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

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, beta 2MG >= 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.