基于可解释机器模型预测经碟垂体瘤术后迟发性低钠血症的可行性研究  

Feasibility study of an interpretable machine based model to predict postoperative delayed hyponatremia in pituitary adenoma patients

作　　者：李峰达 苗发安 陆岳 张奇 董成祥 范月超 LI Fengda;MIAO Fa′an;LU Yue;ZHANG Qi;DONG Chengxiang;FAN Yuechao(Department of Neurosurgery,the Affiliated Hospital of Xuzhou Medical University,Xuzhou,Jiangsu 221002,China)

机构地区：[1]徐州医科大学附属医院神经外科,江苏徐州221002

出　　处：《徐州医科大学学报》2023年第3期225-229,共5页Journal of Xuzhou Medical University

摘　　要：目的 探讨基于XGBoost算法构建经蝶垂体瘤术后迟发性低钠血症的可解释机器预测模型的可行性。方法 选取自2018年8月—2021年8月于徐州医科大学附属医院行神经内镜下经鼻蝶入路手术治疗的垂体腺瘤患者168例,收集临床及实验室数据,使用XGboost算法建立预测模型,并基于SHAP算法对经蝶垂体瘤手术后发生迟发性低钠血症的影响因素进行解释分析。结果 在纳入研究的168例垂体腺瘤患者中,35例出现术后迟发性低钠血症,133例未出现术后迟发性低钠血症。本研究构建的预测模型各项评价指标表现良好,预测模型的R^(2)为0.94。最终基于SHAP值分析显示,术前钠离子水平、甲状腺激素水平及年龄是预测经蝶垂体瘤术后迟发性低钠血症的主要特征。通过具体案例的分析,该模型的可解释性得到进一步验证。结论 通过XGBoost算法构建经蝶垂体瘤手术后发生迟发性低钠血症的预测模型,并用SHAP算法提高机器学习模型对数据分析的可解释性,相关结果有助于降低术后并发症的发生率,改善患者预后,为临床工作提供参考和指导。Objective To investigate the feasibility of constructing an interpretable machine model for predicting delayed hyponatraemia after transsphenoidal pituitary tumour surgery based on the XGBoost algorithm.Methods A total of 168 pituitary adenoma patients who were admitted to the Affiliated Hospital of Xuzhou Medical University from August 2018 to August 2021,and treated via neuroendoscopic transsphenoidal approach were enrolled and their clinical and laboratory data were collected.Then,the XGboost algorithm was used to establish a predictive model and the factors influencing the occurrence of delayed hyponatremia after transsphenoidal pituitary tumor surgery were interpreted based on SHAP algorithm.Results Among the 168 patients with pituitary adenoma,35 developed postoperative delayed hyponatraemia and 133 did not develop postoperative delayed hyponatraemia.The established predictive model performed well on all evaluation criteria,with the R^(2) score of 0.94.The final analysis based on SHAP values revealed that preoperative sodium levels,thyroid hormone levels and age were the main features in predicting the occurrence of delayed hyponatraemia after transsphenoidal pituitary tumour surgery.The interpretability of the model was further validated through analysis of two individual cases.Conclusions We established the XGBoost-based machine learning model to predict the likelihood of delayed hyponatraemia after transsphenoidal pituitary tumour surgery,and utilize SHAP algorithm to improve the interpretability of the model.These findings can provide reference and guidance for clinical work,in order to effectively reduce postoperative morbidity and improve patient safety.

关 键 词：垂体腺瘤 迟发性低钠血症 机器学习 XGboost SHAP 

分 类 号：R651.1[医药卫生—外科学]