游离皮瓣移植术后血管危象风险预测模型的 研究进展

Advances in Risk Prediction Models for Vascular Crisis after Free Flap Transplantation

摘要: 游离皮瓣移植是组织缺损修复的重要方法，但术后血管危象可能引发皮瓣的血运障碍，严重影响了患者的预后。本文就游离皮瓣移植术后血管危象风险预测模型的研究进展进行了综述，重点围绕风险因素的评估指标体系、模型构建方法、性能评估标准以及当前面临的主要问题展开了讨论。逻辑回归等传统统计模型能够提供明确的风险因子权重，具备良好的可解释性；而随机森林、神经网络等机器学习方法在处理多个变量间复杂非线性关系时能够展现出更高的判别精度。预后营养指数(PNI)作为反映患者营养与免疫状态的重要指标，被有关研究证实是血管危象的独立预测因素，炎症标志物联合使用，可进一步提升模型的预测能力。未来研究应当整合多中心数据、优化算法泛化性，并推动个体化的风险管理。本文旨在为临床实践提供理论依据，同时为融合随机森林算法与PNI的预测模型构建提供思路。

Abstract: Free flap transplantation is important for repairing tissue defects, but postoperative vascular crisis may cause flap perfusion and impact the patient’s outcome. In this review, we survey recent improvements of risk prediction models of vascular crisis after free flap transplant, with applications to the assessment index system for risk factors, the model construction, performance evaluations, and the most significant problems. Traditional statistical models such as logistic regression provide clear weights of risk factors and are intuitive, while machine learning methods such as random forests and neural networks can be more accurate in complex non-linear relationships between multiple variables. The Prognostic Nutritional Index (PNI) is a very important indicator of a patient’s nutritional/immune state which is shown to predict vascular crisis independently. Combined with inflammation markers, the proposed model can potentially improve the predictive power. Future work could include the use of multi-center data, better generalization of algorithms, and personalized risk management. In summary, this review could serve as a theoretical basis in clinical practice and provide practical guidance on building prediction models based on random forest algorithms and PNI.