Development of a nomogram prognostic model for early Grade ≥ 3 infection in newly diagnosed multiple myeloma based on immunoparesis
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Published on: May 1, 2015
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.
