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December 8, 2025Blood

Integrating the cardiovascular-renal-metabolic syndrome（ckm）: Construction and validation of a comprehensive model for early identification of high-risk multiple myeloma

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WLWenyi Lu

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Overview

Findings indicate a predictive model enhances risk assessment in non-transplanted multiple myeloma patients, suggesting improved early detection strategies.

Key Points

  • This research aims to develop a model for early identification of high-risk multiple myeloma leveraging CKM syndrome and genetic risk factors.
  • Analyzed clinical data from 241 non-transplanted NDMM patients
  • Used Logistic regression and LASSO regression to identify risk factors
  • Evaluated the model through ROC analysis, calibration, and decision curve analysis
  • Identified significant predictors of high-risk multiple myeloma including age, EMD-S, and CKM stages
  • Achieved an ROC curve area of 0.731 indicating promising predictive accuracy
  • The model demonstrated high clinical utility as shown through various validation methods

Cite This Study

Wenyi Lu (2025) studied this question.

synapsesocial.com/papers/69362f574fa91c937236da71https://doi.org/10.1182/blood-2025-2796
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Also Consider

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  1. 1A multivariable prediction model for early mortality in multiple myeloma patients with renal impairment2025
  2. 2Machine learning risk stratification strategy for multiple myeloma: Insights from the EMN–HARMONY Alliance platform2025 · 4 citations
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  4. 4Prediction of cardiovascular adverse events in newly diagnosed multiple myeloma: Development and validation of a risk score prognostic model2023 · 3 citations
  5. 5Validation and refinement of the new high-risk multiple myeloma (HRMM) criteria in relapsed or refractory patients treated with BCMA CAR-T.2025