Editor's note: In patients with newly diagnosed multiple myeloma (NDMM), infections pose a significant threat to morbidity and mortality, particularly within the initial months post-diagnosis. Immunoparesis, characterized by reduced levels of certain immunoglobulins, serves as a critical risk factor for these infections. To address this challenge, we present a novel nomogram prognostic model designed to identify NDMM patients at high risk of early Grade ≥3 infections, occurring within three months of diagnosis. By incorporating key hematologic and renal function markers, such as bone marrow plasma cell percentage, albumin levels, and renal function parameters, this model aids clinicians in predicting infection risk and tailoring patient management strategies accordingly.Introduction: Multiple myeloma (MM) is a hematologic malignancy primarily affecting the elderly population. While advancements in therapeutic approaches have improved long-term survival, early mortality remains a concern due to complications such as severe infections, which frequently manifest shortly after diagnosis. Immunoparesis, characterized by diminished immunoglobulin levels, significantly heightens infection susceptibility in these patients. Recognizing the need for early intervention, our study introduces a prognostic model aimed at identifying high-risk patients, thereby enabling timely clinical interventions to mitigate infection-related morbidity and mortality.
Study Design and Methods: The study employed a rigorous retrospective analysis encompassing a cohort of 430 newly diagnosed multiple myeloma (NDMM) patients, registered between June 2013 and June 2022, at Union Hospital attached to Tongji Medical College, Huazhong University of Science and Technology. This comprehensive analysis aimed to elucidate the prevalence of early severe infections occurring within three months post-diagnosis and identify associated variables contributing to infection risk. The data collection process was meticulous, covering a wide array of demographics, clinical characteristics, and extensive laboratory parameters. Variables of interest included but were not limited to, bone marrow plasma cell percentage, serum albumin levels, renal function markers (e.g., estimated glomerular filtration rate [eGFR], Cystatin C), uric acid levels, and cytogenetic abnormalities. These variables were selected based on prior literature highlighting their potential association with infection susceptibility in MM patients. The inclusion criteria for patients encompassed a confirmed diagnosis of NDMM, availability of complete medical records, and a minimum follow-up period of one year post-diagnosis. Patients with pre-existing infections or receiving immunomodulatory therapy prior to NDMM diagnosis were excluded to ensure the integrity of the study cohort. Statistical analyses were conducted to assess the significance of associations between identified variables and infection risk, employing techniques such as logistic regression and nomogram development. Internal validation techniques, including bootstrapping and cross-validation, were utilized to assess the robustness and generalizability of the prognostic model. Ethical approval was obtained from the institutional review board, and informed consent was waived due to the retrospective nature of the study. The study adhered to relevant ethical guidelines and regulations, ensuring the confidentiality and privacy of patient data throughout the research process. Overall, the comprehensive study design and meticulous methodology employed in this research endeavor facilitated the generation of valuable insights into infection risk prediction in NDMM patients, with the potential to inform clinical decision-making and improve patient outcomes in this high-risk population.
Results: The retrospective analysis revealed a notable prevalence of infections within the first year of diagnosis among NDMM patients, with a majority occurring within the initial three months post-diagnosis. A total of X% of patients experienced early severe infections, defined as Grade ≥3 infections, within this critical timeframe. Comparative analysis between infected and non-infected cohorts unveiled significant differences in various laboratory parameters, highlighting potential markers for infection risk prediction. Notably, patients who developed early severe infections exhibited distinct profiles, including elevated median uric acid levels and altered renal function markers such as reduced eGFR and elevated Cystatin C levels compared to non-infected counterparts. Furthermore, high-risk cytogenetic abnormalities and an increased bone marrow plasma cell percentage were significantly associated with a heightened frequency of early severe infections. The development of a prognostic nomogram integrating these variables demonstrated robust predictive capacity, exhibiting good discriminatory performance across diverse patient cohorts for early infection risk. Internal validation techniques further confirmed the reliability and generalizability of the prognostic model. Overall, the results underscore the utility of incorporating hematologic and renal function markers into a comprehensive prognostic model for early identification of NDMM patients at high risk of severe infections, thus informing tailored preventive and therapeutic strategies to improve patient outcomes in this vulnerable population.
Discussion: The development of a nomogram prognostic model integrating hematologic parameters and renal function markers presents a valuable tool for early identification of NDMM patients susceptible to severe infections. This model holds promise in stratifying infection risk and facilitating prompt preventive and therapeutic measures, ultimately improving patient outcomes. Moreover, this study contributes to existing literature highlighting immunoparesis as a significant determinant of infection susceptibility in MM patients. Future research endeavors should focus on further validating the nomogram across varied patient populations and assessing its integration into standard clinical practice.
Conclusion: In conclusion, the nomogram prognostic model provides clinicians with a new avenue for identifying high-risk NDMM patients and implementing early interventions to reduce the incidence of severe infections and enhance overall patient outcomes. By comprehensively addressing immune dysfunction in MM, this model underscores the importance of a multifactorial approach to patient care. Further exploration and validation of this model are warranted to maximize its clinical utility and impact on patient management strategies.
In this study, led by Yu Hu and Chunyan Sun from Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, groundbreaking research was conducted on infection risk in newly diagnosed multiple myeloma (NDMM) patients. Published in the prestigious journal ‘International Immunopharmacology,’ their findings, supported by notable funding bodies like the National Natural Science Foundation of China, are poised to influence clinical practices and pharmacotherapeutic strategies. The inclusion of online supplementary data ensures transparency and further engagement within the scientific community, marking a significant contribution to the field of multiple myeloma research.
