Shang Yufeng, Wang Weida, Liang Yuxing, Kaweme Natasha Mupeta, Wang Qian, Liu Minghui, Chen Xiaoqin, Xia Zhongjun, Zhou Fuling
Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Front Oncol. 2022 Mar 17;12:772015. doi: 10.3389/fonc.2022.772015. eCollection 2022.
The study aimed to assess factors associated with early infection and identify patients at high risk of developing infection in multiple myeloma.
The study retrospectively analyzed patients with MM seen at two medical centers between January 2013 and June 2019. One medical center reported 745 cases, of which 540 of the cases were available for analysis and were further subdivided into training cohort and internal validation cohort. 169 cases from the other medical center served as an external validation cohort. The least absolute shrinkage and selection operator (Lasso) regression model was used for data dimension reduction, feature selection, and model building.
Bacteria and the respiratory tract were the most common pathogen and localization of infection, respectively. In the training cohort, PS≥2, HGB<35g/L of the lower limit of normal range, β2MG≥6.0mg/L, and GLB≥2.1 times the upper limit of normal range were identified as factors associated with early grade ≥ 3 infections by Lasso regression. An infection risk model of MM (IRMM) was established to define high-, moderate- and low-risk groups, which showed significantly different rates of infection in the training cohort (46.5% vs. 22.1% vs. 8.8%, <0.0001), internal validation cohort (37.9% vs. 24.1% vs. 13.0%, =0.009) and external validation cohort (40.0% vs. 29.2% vs. 8.5%, =0.0003). IRMM displayed good calibration (<0.05) and discrimination with AUC values of 0.76, 0.67 and 0.71 in the three cohorts, respectively. Furthermore, IRMM still showed good classification ability in immunomodulatory (IMiD) based regimens, proteasome-inhibitors (PI) based regimens and combined IMiD and PI regimens.
In this study, we determined risk factors for early grade ≥ 3 infection and established a predictive model to help clinicians identify MM patients with high-risk infection.
本研究旨在评估与早期感染相关的因素,并识别多发性骨髓瘤中发生感染的高危患者。
本研究回顾性分析了2013年1月至2019年6月在两个医疗中心就诊的骨髓瘤患者。一个医疗中心报告了745例病例,其中540例可用于分析,并进一步细分为训练队列和内部验证队列。另一个医疗中心的169例病例作为外部验证队列。采用最小绝对收缩和选择算子(Lasso)回归模型进行数据降维、特征选择和模型构建。
细菌和呼吸道分别是最常见的病原体和感染部位。在训练队列中,通过Lasso回归确定PS≥2、血红蛋白低于正常范围下限35g/L、β2微球蛋白≥6.0mg/L以及球蛋白≥正常范围上限2.1倍为与早期≥3级感染相关的因素。建立了骨髓瘤感染风险模型(IRMM)以定义高、中、低风险组,其在训练队列(46.5%对22.1%对8.8%,<0.0001)、内部验证队列(37.9%对24.1%对13.0%,=0.009)和外部验证队列(40.0%对29.2%对8.5%,=0.0003)中的感染率有显著差异。IRMM在三个队列中均显示出良好的校准(<0.05)和区分能力,AUC值分别为0.76、0.67和0.71。此外,IRMM在基于免疫调节剂(IMiD)的方案、基于蛋白酶体抑制剂(PI)的方案以及IMiD和PI联合方案中仍显示出良好的分类能力。
在本研究中,我们确定了早期≥3级感染的危险因素,并建立了一个预测模型,以帮助临床医生识别感染高危的骨髓瘤患者。