 0.072
肌酸激酶[U/L, M (Q1, Q3)] 78(50, 92) 59(43, 78) Z=-2.372 0.018
CA19-9[kU/L, M (Q1, Q3)] 12(6, 22) 9(5, 17) Z=-1.284 0.199
D-二聚体[mg/L, M (Q1, Q3)] 0.5(0.3, 1.0) 0.4(0.2, 0.7) Z=-1.758 0.079
表3 AGC发生因素Logistic多因素回归分析
因素 b SE(b) Wald χ2 P值 OR 95%CI
男性 1.310 0.400 10.694 0.001 3.705 1.690~8.121
BMI 0.184 0.053 12.100 0.001 1.202 1.084~1.333
胆囊积气积液 2.303 1.147 4.033 0.045 10.005 1.057~94.72
胆囊结石 2.274 1.106 4.226 0.040 9.718 1.112~84.948
结石嵌顿 1.269 0.395 10.325 0.001 3.559 1.641~7.72
WBC 0.214 0.052 17.133 <0.001 1.239 1.120~1.372
肌酸激酶 0.001 0.003 0.119 0.730 1.001 0.995~1.007
图1 AGC预测的列线图模型 注：AGC为急性坏疽性胆囊炎
图2 AGC列线图预测模型及验证模型的ROC曲线 注：AGC为急性坏疽性胆囊炎
图3 建模组与验证组AGC预测列线图模型的校准曲线 注：a为建模组，b为验证组
图4 建模组中AGC预测列线图模型的DCA 注：DCA为决策曲线分析
[1]
刘宝, 苏晓晨, 李广飞, 等. 超声引导下经皮肝穿刺胆囊引流对急性胆囊炎患者肝功能、胃肠道功能恢复及血清炎症指标的影响[J/OL]. 中华消化病与影像杂志(电子版), 2026, 16(1): 73-78.DOI: 10.3877/cma.j.issn.2095-2015.2026.01.013.
[2]
Fagan SP, Awad SS, Rahwan K, et al. Prognostic factors for the development of gangrenous cholecystitis[J]. Am J Surg, 2003, 186(5): 481-485.DOI: 10.1016/j.amjsurg.2003.08.001.
[3]
李征, 贾善勇, 刘凤祝, 等. 坏疽性胆囊炎的诊断和治疗进展[J]. 中华外科杂志, 2022, 60(4): 392-396.DOI: 10.3760/cma.j.cn112139-20211221-00616.
[4]
Chick JFB, Chauhan NR, Mason EF. Acute gangrenous cholecystitis[J]. Intern Emerg Med, 2012, 7(4): 387-388.DOI: 10.1007/s11739-012-0809-6.
[5]
Yeh DD, Cropano C, Fagenholz P, et al. Gangrenous cholecystitis: deceiving ultrasounds, significant delay in surgical consult, and increased postoperative morbidity![J]. J Trauma Acute Care Surg, 2015, 79(5): 812-816.DOI: 10.1097/ta.0000000000000832.
[6]
Safa R, Berbari I, Hage S, et al. Atypical presentation of gangrenous cholecystitis: a case series[J]. Am J Emerg Med, 2018, 36(11): 2135. e1-2135. e5.DOI: 10.1016/j.ajem.2018.08.039.
[7]
Terho PM, Leppäniemi AK, Mentula PJ. Laparoscopic cholecystectomy for acute calculous cholecystitis: a retrospective study assessing risk factors for conversion and complications[J]. World J Emerg Surg, 2016, 11(1): 54.DOI: 10.1186/s13017-016-0111-4.
[8]
Siada S, Jeffcoach D, Dirks RC, et al. A predictive grading scale for acute cholecystitis[J]. Trauma Surg Acute Care Open, 2019, 4(1): e000324.DOI: 10.1136/tsaco-2019-000324.
[9]
Miura F, Okamoto K, Takada T, et al. Tokyo Guidelines 2018: initial management of acute biliary infection and flowchart for acute cholangitis[J]. J Hepatobiliary Pancreat Sci, 2018, 25(1): 31-40.DOI: 10.1002/jhbp.509.
[10]
Shirah BH, Shirah HA, Saleem MA, et al. Predictive factors for gangrene complication in acute calculous cholecystitis[J]. Ann Hepatobiliary Pancreat Surg, 2019, 23(3): 228.DOI: 10.14701/ahbps.2019.23.3.228.
[11]
Alghamdi K, Rizk H, Jamal W, et al. Risk factors of gangrenous cholecystitis in general surgery patient admitted for cholecystectomy in king abdul-aziz university hospital (KAUH), Saudi Arabia[J]. Mater Sociomed, 2019, 31(4): 286.DOI: 10.5455/msm.2019.31.286-289.
