[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117549-en":3,"doc-seo-117549-105":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":4,"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},117549,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning-Based Prediction of Post-PKP Frailty - Retrospective Cohort Study","Frailty and osteoporotic vertebral compression fractures create a bidirectional clinical challenge, while the effect of percutaneous kyphoplasty (PKP) on frailty progression remains insufficiently clarified. A retrospective cohort study of 4599 PKP patients used two-year follow-up to classify frailty status and trained machine learning models with baseline clinical variables, imaging features, and surgical details. After feature selection, data splitting, and hyperparameter optimization, Extreme Gradient Boosting achieved strong discrimination, supported by SHAP interpretation and external validation.","Clinical Interventions in Aging downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nClinical Interventions in Aging  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nMachine Learning-Based Prediction of Post-PKP Frailty: A Retrospective Cohort Study  \nDingjun Xu *, Ziwei Fan *, Zhiyuan Li*, Mengxian Jia, Xiang Fang, Yizhe Shen, Quan Zhou, Changnan Xie, Honglin Teng   \nDepartment of Orthopedics (Spine Surgery), The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Changnan Xie; Honglin Teng, Email [541017595@qq.com](541017595@qq.com); [tenghonglin@wzhospital.cn](tenghonglin@wzhospital.cn)  \n\n| Background: Frailty and osteoporotic vertebral compression fractures (OVCFs) exhibit bidirectional causality, yet the impact of percutaneous kyphoplasty (PKP) on frailty progression remains unclear. This study developed machine learning (ML) models to predict post-PKP frailty and identify key predictors.\u003Cbr>Methods: A retrospective cohort of 4599 PKP patients was categorized into frailty/non-frailty groups based on two-year follow-up. Variables included preoperative baseline data, imaging parameters (fracture number/segments, Genant classification, T2 hyperintensity), clinical characteristics (osteoporosis severity, Visual Analogue Scale scores, residual low back pain [LBP]), and surgical details. After data splitting (4:1 ratio), features were selected to train and optimize ML models, with performance evaluated via area under the curve (AUC) . The ML model with the best performance was selected as our final model while using it for external validation. SHAP analysis determined predictor contributions.\u003Cbr>Results: Key features (residual LBP, Genant classification, etc) informed model development. Hyperparameter optimization enhanced performance, with Extreme Gradient Boost achieving superior prediction (AUC 0.950, 95% CI 0.934–0.965) . The model still maintains a good performance in the external test set, with an AUC of 0.845 (95% CI 0.805–0.884) . SHAP identified residual LBP, Genant classification, and postoperative recumbency duration as top predictors.\u003Cbr>Conclusion: ML models effectively predict post-PKP frailty, highlighting modifiable risk factors. Standardized anti-osteoporosis therapy, residual LBP prevention, and reduced postoperative recumbency may mitigate frailty risk.\u003Cbr>Keywords: frailty, machine learning, osteoporosis, PKP, prognostic prediction |\n| --- |\n| Introduction\u003Cbr>Frailty, a clinical syndrome characterized by diminished physiological reserves and multisystem dysfunction, compromises the capacity to withstand minor stressors, thereby predisposing individuals to adverse health outcomes.1,2 Although prevalent across all age groups, frailty demonstrates age-dependent epidemiology, with incidence rates positively correlating with advancing age.3 Global demographic projections estimate the population aged ≥65 years will reach 2 billion by 2050, suggesting an impending escalation in frailty burden.4 Substantial evidence links frailty to critical adverse outcomes including falls, increased hospitalization rates, malignancies, and mortality, which collectively deteriorate elderly health and impose substantial strain on healthcare systems.5–9 Notably, frailty represents a dynamic and potentially reversible state except during terminal decline phases, underscoring the imperative to identify modifiable risk factors for targeted prevention and mitigation strategies.10\u003Cbr>Osteoporotic vertebral compression fractures (OVCFs), a prevalent geriatric condition, have emerged as a critical public health concern due to their substantial morbidity burden in elderly populations.11 Substantial evidence identifies frailty as a significant predictor of osteoporotic fractures in older adults.12 Furthermore, OVCFs exhibit an accumulative effect on both frailty incid","cbCaikd1yXOQsoCS","https://ap.wps.com/l/cbCaikd1yXOQsoCS","pdf",3650276,1,12,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"How was post-PKP frailty defined in the study?\",\"answer\":\"Patients were grouped into frailty and non-frailty categories using two-year follow-up after PKP.\"},{\"question\":\"Which variables were used to build the machine learning models?\",\"answer\":\"The models incorporated preoperative baseline data, imaging parameters, clinical characteristics such as osteoporosis severity and pain scores, and surgical details.\"},{\"question\":\"How did the best-performing model perform and what improved it?\",\"answer\":\"Extreme Gradient Boosting achieved an AUC of 0.950 in the main evaluation, and hyperparameter optimization enhanced overall predictive performance.\"}]","Machine Learning-Based Prediction of Post-PKP Frailty - Retrospective Cohort Study | PDF",1785676920,30,{"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-prediction-of-post-pkp-frailty-retrospective-cohort-study","",{"@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-prediction-of-post-pkp-frailty-retrospective-cohort-study/117549/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How was post-PKP frailty defined in the study?","Question",{"text":75,"@type":76},"Patients were grouped into frailty and non-frailty categories using two-year follow-up after PKP.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which variables were used to build the machine learning models?",{"text":80,"@type":76},"The models incorporated preoperative baseline data, imaging parameters, clinical characteristics such as osteoporosis severity and pain scores, and surgical details.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the best-performing model perform and what improved it?",{"text":84,"@type":76},"Extreme Gradient Boosting achieved an AUC of 0.950 in the main evaluation, and hyperparameter optimization enhanced overall predictive performance.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]