[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123413-en":3,"doc-seo-123413-105":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},123413,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine learning-based nomogram predicts heart failure risk in elderly relapsed/refractory multiple myeloma patients receiving carfilzomib-based therapy - Research results and clinical utility","Machine learning-based models were used to develop and validate a nomogram for predicting heart failure risk in elderly patients with relapsed/refractory multiple myeloma treated with carfilzomib-based therapy. A retrospective cohort of 192 patients from a single center (January 1, 2023 to December 31, 2024) supported feature selection using LASSO, SVM, and XGBoost with cross-model and bootstrap consistency checks. The nomogram identified coronary artery disease, hypertension, renal insufficiency, and albumin level as key risk factors, achieving strong discrimination and calibration with internal and external validation. Decision curve analysis confirmed clinical net benefit across a broad threshold range.","TYPE Original Research PUBLISHED 03 September 2025 DOI 10.3389/fonc.2025.1624680  \nOPEN ACCESS  \nEDITED BY  \nQiang Wang,  \nHouston Methodist Research Institute, United States  \nREVIEWED BY  \nKexin Huang,  \nUniversity of Texas Health Science Center at Houston, United States  \nRui Duan,  \nHouston Methodist Hospital, United States Miao Xian,  \nSichuan University, China  \n*CORRESPONDENCE  \nLei Nie  \n [xhcn307@outlook.com](xhcn307@outlook.com)  \nRECEIVED 07 May 2025  \nACCEPTED 30 July 2025  \nPUBLISHED 03 September 2025  \nCITATION  \nQiao D, Ding H-b, Zhu C-h, Chen R-a and Nie L (2025) Machine learning-based nomogram predicts heart failure risk in elderly relapsed/refractory multiple myeloma patients receiving carfilzomib-based therapy. Front. Oncol. 15:1624680 .  \ndoi: 10.3389/fonc.2025.1624680  \nCOPYRIGHT  \n© 2025 Qiao, Ding, Zhu, Chen and Nie. This isan 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) 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 nomogram predicts heart failure risk in elderly relapsed/ refractory multiple myeloma patients receiving carfilzomib-based therapy  \nDan Qiao 1, Hai-bin Ding 1, Cong-hui Zhu 2, Ren-an Chen 2 and Lei Nie 1*  \n1 Department of Medical Oncology, Shaanxi Provincial Cancer Hospital, Xi’an, Shaanxi, China, 2 Department of Hematology, Xi ’an Daxing Hospital, Xi’an, Shaanxi, China  \nObjective: To develop and validate a machine learning-based nomogram for predicting heart failure (HF) in elderly patients with relapsed/refractory multiple myeloma (RRMM) receiving carfilzomib-based therapy, facilitating early identification and individualized clinical management.  \nMethods: This retrospective study analyzed clinical data from 192 elderly RRMM patients treated with carfilzomib-based therapy at Shaanxi Provincial Cancer Hospital (from January 1, 2023, to December 31, 2024) . Machine learning algorithms, including the Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were used for variable selection. Robust predictorsidentified through cross-model consistency evaluation and bootstrap resampling were incorporated into a nomogram. Model performance was assessed using concordance index (C-index), calibration curves, and decision curve analysis (DCA) .  \nResults: HF occurred in 25 . 5%(49/192) of patients. Machine learning models consistently identified coronary artery disease (CAD), hypertension, renal insufficiency, and albumin (Alb) levels as significant HF risk factors. Thenomogram showed good predictive performance (C-index: 0.780, 95% CI: 0.704–0. 841), internal calibration (Hosmer–Lemeshow c² = 1 .334, P = 0 . 970), and external validation (Hosmer-Lemeshow c² = 1 . 054, P = 0 . 788) . DCAconfirmed clinical utility across a wide range of threshold probabilities (1% to 83%), with a peak net benefit of 0 .248.  \nConclusion: This study provides a practical nomogram for cardiovascular risk assessment in elderly RRMM patients receiving carfilzomib-based therapy, which may assist clinicians in early risk stratification and support tailored monitoring and management throughout treatment.  \nKEYWORDS  \nmultiple myeloma, carfilzomib, heart failure, nomogram model, machine learning  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nMultiple myeloma (MM) is a hematologic malignancy characterized by the proliferation of clonal plasma cells in the bone marrow and associated organ dysfunction (1). As the second most common hematologic malignancy, MM accounts for approximately 1.0% of all cancers and 13.0% of hematologic malignancies, with a median age","cbCaisV9Q4nQz3on","https://ap.wps.com/l/cbCaisV9Q4nQz3on","pdf",1810083,1,14,"English","en",105,"# Introduction\n# Methods\n## Study design and data\n## Machine learning model development and variable selection\n# Results\n## Heart failure incidence and key risk factors\n## Nomogram performance (C-index, calibration)\n## Decision curve analysis\n# Conclusion","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To develop and validate a machine learning-based nomogram that predicts heart failure risk in elderly relapsed/refractory multiple myeloma patients receiving carfilzomib-based therapy for early identification and individualized management.\"},{\"question\":\"Which variables were identified as significant heart failure risk factors?\",\"answer\":\"Coronary artery disease, hypertension, renal insufficiency, and albumin (Alb) levels were consistently identified as significant risk factors by the machine learning models.\"},{\"question\":\"How was the nomogram’s performance evaluated?\",\"answer\":\"Performance was assessed using concordance index (C-index), calibration curves with Hosmer–Lemeshow tests for internal and external validation, and decision curve analysis (DCA) for clinical utility.\"}]","Machine learning-based nomogram predicts heart failure risk in elderly relapsed/refractory multiple myeloma patients receiving carfilzomib-based therapy - Research results and clinical utility | PDF",1785816348,35,{"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-nomogram-predicts-heart-failure-risk-in-elderly-relapsedrefractory-multiple-myeloma-patients-receiving-carfilzomib-based-therapy-research-results-and-clinical-utility","",{"@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-nomogram-predicts-heart-failure-risk-in-elderly-relapsedrefractory-multiple-myeloma-patients-receiving-carfilzomib-based-therapy-research-results-and-clinical-utility/123413/",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","2026-08-04",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 was the main objective of the study?","Question",{"text":75,"@type":76},"To develop and validate a machine learning-based nomogram that predicts heart failure risk in elderly relapsed/refractory multiple myeloma patients receiving carfilzomib-based therapy for early identification and individualized management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which variables were identified as significant heart failure risk factors?",{"text":80,"@type":76},"Coronary artery disease, hypertension, renal insufficiency, and albumin (Alb) levels were consistently identified as significant risk factors by the machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the nomogram’s performance evaluated?",{"text":84,"@type":76},"Performance was assessed using concordance index (C-index), calibration curves with Hosmer–Lemeshow tests for internal and external validation, and decision curve analysis (DCA) for clinical utility.","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"]