Risk Factors and Predictive Model for Disseminated Intravascular Coagulation in Patients with Multiple Myeloma
Objectives Multiple myeloma (MM) is a hematologic malignancy comprising approximately 10% of all blood cancers. Patients with MM are at risk for disseminated intravascular coagulation (DIC), a serious complication characterized by systemic coagulation activation, leading to microthrombi, organ dysfu...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-02-01
|
| Series: | Clinical and Applied Thrombosis/Hemostasis |
| Online Access: | https://doi.org/10.1177/10760296251316873 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Objectives Multiple myeloma (MM) is a hematologic malignancy comprising approximately 10% of all blood cancers. Patients with MM are at risk for disseminated intravascular coagulation (DIC), a serious complication characterized by systemic coagulation activation, leading to microthrombi, organ dysfunction, and severe bleeding. This study aims to investigate the incidence of DIC among MM patients and identify risk factors associated with DIC development. We also sought to develop a predictive formula for assessing DIC risk. Methods A retrospective analysis was conducted on MM patients. Logistic regression analysis was used to identify factors significantly associated with DIC. The predictive power of the logistic regression model was evaluated using receiver operating characteristic (ROC) curve analysis. Results The incidence of DIC among hospitalized MM patients was 16.8%. Significant factors identified by logistic regression analysis included prothrombin time (PT), fibrin degradation products (FDP), and D-dimer levels. ROC curve analysis indicated that the predictive model had strong discriminatory power, with an area under the curve (AUC) of 0.927. A predictive formula for the probability of DIC occurrence was developed based on the logistic regression model. Conclusions The predictive formula developed in this study offers a tool for early identification of MM patients at high risk of DIC. While the model demonstrates strong predictive capability, further validation and refinement are required to improve its accuracy and clinical application. |
|---|---|
| ISSN: | 1938-2723 |