实用肝脏病杂志 ›› 2026, Vol. 29 ›› Issue (3): 458-462.doi: 10.3969/j.issn.1672-5069.2026.03.035

• 胆石症 • 上一篇    下一篇

腹腔镜经胆囊管胆总管取石术后72小时血清总胆红素水平预测模型的建立与验证*

王康, 张英峰, 罗健, 李久平, 李相成   

  1. 210000 江苏省南京市 南京医科大学第一附属医院肝胆中心(王康,李相成);江苏省高邮市人民医院肝胆外科(王康,张英峰,罗健,李久平)
  • 收稿日期:2025-08-05 出版日期:2026-05-10 发布日期:2026-05-18
  • 通讯作者: 李相成,E-mail:drxcli@njmu.edu.cn
  • 作者简介:王康,男,34岁,大学本科,主治医师。研究方向:主要从事肝胆外科研究。E-mail:wkpop216@163.com
  • 基金资助:
    *国家自然科学基金面上项目(编号:82472865);江苏省扬州市卫生健康委卫生基础研究计划(联合专项)项目(编号:2024-4-31/2024-4-30)

Validation of a predictive model by total serum bilirubin levels 72 hours after laparoscopic common bile duct stone extraction via cystic duct

Wang Kang, Zhang Yingfeng, Luo Jian, et al   

  1. Hepatobiliary Surgery Center, First Affiliated Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
  • Received:2025-08-05 Online:2026-05-10 Published:2026-05-18

摘要: 目的 构建腹腔镜经胆囊管胆总管取石(LTCBDE)术后72小时血清总胆红素(TSB)升高预测模型并评价其临床应用价值。方法 2023年1月~2024年12月高邮市人民医院诊治的胆总管结石患者158例,均接受LTCBDE术治疗。采用Lasso和多因素Logistic回归法建立预测模型,应用绘制受试者工作特征(ROC)曲线衡量模型的区分能力,应用校准曲线验证其预测的准确性,借助决策曲线分析法评估其在临床应用中的实际效果。采用Bootstrap法对模型进行内部验证。结果 在术后发现本组TSB水平升高者44例(27.8%);经Lasso回归和多因素Logistic回归分析显示胆总管结石(CBDS)直径≥10 mm(OR=12.272,95%CI:3.853~39.084,P<0.001)、多发CBDS(OR=11.317,95%CI:3.956~32.377,P<0.001)和血清白蛋白(ALB)水平(OR=0.841,95%CI:0.761~0.930,P<0.001)是LTCBDE术后72小时TSB升高的影响因素;构建预测模型展现出显著的诊断效能,其AUC达到0.900(95%CI:0.8509~0.9484),最佳截断点为0.232,其特异度为0.798和灵敏度为0.886;绘制校准曲线与实际值的一致性较高,Brier值为0.115,表明模型具有良好的预测效能;决策曲线揭示了该模型在临床应用中具有净收益;在内部验证过程中,模型的AUC为0.892(95%CI:0.8399~0.9442),显示出良好的区分能力和校准效果。结论 CBDS直径≥10 mm、多发CBDS和血清白蛋白水平可能影响LTCBDE术后TSB水平,能为临床诊疗和个体化评估提供一定的参考。

关键词: 胆总管结石, 腹腔镜经胆囊管胆总管取石术, 经胆囊管, 血清总胆红素, 预测模型

Abstract: Objective The aim of this study was to construct and evaluate a predictive model by abnormal total serum bilirubin (TSB) level at 72 hours after laparoscopic transcystic common bile duct exploration (LTCBDE). Methods A retrospective analysis was conducted on the clinical data of 158 patients with common bile duct stone who underwent LTCBDE at Gaoyou People's Hospital between January 2023 and December 2024. Lasso and multivariate Logistic regression analysis were employed to identify clinical risk indicators for elevated TSB levels, by which a Logistic predictive model was established. The discriminative ability of the model was assessed by using receiver operating characteristic (ROC) curves, while calibration curves were used to verify its predictive accuracy. Decision curve analysis (DCA) was applied to evaluate its clinical utility. Internal validation of the model was performed by Bootstrap method. Results TSB levels elevated in 44 cases (27.8%) post-operationally in our series; Lasso regression and multivariate Logistic regression analysis revealed that common bile duct stone diameter ≥10 mm (OR=12.272, 95% CI: 3.853-39.084, P<0.001), multiple stones (OR=11.317, 95% CI: 3.956-32.377, P<0.001) and serum albumin level (OR=0.841, 95% CI: 0.761-0.930, P<0.001) were the impacting factors for elevated TSB levels 72 hours after LTCBDE; the three predictors were incorporated into multivariate Logistic regression model, and the constructed predictive model demonstrated a significant diagnostic efficacy, with the area under the ROC curve (AUC) of 0.900 (95% CI: 0.8509-0.9484); the optimal cutoff value was 0.232, achieving a balance between specificity (0.798) and sensitivity (0.886); the calibration curve showed a high agreement with actual values, and the Brier score was 0.115, indicating excellent predictive performance; additionally, the decision curve revealed net clinical benefit; during internal validation, the model exhibited an AUC of 0.892 (95% CI: 0.8399-0.9442), demonstrating robust discriminative ability and calibration. Conclusion The clinical predictive model based on CBDS diameter, multiple CBDS and serum albumin level could effectively identify patients at risk of elevated TSB at 72 hours after LTCBDE, providing valuable reference for clinical decision-making and individualized assessment.

Key words: Common bile duct stone, Laparoscopic transcystic common bile duct exploration, Via cystic duct, Total serum bilirubin, Predictive model