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Updated: Jan 20, 2026
Establishment of a Human Multiple Myeloma Xenograft Model in the Chicken to Study Tumor Growth, Invasion and Angiogenesis
Published on: May 1, 2015
Construction and validation of a simple, scoreable model for predicting infection risk in patients with multiple
Sheng-Ke Tu1, Jing Yang1, Sha-Dong Min1
1Department of Hematology, The First Affiliated Hospital of Jishou University, Jishou, Hunan, China.
Objective:
This study aims to identify the risk factors for infection in patients with multiple myeloma (MM) and to develop a predictive model for infection.
Methods:
We retrospectively analyzed the clinical data of 180 multiple myeloma patients with MM who underwent chemotherapy at the First Affiliated Hospital of Jishou University from January 2017 to December 2022. A predictive model for infection events was constructed based on these data.
Results:
In the modeling group, 34 out of 90 patients (37.78%) experienced infections, whereas in the validation group, 40 out of 90 patients (44.44%) had infections. Binary logistic regression analysis showed that the levels of C-reactive protein level, fasting blood glucose level, lactate dehydrogenase level, Eastern Cooperative Oncology Group (ECOG) score, and the percentage of bone marrow plasma cell percentage were independent risk factors for infection in patients with MM (P < 0.05). The infection prediction model developed using these variables demonstrated good accuracy, with an area under the ROC curve of 0.827 (95% CI: 73.66%-91.78%) in the modeling group and for the validation group being 0.760 (95% CI: 65.97%-85.93%) in the validation group.
Conclusion:
This study confirms that C-reactive protein level, fasting blood glucose level, lactate dehydrogenase level, ECOG score, and the percentage of bone marrow plasma cell percentage are significant risk factors for infection in patients with MM.
Clinical Significance:
This infection prediction model offers substantial clinical value by enabling a shift from reactive management to proactive, preventive intervention for infections in this vulnerable population.
Insights
This study identified key risk factors for infection in multiple myeloma patients undergoing chemotherapy. A predictive model using C-reactive protein, blood glucose, lactate dehydrogenase, ECOG score, and bone marrow plasma cells aids proactive infection prevention.
Area of Science:
- Oncology
- Infectious Diseases
- Biostatistics
Background:
- Multiple myeloma (MM) patients undergoing chemotherapy are at high risk of infection.
- Early identification of infection risk factors is crucial for timely intervention.
Purpose of the Study:
- To identify independent risk factors for infection in MM patients.
- To develop and validate a predictive model for infection events in this population.
Main Methods:
- Retrospective analysis of clinical data from 180 MM patients treated between January 2017 and December 2022.
- Binary logistic regression was used to identify risk factors.
- An infection prediction model was constructed and validated.
Main Results:
- Infection occurred in 37.78% of the modeling group and 44.44% of the validation group.
- Independent risk factors identified: C-reactive protein, fasting blood glucose, lactate dehydrogenase, ECOG score, and bone marrow plasma cell percentage (P < 0.05).
- The model showed good accuracy (AUC 0.827 in modeling group, 0.760 in validation group).
Conclusions:
- C-reactive protein, fasting blood glucose, lactate dehydrogenase, ECOG score, and bone marrow plasma cell percentage are significant risk factors for infection in MM patients.
- The developed prediction model can assist in proactive infection management, shifting from reactive to preventive strategies.
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