Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma
Abstract 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 be...
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Nature Portfolio
2025-01-01
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| author | Kunzhe Lin Jianping Zhang Lin Zhao Liangfeng Wei Shousen Wang |
| author_facet | Kunzhe Lin Jianping Zhang Lin Zhao Liangfeng Wei Shousen Wang |
| author_sort | Kunzhe Lin |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-45125ca095154df1bf10f85fb155794f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-45125ca095154df1bf10f85fb155794f2025-01-12T12:16:16ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-024-83319-1Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenomaKunzhe Lin0Jianping Zhang1Lin Zhao2Liangfeng Wei3Shousen Wang4Fuzong Clinical Medical College of Fujian Medical UniversityDepartment of Urology, 910th Hospital of Joint Logistics Support ForceDepartment of Neurosurgery, 900th Hospital of Joint Logistics Support ForceDepartment of Neurosurgery, 900th Hospital of Joint Logistics Support ForceFuzong Clinical Medical College of Fujian Medical UniversityAbstract 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.https://doi.org/10.1038/s41598-024-83319-1Machine learningdelayed hyponatremiatranssphenoidal surgerypituitary adenomasSHAP algorithm |
| spellingShingle | Kunzhe Lin Jianping Zhang Lin Zhao Liangfeng Wei Shousen Wang Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma Scientific Reports Machine learning delayed hyponatremia transsphenoidal surgery pituitary adenomas SHAP algorithm |
| title | Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma |
| title_full | Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma |
| title_fullStr | Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma |
| title_full_unstemmed | Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma |
| title_short | Machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma |
| title_sort | machine learning algorithms for predicting delayed hyponatremia after transsphenoidal surgery for patients with pituitary adenoma |
| topic | Machine learning delayed hyponatremia transsphenoidal surgery pituitary adenomas SHAP algorithm |
| url | https://doi.org/10.1038/s41598-024-83319-1 |
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