Lin Kunzhe, Zhang Jianping, Zhao Lin, Wei Liangfeng, Wang Shousen
Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China.
Department of Neurosurgery, 900th Hospital of Joint Logistics Support Force, Fuzhou, China.
Sci Rep. 2025 Jan 9;15(1):1463. doi: 10.1038/s41598-024-83319-1.
This study aimed to develop and validate machine learning (ML) models to predict the occurrence of delayed hyponatremia after transsphenoidal surgery for pituitary adenoma. We retrospectively collected clinical data on patients with pituitary adenomas treated with transsphenoidal surgery between January 2010 and December 2020. From January 2021 to December 2022, patients with pituitary adenomas were prospectively enrolled. We trained seven ML models to predict delayed hyponatremia using the clinical variables in the training set. The final model was internally validated using a test set and a prospective dataset. The SHapley Additive exPlanations (SHAP) algorithm was used to determine the significance of each variable in the occurrence of delayed hyponatremia. In the training dataset, the best predictive performance was observed for XGBoost (area under the ROC curve; AUC = 0.821), followed by Random Forest (AUC = 0.8), Logistic Regression (AUC = 0.793), Support Vector Machine (AUC = 0.776), naïve Bayes (AUC = 0.774), K-Nearest Neighbors (AUC = 0.742), and Decision Tree (AUC = 0.717). The AUC of the XGBoost model for the test and prospective datasets are 0.831 and 0.785, respectively. The differences in pituitary stalk deviation angle, the "measurable pituitary stalk" length before and after surgery, and blood sodium concentration between preoperative and postoperative day 2 were important variables for predicting delayed hyponatremia as determined by the SHAP algorithm. The XGBoost model was best able to predict delayed hyponatremia after transsphenoidal surgery for pituitary adenomas. The differences in pituitary stalk deviation angle, pre- versus postoperative "measurable pituitary stalk" length, and pre- versus postoperative day 2 blood sodium concentrations were important variables for predicting delayed hyponatremia.
本研究旨在开发并验证机器学习（ML）模型，以预测垂体腺瘤经蝶窦手术后迟发性低钠血症的发生情况。我们回顾性收集了2010年1月至2020年12月期间接受经蝶窦手术治疗的垂体腺瘤患者的临床数据。2021年1月至2022年12月，前瞻性纳入垂体腺瘤患者。我们使用训练集中的临床变量训练了七个ML模型来预测迟发性低钠血症。最终模型使用测试集和前瞻性数据集进行内部验证。采用SHapley加性解释（SHAP）算法确定每个变量在迟发性低钠血症发生中的重要性。在训练数据集中，XGBoost的预测性能最佳（ROC曲线下面积；AUC = 0.821），其次是随机森林（AUC = 0.8）、逻辑回归（AUC = 0.793）、支持向量机（AUC = 0.776）、朴素贝叶斯（AUC = 0.774）、K近邻（AUC = 0.742）和决策树（AUC = 0.717）。XGBoost模型在测试集和前瞻性数据集中的AUC分别为0.831和0.785。根据SHAP算法确定，垂体柄偏斜角度、手术前后“可测量垂体柄”长度以及术前与术后第2天血钠浓度的差异是预测迟发性低钠血症的重要变量。XGBoost模型最能预测垂体腺瘤经蝶窦手术后的迟发性低钠血症。垂体柄偏斜角度、术前与术后“可测量垂体柄”长度以及术前与术后第2天血钠浓度的差异是预测迟发性低钠血症的重要变量。
