[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117393-en":3,"doc-seo-117393-105":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},117393,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture","Hip fractures pose a major health challenge for super-aged patients (≥80 years) because frailty and multiple comorbidities substantially raise postoperative pneumonia risk. Using data from the Chinese PLA General Hospital Hip Fracture Cohort Study, the study analyzed 555 surgically treated super-aged patients, with demographics, comorbidities, laboratory tests, and surgery types. Patients were randomly split 7:3 into training and validation sets, and models including DT, RF, eXGBM, SVM, NN, and LR were tuned and evaluated. The eXGBM model achieved the best discrimination (AUC 0.929), accuracy, and calibration, supporting clinically useful prediction and prevention guidance.","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 Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture  \nMiaotian Tang 1 , Meng Zhang 1 , Yu Dang 1 , Mingxing Lei2 , Dianying Zhang 1 , 3–5  \n1Department of Trauma Orthopaedics, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China; 2Department of Orthopaedics, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, People’s Republic of China; 3National Trauma Medical Center, Beijing, 100044, People’s Republic of China; 4Key Laboratory of Trauma Treatment and Neural Regeneration, Ministry of Education, Beijing, 100044, People’s Republic of China; 5Department of Orthopedics, Peking University Binhai Hospital, Tianjin, 300450, People’s Republic of China  \nCorrespondence: Dianying Zhang, Department of Trauma Orthopaedics, Peking University People’s Hospital, Beijing, 100044, People’s Republic of China, Tel +86 010-88326550, Email [zdy8016@163.com](zdy8016@163.com); Mingxing Lei, Department of Orthopaedics, Hainan Hospital of Chinese PLA General Hospital, Sanya, 572013, People’s Republic of China, Tel +8618811772189, Email [leimingxing2@sina.com](leimingxing2@sina.com)  \n\n| Background: Hip fractures have become a significant health concern, particularly among super-aged patients, who were at a high risk of postoperative pneumonia due to their frailty and the presence of multiple comorbidities. This study aims to establish and validate a model to predict postoperative pneumonia among super-aged patients with hip fracture.\u003Cbr>Methods: Data were derived from the Chinese PLA General Hospital (PLAGH) Hip Fracture Cohort Study, and we included 555 super-aged patients (≧80 years old) with hip fracture treated with surgery. Patient’s demographics, comorbidities, laboratory tests, and surgery types were collected for analysis. All patients were randomly splitting into a training group and a validation group according to the ratio of 7:3 . The majority of patients were used to train models, which was tuned using a series of algorithms, including decision tree (DT), random forest (RF), extreme gradient boosting machine (eXGBM), support vector machine (SVM), neural network (NN), and logistic regression (LR) .\u003Cbr>Results: The incidence of postoperative pneumonia was 7.2%(40/555) . Among the six developed models, the eXGBM model demonstrated the optimal model, with the area under the curve (AUC) value of 0.929 (95% CI: 0.900–0.959), followed by the RF model (AUC: 0.916, 95% CI: 0.885–0.948) . The LR model had an AUC value of 0.720 (95% CI: 0.662–0.778) . In addition, theeXGBM model demonstrated the optimal prediction performance in terms of accuracy (0.858), precision (0.870), F1 score (0.855), Brier score (0.104), and log loss (0.349) . It also showed favorable calibration ability and favorable clinical net benefits across various threshold risk.\u003Cbr>Conclusion: This study develops and validates a reliable machine learning-based model to predict pneumonia specifically among super-aged patients with hip fracture following surgery. This model can serve as a useful tool to identify postoperative pneumonia and guide clinical strategies for super-aged patients with hip fracture.\u003Cbr>Keywords: machine learning, postoperative pneumonia, hip fracture, super-aged patients, geriatric patients |\n| --- |\n| Introduction\u003Cbr>With the global aging population on the rise, hip fractures have become a significant health concern, 1 particularly among the older adults. Super-aged patients, defined as those aged 80 years and older,2,3 were at a heightened risk of complications following surgery due to their frailty and the presence of multiple comorbidities. One of the most severe and common complications in this demographic is postoperative pneumonia,4 which can lead to","cbCaioHY7IpFJHF3","https://ap.wps.com/l/cbCaioHY7IpFJHF3","pdf",6869587,1,14,"English","en",105,"# Background\n# Methods\n## Data and Participants\n## Model Development\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What population and clinical context does the model target?\",\"answer\":\"The model targets super-aged patients (≥80 years) with hip fracture who undergo surgery, aiming to predict postoperative pneumonia risk in this high-risk group.\"},{\"question\":\"How was the dataset prepared and how were patients split for model validation?\",\"answer\":\"Data came from the Chinese PLA General Hospital Hip Fracture Cohort Study, including 555 surgically treated super-aged patients. Patients were randomly split into training and validation groups at a 7:3 ratio.\"},{\"question\":\"Which machine learning model performed best and how was performance measured?\",\"answer\":\"The extreme gradient boosting machine (eXGBM) model performed best, with AUC 0.929 (95% CI 0.900–0.959). Performance also included accuracy, precision, F1 score, Brier score, log loss, and calibration/clinical net benefit across thresholds.\"}]","Machine Learning-Based Prediction of Postoperative Pneumonia Among Super-Aged Patients With Hip Fracture | PDF",1785675613,35,{"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-postoperative-pneumonia-among-super-aged-patients-with-hip-fracture","",{"@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-postoperative-pneumonia-among-super-aged-patients-with-hip-fracture/117393/",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},"What population and clinical context does the model target?","Question",{"text":75,"@type":76},"The model targets super-aged patients (≥80 years) with hip fracture who undergo surgery, aiming to predict postoperative pneumonia risk in this high-risk group.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset prepared and how were patients split for model validation?",{"text":80,"@type":76},"Data came from the Chinese PLA General Hospital Hip Fracture Cohort Study, including 555 surgically treated super-aged patients. Patients were randomly split into training and validation groups at a 7:3 ratio.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and how was performance measured?",{"text":84,"@type":76},"The extreme gradient boosting machine (eXGBM) model performed best, with AUC 0.929 (95% CI 0.900–0.959). Performance also included accuracy, precision, F1 score, Brier score, log loss, and calibration/clinical net benefit across thresholds.","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"]