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Establishment of a Human Multiple Myeloma Xenograft Model in the Chicken to Study Tumor Growth, Invasion and Angiogenesis
Published on: May 1, 2015
Development of a nomogram prognostic model for early Grade ≥ 3 infection in newly diagnosed multiple myeloma based on
Min Zhang1, Qianwen Cheng2, Fei Zhao1
1Institute of Hematology, Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
Background:
Infection, a significant cause of death in multiple myeloma (MM) patients, is a common complication and is closely associated with immunoparesis. There exists no clear definition of early infection, so early infection is defined in this paper as the occurrence within 3 months after diagnosis, considering the high incidence of infections within 3 months after diagnosis. This study established a new nomogram model based on immunoparesis to identify MM patients with high-risk early infection.
Methods:
A retrospective collection of 430 NDMM patients from June 2013 to June 2022 was conducted, and the patients were further divided into a training cohort and a validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) was used to select the best variables that can be used to establish a new nomogram prediction model. Validation was performed in the validation and entire cohorts.
Results:
After diagnosis, 67.7 % of the patients suffered from severe infection within 1 year, and 59.5 % experienced the first severe infection within 3 months. Variables associated with an increased risk of severe infection in the first 3 months included: BMPC, D-dimer, serum β2 microglobulin, immunoparesis, albumin, and eGFR. The nomogram based on the above six factors achieved a good C-index of 0.754, 0.73, and 0.731 in predicting early infection in the training cohort, validation cohort, and entire cohort, respectively. Finally, the time-dependent receiver operating characteristic (ROC) curve and decision curve analysis (DCA) of the nomogram showed that the model provided superior diagnostic capacity and clinical net benefit.
Conclusion:
In this study, we established a nomogram model to predict early grade ≥ 3 infection in NDMM patients. This model can assist clinicians in identifying NDMM patients with high-risk infections and improve their prognosis through early intervention.
Insights
This study developed a nomogram model to predict early severe infections in newly diagnosed multiple myeloma (NDMM) patients. The model identifies high-risk individuals, enabling timely interventions to improve outcomes for patients with multiple myeloma.
Area of Science:
- Hematology
- Oncology
- Infectious Diseases
Background:
- Infection is a major cause of mortality in multiple myeloma (MM) patients, often linked to immunoparesis.
- Early infection lacks a clear definition; this study defines it as occurring within 3 months of diagnosis due to high incidence.
- A nomogram model based on immunoparesis was developed to identify MM patients at high risk for early infection.
Purpose of the Study:
- To establish a predictive model for early-stage severe infections in newly diagnosed multiple myeloma (NDMM) patients.
- To identify key clinical factors associated with the risk of severe infection within the first three months post-diagnosis.
- To improve patient prognosis through early identification and intervention for high-risk infections.
Main Methods:
- Retrospective analysis of 430 NDMM patients (June 2013-June 2022).
- Patients divided into training and validation cohorts.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression used for variable selection and nomogram construction.
- Model validation performed on separate validation and entire patient cohorts.
Main Results:
- 59.5% of patients experienced severe infection within 3 months of diagnosis.
- Key predictors for early severe infection included bone marrow plasma cells (BMPC), D-dimer, serum β2 microglobulin, immunoparesis, albumin, and eGFR.
- The nomogram demonstrated strong predictive accuracy (C-index: 0.754 training, 0.730 validation, 0.731 entire cohort).
- Time-dependent ROC curve and DCA confirmed the model's superior diagnostic capacity and clinical utility.
Conclusions:
- A novel nomogram model effectively predicts early grade ≥3 infection in NDMM patients.
- This tool aids clinicians in identifying high-risk patients for prompt intervention.
- Early intervention based on nomogram predictions can potentially improve patient prognosis.
