Development of a Risk Assessment Model for Early Grade ≥ 3 Infection During the First 3 Months in Patients Newly Diagnosed With Multiple Myeloma Based on a Multicenter, Real-World Analysis in China

Author:

Shang Yufeng,Wang Weida,Liang Yuxing,Kaweme Natasha Mupeta,Wang Qian,Liu Minghui,Chen Xiaoqin,Xia Zhongjun,Zhou Fuling

Abstract

PurposeThe study aimed to assess factors associated with early infection and identify patients at high risk of developing infection in multiple myeloma.MethodsThe 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.ResultsBacteria 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%, p<0.0001), internal validation cohort (37.9% vs. 24.1% vs. 13.0%, p=0.009) and external validation cohort (40.0% vs. 29.2% vs. 8.5%, p=0.0003). IRMM displayed good calibration (p<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.ConclusionIn 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.

Funder

National Natural Science Foundation of China

Publisher

Frontiers Media SA

Subject

Cancer Research,Oncology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3