腹腔镜胆囊切除术术前预测模型的研究进展与挑战

Research Progress and Challenges of Preoperative Prediction Models for Laparoscopic Cholecystectomy

摘要: 腹腔镜胆囊切除术(Laparoscopic Cholecystectomy, LC)是治疗胆囊良性疾病的金标准。然而，困难腹腔镜胆囊切除术(Difficult Laparoscopic Cholecystectomy, DLC)显著增加胆管损伤、出血及转为开腹手术的风险。术前准确预测LC难度对于提高手术安全性和减少并发症至关重要。本综述遵循PRISMA流程，系统评估了与LC相关的预测模型和因素。我们检索了PubMed、Embase、Web of Science和Cochrane Library等数据库，使用关键词包括“Preoperative prediction”、“Laparoscopic cholecystectomy”和“Difficult laparoscopic cholecystectomy”。文献检索范围为2000年至今，纳入了随机对照试验(RCT)、前瞻性和回顾性队列研究。现有研究表明，炎症标志物(如CRP、PCT)、影像学参数(如胆囊壁厚度、结石嵌顿)以及多因素预测模型在临床中具有重要应用价值。此外，人工智能驱动的预测模型正在崭露头角，显示出较大的潜力。然而，现有的预测工具仍面临验证样本不足、跨中心适用性差等挑战。未来基于大数据和人工智能的个性化预测模型有望提高术前评估的准确性，并优化手术决策。

Abstract: Laparoscopic cholecystectomy (LC) is the gold standard for treating benign gallbladder diseases. However, difficult laparoscopic cholecystectomy (DLC) significantly increases the risk of bile duct injury, bleeding, and conversion to open surgery. Accurate preoperative prediction of LC difficulty is crucial for enhancing surgical safety and reducing complications. This review, conducted following PRISMA guidelines, systematically evaluates predictive models and factors associated with LC. A comprehensive search of PubMed, Embase, Web of Science, and Cochrane Library was performed, using keywords such as “Preoperative prediction”, “Laparoscopic cholecystectomy”, and “Difficult laparoscopic cholecystectomy”. The literature search was limited to studies published from 2000 to present, including randomized controlled trials (RCTs), prospective, and retrospective cohort studies. Current evidence shows that inflammatory markers (e.g., CRP, PCT), imaging parameters (e.g., gallbladder wall thickness, stone impaction), and multifactorial models have significant clinical utility. Additionally, artificial intelligence-driven models are emerging as promising tools. However, challenges remain, including insufficient validation samples and poor cross-center applicability. Future personalized predictive models based on big data and AI are expected to improve preoperative assessment accuracy and optimize surgical decision-making.