ORIGINAL RESEARCH article
Front. Oncol.
Sec. Hematologic Malignancies
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1624680
Machine Learning-Based Nomogram Predicts Heart Failure Risk in Elderly Relapsed/Refractory Multiple Myeloma Patients Receiving Carfilzomib-Based Therapy
Provisionally accepted
- 1Department of Medical Oncology, Shaanxi Provincial Cancer Hospital, Xi'an, China
- 2Department of Hematology, Xi'an Daxing Hospital, Xi’an 710061, Shaanxi, China, Xi'an, China
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Objective: 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. Methods: 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 predictors identified 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).Results: 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. The nomogram showed good predictive performance (C-index: 0.780, 95% CI: 0.704-0.841), internal calibration (Hosmer-Lemeshow P = 0.430), and external validation (Hosmer-Lemeshow χ² = 1.054, P = 0.788). DCA confirmed clinical utility across a wide range of threshold probabilities (1% to 83%), with a peak net benefit of 0.248.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.
Keywords: Multiple Myeloma, Carfilzomib, Heart Failure, Nomogram model, machine learning
Received: 07 May 2025; Accepted: 30 Jul 2025.
Copyright: © 2025 Qiao, Ding, Zhu, Chen and Nie. 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: Lei Nie, Department of Medical Oncology, Shaanxi Provincial Cancer Hospital, Xi'an, China
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