这项回顾性队列研究使用 Taussig 癌症中心的骨髓瘤患者登记系统来识别 2017 年 1 月至 2021 年 12 月期间诊断出的患有多发性骨髓瘤的成人。电子健康记录数据捕获了从诊断到首次开具口服抗骨髓瘤药物处方以及开始设施管理或口服抗骨髓瘤治疗的时间。 。我们确定了 720 名患者，诊断时的平均年龄为 67 岁±11 岁； 55% 为男性，77% 为白人，22% 为黑人，1% 为其他种族，享受私人保险 (36%)、传统医疗保险 (29%)、医疗保险优势 (25%) 和医疗补助 (8.3%)。超过三分之一的患者 (37%) 居住在最弱势地区剥夺指数 (ADI) 四分位数的地区。中位随访时间为 765 天。该队列中 75% 的人服用了口服抗骨髓瘤药物处方（不包括皮质类固醇），中位服用时间为 28 天（IQR，15-61）。在多变量 Cox 回归模型中，黑人种族（与白人相比，调整后风险比 [aHR]，0.61，95% CI，0.42-0.87），诊断时年龄较大（aHR 每 1 年，0.97，95% CI，0.95- 0.98），住院期间诊断（aHR，0.63，95% CI，0.43-0.92），估计肾小球滤过率≤29 ml/min/1.73 m2（对比≥60，aHR，0.46，95% CI，0.29） -0.73）与 30 天时口服抗骨髓瘤药物的处方填写呈负相关，而保险类型和 ADI 并不是显着的预测因素。© 2024。作者。

This retrospective cohort study used Taussig Cancer Center's Myeloma Patient Registry to identify adults with multiple myeloma diagnosed between January 2017-December 2021. Electronic health records data captured time from diagnosis to initial prescription fill for oral antimyeloma medications and initiation of facility administered or oral antimyeloma treatment. We identified 720 patients with a mean age at diagnosis of 67 years ±11; 55% were male, 77% White, 22% Black, 1% other races, covered by private insurance (36%), traditional Medicare (29%), Medicare Advantage (25%), and Medicaid (8.3%). Over a third of patients (37%) resided in an area in the most disadvantaged area deprivation index (ADI) quartile. The median available follow-up was 765 days. Seventy-five percent of the cohort filled an oral antimyeloma medication prescription (excluding corticosteroids), with a median time to fill of 28 days (IQR, 15-61). In the multivariable Cox regression model, Black race (vs. White, adjusted hazard ratio [aHR], 0.61, 95% CI, 0.42-0.87), older age at diagnosis (aHR per 1 year, 0.97, 95% CI, 0.95-0.98), diagnosis during an inpatient admission (aHR, 0.63, 95% CI, 0.43-0.92), and estimated glomerular filtration rate ≤29 ml/min/1.73 m2 (vs. ≥60, aHR, 0.46, 95% CI, 0.29-0.73) were negatively associated with prescription fill for oral antimyeloma medication at 30 days, while insurance type and ADI were not significant predictors.© 2024. The Author(s).