Clinical characteristics and risk factors of infection in initially treated patients with multiple myeloma during the induction period.

Abstract

Background: Multiple myeloma (MM) is a common hematologic malignancy and immune dysfunction is a hallmark of the disease. It leads to an increased infection risk, which is still a major cause of mortality. The infection spectrum and characteristics have evolved with the introduction of novel agents. An understanding of risk factors that increasing susceptibility to infection is critical in fighting them. This retrospective study aimed to identify risk factors associated with infection and develop nomogram to qualify the risk of infection.

Methods: We retrospectively reviewed the data of patients who were diagnosed with MM between April 1, 2018 and December 31, 2021 in our department. Independent predictors for infection were determined by the univariate and multivariate logistic regression analysis. Nomogram was established and evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA).

Results: A total of 230 MM patients who were diagnosed or treated in our department were included. Infections were identified in 37.4% of MM patients in the first treatment course. The most common infection was the pulmonary infection. The first treatment course had the highest infection rate. With three or more comorbidities, anemia, high LDH level and high β2-MG level were independent risk factors for infection in MM patients during the induction period. The area under the curve (AUC) of nomogram was 0.746 (95% CI: 0.679-0.814). The calibration curve and DCA indicated the good performance of the nomogram.

Conclusion: Multiple myeloma patients with one or more of these mentioned risk factors should be monitored with particular care in order to decrease the incidence and severity of infective complications. Nomogram was established to predict the incidence of infection in MM patients. Nomogram has satisfactory accuracy, and clinical utility may benefit for clinical decision-making.