Theprungsirikul Poy, Wang Rong, Ahmad Ishfaq, Neparidze Natalia, Ma Xiaomei, Chang Su-Hsin, Wang Shi-Yi
Department of Internal Medicine, Section of Hematology, Yale School of Medicine, New Haven, CT; Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale University, New Haven, CT.
Cancer Outcomes, Public Policy, and Effectiveness Research (COPPER) Center, Yale University, New Haven, CT; Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT.
Clin Lymphoma Myeloma Leuk. 2025 Jun 22. doi: 10.1016/j.clml.2025.06.016.
While several risk stratification models for smoldering multiple myeloma (SMM) to symptomatic multiple myeloma (MM) progression have been developed, the association between patient demographics, such as race, gender, and age, and SMM progression is not well understood.
Analyzing surveillance, epidemiology, and end results (SEER)-Medicare data, we applied a previously developed algorithm to identify patients with SMM diagnosed between 2007 and 2019. We used noncancer patients from the 5% random sample of Medicare beneficiaries as the controls. Cox proportional hazards models were applied to assess the association between race/gender/age and the development of hypercalcemia, renal failure, anemia and bone disease among SMM patients and the controls. We applied bootstrapping to calculate the estimates hazard ratios (aHRs) and 95% confidence intervals (CIs) of progression among SMM patients, adjusting for that of the noncancer controls.
Out of 1235 identified SMM patients (median age 75 years, White 76.7%), 856 (69.3%) of them progressed to symptomatic MM. Race (Black vs. White aHR = 0.82, 95% CI: 0.65-1.01) and gender (male vs. female aHR = 0.99, 95% CI: 0.86-1.13) were not significantly associated with SMM progression. Only age was negatively associated with SMM progression (75-79 years vs. 66-69 years aHR = 0.71, 95% CI: 0.58-0.87; 80-84 years vs. 66-69 years aHR = 0.59; 95% CI: 0.46-0.74; and ≥ 85 years vs. 66-69 years aHR = 0.59; 95% CI: 0.45-0.75).
This analysis provided insight into important parameters for MM natural history modeling by demonstrating that only age, but not race and gender, is negatively associated with SMM progression.
虽然已经开发了几种用于评估冒烟型多发性骨髓瘤（SMM）向症状性多发性骨髓瘤（MM）进展的风险分层模型，但患者人口统计学特征（如种族、性别和年龄）与SMM进展之间的关联尚未得到充分了解。
通过分析监测、流行病学和最终结果（SEER）-医疗保险数据，我们应用先前开发的算法来识别2007年至2019年间诊断为SMM的患者。我们将来自医疗保险受益人的5%随机样本中的非癌症患者作为对照。应用Cox比例风险模型来评估种族/性别/年龄与SMM患者及对照中高钙血症、肾衰竭、贫血和骨病发生之间的关联。我们应用自抽样法来计算SMM患者进展的估计风险比（aHRs）和95%置信区间（CIs），并对非癌症对照进行调整。
在1235名确定的SMM患者中（中位年龄75岁，白人占76.7%），其中856名（69.3%）进展为症状性MM。种族（黑人与白人相比，aHR = 0.82，95% CI：0.65 - 1.01）和性别（男性与女性相比，aHR = 0.99，95% CI：0.86 - 1.13）与SMM进展无显著关联。只有年龄与SMM进展呈负相关（75 - 79岁与66 - 69岁相比，aHR = 0.71，95% CI：0.58 - 0.87；80 - 84岁与66 - 69岁相比，aHR = 0.59；95% CI：0.46 - 0.74；≥85岁与66 - 69岁相比，aHR = 0.59；95% CI：0.45 - 0.75）。
该分析通过证明只有年龄而非种族和性别与SMM进展呈负相关，为MM自然史建模的重要参数提供了见解。