[12]
Karthikarajam V, Pushpaketu N, Badhai S, et al. Risk factors associated with gangrenous cholecystitis: a cohort study from eastern India[J]. Cureus, 2024, 16(11): e74126.DOI: 10.7759/cureus.74126.
[13]
Kangas P, Tikkakoski A, Kettunen J, et al. Changes in hemodynamics associated with metabolic syndrome are more pronounced in women than in men[J]. Sci Rep, 2019, 9: 18377.DOI: 10.1038/s41598-019-54926-0.
[14]
Premkumar M, Anand AC. Tobacco, cigarettes, and the liver: the smoking Gun[J]. J Clin Exp Hepatol, 2021, 11(6): 700-712.DOI: 10.1016/j.jceh.2021.07.016.
[15]
Kim KH, Kim SJ, Lee SC, et al. Risk assessment scales and predictors for simple versus severe cholecystitis in performing laparoscopic cholecystectomy[J]. Asian J Surg, 2017, 40(5): 367-374.DOI: 10.1016/j.asjsur.2015.12.006.
[16]
Asgari Mehr M. Managing silent threats: unveiling gangrenous complication in acute emphysematous cholecystitis[J]. Radiol Case Rep, 2024, 19(5): 1768-1770.DOI: 10.1016/j.radcr.2024.01.084.
[17]
Bakri K, Abu-Shaban K, Doddi S, et al. Distinguishing between gangrenous cholecystitis and ascending cholangitis: a case study[J]. Cureus, 2022, 14(8): e28322.DOI: 10.7759/cureus.28322.
[18]
陈昌雄, 吕云福, 郑进方. 胆总管结石胆汁动力学研究进展[J/OL]. 中华肝脏外科手术学电子杂志, 2024,13(1):100-102.DOI: 10.3877/cma.j.issn.2095-3232.2024.01.020.
[19]
李旭, 郭鑫, 郭志民. 腹腔镜下急性坏疽性胆囊炎处理对策[J]. 川北医学院学报, 2015, 30(6): 857-860.DOI: 10.3969/j.issn.1005-3697.2015.06.31.
[20]
Stefanidis D, Sirinek KR, Bingener J. Gallbladder perforation: risk factors and outcome[J]. J Surg Res, 2006, 131(2): 204-208.DOI: 10.1016/j.jss.2005.11.580.
[21]
Önder A, Kapan M, Ülger BV, et al. Gangrenous cholecystitis: mortality and risk factors[J]. Int Surg, 2015, 100(2): 254-260.DOI: 10.9738/intsurg-d-13-00222.1.
[22]
Wu B, Buddensick TJ, Ferdosi H, et al. Predicting gangrenous cholecystitis[J]. HPB, 2014, 16(9): 801-806.DOI: 10.1111/hpb.12226.
[23]
Yacoub WN, Petrosyan M, Sehgal I, et al. Prediction of patients with acute cholecystitis requiring emergent cholecystectomy: a simple score[J]. Gastroenterol Res Pract, 2010, 2010: 901739.DOI: 10.1155/2010/901739.
[24]
Ganapathi AM, Speicher PJ, Englum BR, et al. Gangrenous cholecystitis: a contemporary review[J]. J Surg Res, 2015, 197(1): 18-24.DOI: 10.1016/j.jss.2015.02.058.
[25]
张志强. 坏疽性胆囊炎发生的危险因素分析[J]. 肝胆胰外科杂志, 2016, 28(4): 278-281.DOI: 10.11952/j.issn.1007-1954.2016.04.004.
[26]
刘南斌, 李堃, 魏玉华, 等. 急性进展迅速型坏疽性胆囊炎的危险因素分析[J]. 腹部外科, 2021, 34(2): 149-153.DOI: 10.3969/j.issn.1003-5591.2021.02.015.
[27]
李婧, 张传晔. CA19-9和D-二聚体联合检测在坏疽性胆囊炎诊断中的应用价值[J]. 湖南师范大学学报(医学版), 2020, 17(3): 89-93.
[28]
刘松, 胡道予, 彭俊红. 急性坏疽性胆囊炎的CT诊断价值[J]. 放射学实践, 2019, 34(5): 530-534.DOI: 10.13609/j.cnki.1000-0313.2019.05.010.
[29]
Bouassida M, Chtourou MF, Charrada H, et al. The severity grading of acute cholecystitis following the Tokyo Guidelines is the most powerful predictive factor for conversion from laparoscopic cholecystectomy to open cholecystectomy[J]. J Visc Surg, 2017, 154(4): 239-243.DOI: 10.1016/j.jviscsurg.2016.11.007.
