[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121466-en":3,"doc-seo-121466-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121466,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine learning-based predictive model for hungry bone syndrome following parathyroidectomy in secondary hyperparathyroidism - Original research","An interpretable machine learning model is developed to predict hungry bone syndrome (HBS) risk after parathyroidectomy in secondary hyperparathyroidism (SHPT). A retrospective cohort of 181 SHPT patients is analyzed, with data split into training and validation groups. Five key predictors are selected from 46 candidates using logistic regression and Boruta, and seven models are evaluated by ROC, calibration, and decision curve analysis. XGBoost yields strong discrimination and calibration, while SHAP supports model interpretability.","TYPE Original Research PUBLISHED 05 September 2025 DOI 10.3389/fendo.2025.1635451  \nOPEN ACCESS  \nEDITED BY  \nSree Bhushan Raju,  \nNizam ’s Institute of Medical Sciences, India  \nREVIEWED BY  \nAnna Eremkina,  \nEndocrinology Research Center, Russia Shuai Lu,  \nBeijing Jishuitan Hospital, China Petru Adrian Radu,  \nNephrology Clinical Hospital “Dr. Carol Davila”, Romania  \n*CORRESPONDENCE  \nFeng Luo  \n [luofeng19821124@126.com](luofeng19821124@126.com)[ ](luofeng19821124@126.com)Congjuan Luo  \n [luocongjuan2018@163.com](luocongjuan2018@163.com)  \nRECEIVED 27 May 2025  \nACCEPTED 18 August 2025  \nPUBLISHED 05 September 2025  \nCITATION  \nChai Y, Yuan N, Yin J, Shen B, Sun L, Zhang L, Yin L, Wang X, Luo F and Luo C (2025) Machine learning-based predictive model for hungry bone syndrome following parathyroidectomy in secondary hyperparathyroidism.  \nFront. Endocrinol. 16:1635451 .  \ndoi: 10.3389/fendo.2025.1635451  \nCOPYRIGHT  \n© 2025 Chai, Yuan, Yin, Shen, Sun, Zhang, Yin, Wang, Luo and Luo. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 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.  \nMachine learning-based predictive model for hungry bone syndrome following parathyroidectomy in secondary hyperparathyroidism  \nYalin Chai, Nan Yuan, Jiaming Yin, Bing Shen, Lijie Sun, Lin Zhang, Le Yin, Xuan Wang, Feng Luo* and Congjuan Luo *  \nThe Affiliated Hospital of Qingdao University, Qingdao, China  \nObjective: To develop an interpretable machine learning model for predicting hungry bone syndrome (HBS) risk following parathyroidectomy in secondary hyperparathyroidism (SHPT) patients.  \nMethods: 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) .  \nResults: 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.  \nConclusions: 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.  \nKEYWORDS  \nhungry bone syndrome, secondary hyperparathyroidism, parathyroidectomy, risk factors, machine learning  \nFrontiers in Endocrinology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nSecondary hyperparathyroidism (SHPT), a prevalent complication of chronic kidney disease (CKD), contributes to bone lesions, vascular calcification, and elevated risks of cardiovascular events and mortality (1–3) . Epidemiological studies indicate that SHPT affects 20%-80% of CKD patients, with prevalence rates correlating with disease severity and dialysis duration (4) . Current therapeutic strategies encompass vitamin Danalogs (e.g., calcitriol) (5), calcimimetics (e.g., cinacalcet), and phosphate binders (6) . For refractory cases, parathyroidectomy (PTX) remains the definitive intervention per KDIGO guidelines, particularl","cbCaikjmVsLlhyyA","https://ap.wps.com/l/cbCaikjmVsLlhyyA","pdf",4399406,1,13,"English","en",105,"# Introduction\n## Secondary hyperparathyroidism and current management\n## Postoperative hungry bone syndrome and rationale\n# Methods\n## Patients and study design\n## Predictor selection and model development\n## Model evaluation and interpretability","[{\"question\":\"What is the main goal of the predictive model in this study?\",\"answer\":\"To develop an interpretable machine learning model that predicts the risk of hungry bone syndrome after parathyroidectomy in SHPT patients.\"},{\"question\":\"How were predictors selected for the final model?\",\"answer\":\"From 46 candidate variables, five key predictors were selected using logistic regression and the Boruta algorithm.\"},{\"question\":\"Which model showed the best predictive performance?\",\"answer\":\"XGBoost demonstrated excellent performance, with an AUC of 0.878 and an F1 score of 0.871 in the validation cohort.\"}]","Machine learning-based predictive model for hungry bone syndrome following parathyroidectomy in secondary hyperparathyroidism - Original research | PDF",1785735782,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-predictive-model-for-hungry-bone-syndrome-following-parathyroidectomy-in-secondary-hyperparathyroidism-original-research","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-predictive-model-for-hungry-bone-syndrome-following-parathyroidectomy-in-secondary-hyperparathyroidism-original-research/121466/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the predictive model in this study?","Question",{"text":75,"@type":76},"To develop an interpretable machine learning model that predicts the risk of hungry bone syndrome after parathyroidectomy in SHPT patients.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were predictors selected for the final model?",{"text":80,"@type":76},"From 46 candidate variables, five key predictors were selected using logistic regression and the Boruta algorithm.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model showed the best predictive performance?",{"text":84,"@type":76},"XGBoost demonstrated excellent performance, with an AUC of 0.878 and an F1 score of 0.871 in the validation cohort.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]