Clinical neurology and neurosurgery · 2025
Predictive modeling of postoperative hyponatremia after pituitary adenoma surgery.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Li Guoge, Li Xiaotong, Ding Yaowei, Sun Jialu, Chen Yuxin, Wang Siqi, Yang Zhijun, Zhang Guojun
Аннотация
OBJECTIVE: To improve the prediction of postoperative hyponatremia after pituitary surgery by comparing six machine learning (ML) models.
METHODS: We analyzed 777 patients post-pituitary surgery, with 118 developing hyponatremia, and validated our findings in an external cohort of 183 patients (28 hyponatremia cases). Six ML models were developed using pre- and postoperative clinical and laboratory data, with predictive accuracy assessed via the area under the ROC curve (AUC). Interpretability techniques included permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and Shapley Additive exPlanations (SHAP).
RESULTS: Thirteen key features were identified for ML model construction. The Random Forest (RF) model showed the highest predictive accuracy with AUCs of 0.88 in internal validation datasets. PFI analysis identified desmopressin dosage, postoperative day 1 and day 2 serum sodium levels, and surgical duration as the most significant influential variables. LIME and SHAP corroborated PFI's local feature importance findings.
CONCLUSIONS: The RF-based ML model offers accurate predictions of postoperative hyponatremia by integrating personalized patient data, potentially enhancing patient outcomes through rapid hyponatremia prediction.
METHODS: We analyzed 777 patients post-pituitary surgery, with 118 developing hyponatremia, and validated our findings in an external cohort of 183 patients (28 hyponatremia cases). Six ML models were developed using pre- and postoperative clinical and laboratory data, with predictive accuracy assessed via the area under the ROC curve (AUC). Interpretability techniques included permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and Shapley Additive exPlanations (SHAP).
RESULTS: Thirteen key features were identified for ML model construction. The Random Forest (RF) model showed the highest predictive accuracy with AUCs of 0.88 in internal validation datasets. PFI analysis identified desmopressin dosage, postoperative day 1 and day 2 serum sodium levels, and surgical duration as the most significant influential variables. LIME and SHAP corroborated PFI's local feature importance findings.
CONCLUSIONS: The RF-based ML model offers accurate predictions of postoperative hyponatremia by integrating personalized patient data, potentially enhancing patient outcomes through rapid hyponatremia prediction.