[30]
Bouassida M, Madhioub M, Kallel Y, et al. Acute gangrenous cholecystitis: proposal of a score and comparison with previous published scores[J]. J Gastrointest Surg, 2021, 25(6): 1479-1486.DOI: 10.1007/s11605-020-04707-2.
[31]
Nguyen L, Fagan SP, Lee TC, et al. Use of a predictive equation for diagnosis of acute gangrenous cholecystitis[J]. Am J Surg, 2004, 188(5): 463-466.DOI: 10.1016/j.amjsurg.2004.07.013.
[32]
Tominaga GT, Staudenmayer KL, Shafi S, et al. The American Association for the Surgery of Trauma grading scale for 16 emergency general surgery conditions: disease-specific criteria characterizing anatomic severity grading[J]. J Trauma Acute Care Surg, 2016, 81(3): 593-602.DOI: 10.1097/ta.0000000000001127.
[1] 邬明嫄, 李婷婷, 鲜欣欣, 罗朝阳, 杨青, 卢漫. 基于超微血流成像血管指数的列线图模型诊断肢端黑色素瘤引流区域淋巴结的价值[J/OL]. 中华医学超声杂志（电子版）, 2026, 23(04): 276-282.
[2] 苏金铭, 田萍, 李青林, 马静, 宋伟伟, 刘博文, 曹晓莹, 薛玲, 王文军. HER-2阳性乳腺癌患者复发转移的影响因素及风险预测模型的构建[J/OL]. 中华乳腺病杂志(电子版), 2026, 20(04): 228-235.
[3] 江伟东, 陈博. 早发性可切除胃癌的临床病理特征及预后列线图模型的建立[J/OL]. 中华普通外科学文献(电子版), 2026, 20(04): 230-237.
[4] 罗文勇, 黄文龙, 阮沁桐, 蔡莹, 王欣, 吴柱国, 王利玲, 于海兵. 甲状腺癌术后并发低钙血症的发病风险预测模型构建[J/OL]. 中华普通外科学文献(电子版), 2026, 20(04): 257-264.
[5] 张彬, 王敏, 郑鹏, 冯犁, 李鑫, 赵高平. 早期胃癌淋巴结转移的危险因素及预后分析[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 337-342.
[6] 鲁峰刚, 刘宇, 张毅, 王秦玉. 新辅助放化疗直肠癌根治术患者术后吻合口漏影响因素分析及风险预测模型构建[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 347-351.
[7] 凌思果, 杨军, 乔旭华. ThuLEP与TURP术后发生尿道狭窄的预测模型构建[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(05): 534-541.
[8] 谢吉艳, 江志鹏, 何立锐. 复发性腹壁疝的危险因素及治疗策略[J/OL]. 中华疝和腹壁外科杂志(电子版), 2026, 20(04): 471-476.
[9] 刘慧, 赵燕霞, 汪海涛, 魏肖慧, 魏雪梅. CT肿瘤内部异质性对NSCLC患者免疫治疗反应性的预测价值[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(04): 560-565.
[10] 丁海斌, 刘爽, 乔丹, 李新涛, 陈艳妮, 程蒿, 刘佳. 外周血炎症指数预测148例局部晚期NSCLC免疫化疗后MPR的临床意义[J/OL]. 中华肺部疾病杂志(电子版), 2026, 19(04): 600-606.
[11] 马煜博, 李起, 耿智敏. 胆道恶性肿瘤预后预测模型：现状、挑战与未来展望[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(05): 702-710.
[12] 方荣焕, 孟子凡, 李馥彤, 鹿云皓, 杨永生. 肝硬化门静脉高压症脾切除术后并发症研究进展[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(04): 642-647.
[13] 高韶华, 徐涛, 宋亮, 雷丽, 王洪伟, 刘宏新. 1994—2023年中国结直肠癌疾病年龄-时期-队列效应及危险因素归因分析——基于GBD 2023研究[J/OL]. 中华结直肠疾病电子杂志, 2026, 15(04): 321-330.
[14] 董雯, 张小春, 金黑鹰, 金超. 结直肠息肉生长速率预测模型建立及其在结直肠腺瘤性息肉筛查中的应用价值[J/OL]. 中华结直肠疾病电子杂志, 2026, 15(04): 331-339.
[15] 郑顺, 张楠, 丁奕, 李文静. 基于人工智能的血液透析患者容量状态评估研究进展[J/OL]. 中华肾病研究电子杂志, 2026, 15(04): 202-205.
阅读次数
全文


摘要

版权所有 中华医学会 中华医学电子音像出版社有限责任公司  京ICP备14006079号-1  网络出版服务许可证：(署)网出证(京)字第075号

AI
小
编
AI小编
你好！我是《中华医学电子期刊资源库》AI小编，有什么可以帮您的吗？