ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1635451
Machine learning-based predictive model for hungry bone syndrome following parathyroidectomy in secondary hyperparathyroidism
Provisionally accepted
Yalin Chai1
Congjuan Luo2*Feng Luo1*Nan Yuan1Jiaming Yin1Bing Shen1Lijie Sun1Lin Zhang1Le Yin1Xuan Wang1- 1The Affiliated Hospital of Qingdao University, Qingdao, China
- 2Qingdao University, Qingdao, China
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Objective: To develop an interpretable machine learning model for predicting hungry bone syndrome (HBS) risk following parathyroidectomy in secondary hyperparathyroidism (SHPT) patients.Methods: This retrospective study analyzed 181 SHPT patients who underwent parathyroidectomy at the Affiliated Hospital of Qingdao University (2015-2025). Participants were randomly divided into a training group (70%) and a validation group (30%). From 46 candidate variables, five key predictors were selected through logistic regression and Boruta algorithm. Seven machine learning models were trained, evaluated by ROC curves, calibration curves, and decision curve analysis (DCA). Model interpretability was quantified via SHapley Additive exPlanations (SHAP).The XGBoost algorithm demonstrated excellent predictive performance, with an AUC of 0.878 (95% CI: 0.779-0.973) and an F1 score of 0.871 for the validation cohort. The key predictors included preoperative parathyroid hormone (Pre-PTH), the percentage decay between Pre-PTH and PTH at skin closure (%PTH), alkaline phosphatase, serum calcium, and age. Additionally, we designed a web application to estimate HBS risk.This interpretable machine-learning model is effective in predicting the risk of HBS in SHPT patients after parathyroidectomy, thereby providing guidance for postoperative surveillance strategies.
Keywords: Hungry bone syndrome, Secondary hyperparathyroidism, Parathyroidectomy, Risk factors, machine learning
Received: 27 May 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Chai, Luo, Luo, Yuan, Yin, Shen, Sun, Zhang, Yin and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Congjuan Luo, Qingdao University, Qingdao, China
Feng Luo, The Affiliated Hospital of Qingdao University, Qingdao, China
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