A validated nomogram incorporating five independent predictors demonstrated excellent discrimination (AUC 0.858) for predicting early mortality in multiple myeloma patients with renal impairment, stratifying patients into low (1.6%), mid (13.9%), and high (43.9%) risk groups.
Age greater than 55 years was identified as an independent predictor of early mortality in MM patients with renal impairment.
Serum calcium level ≥2.5 mmol/L was an independent predictor of early mortality.
Bone marrow plasma cell percentage ≥40% was independently associated with early mortality.
Renal response less than partial response was independently associated with increased risk of early mortality.
Hematologic response less than complete response was the strongest independent predictor of early mortality.
The developed nomogram demonstrated excellent discrimination and calibration in the training cohort and consistent performance in the validation cohort.
Risk stratification using the nomogram categorized patients into three prognostic groups with significantly different early mortality rates.
No validated prognostic models for early mortality were previously available specifically for MM patients with renal impairment prior to this study.
This research suggests that a prediction tool (called a nomogram) can reliably identify which multiple myeloma (MM) patients who also have kidney problems are at high risk of dying within the first year of treatment. MM is a blood cancer, and kidney damage is a common and serious complication that makes treatment more difficult. The researchers analyzed records from 285 patients treated at multiple hospitals between 2007 and 2020, and found five key factors that predicted early death: being older than 55, having high blood calcium, having a high percentage of cancer cells in the bone marrow, not achieving at least a partial recovery of kidney function, and not achieving a complete response of the cancer to treatment. The last factor — failing to achieve complete cancer response — was by far the strongest predictor, with patients more than 20 times as likely to die early if they did not reach this milestone. Using these five factors, the researchers built and tested a scoring tool that sorted patients into three risk groups. Patients in the low-risk group had only a 1.6% chance of dying within the first year, while those in the high-risk group had a 43.9% chance — more than one in three. The tool performed well both in the group used to build it (correctly distinguishing outcomes about 86% of the time) and in a separate group used to test it (about 75% accuracy). This research suggests that clinicians could use this kind of scoring tool at the time of diagnosis to identify MM patients with kidney problems who are at the greatest risk of early death, potentially allowing for more intensive or tailored treatment approaches for those patients. The authors note that this is the first validated tool of its kind for this specific high-risk patient group, and that future studies with prospective data collection would help confirm how broadly applicable it is.
Liu M, Jian Y, Zhou H, Jia J, Li J, Geng C, et al.. (2026). A Multivariable Prediction Model for Early Mortality in Multiple Myeloma Patients With Renal Impairment.. Cancer medicine. https://doi.org/10.1002/cam4.72054