Research Article: Machine learning-based nomogram predicts heart failure risk in elderly relapsed/refractory multiple myeloma patients receiving carfilzomib-based therapy
Abstract:
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.
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).
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 ?² = 1.334, P = 0.970), and external validation (Hosmer-Lemeshow ?² = 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.
Introduction:
Multiple 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 at onset of 65 years and a 1.2-fold higher incidence in males than in females ( 2 ). Age-related frailty status and underlying comorbidities, especially cardiovascular and renal diseases,…
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