Analysis of Coagulation Changes and Influencing Factors during Treatment of Acute Promyelocytic Leukemia.
10.19746/j.cnki.issn.1009-2137.2025.01.007
- Author:
Zhen-Zhu CHEN
1
;
Tao LIU
1
;
He-He GUO
1
;
Wen-Wen REN
1
;
Kai WANG
1
;
Ying-Xu PANG
1
Author Information
1. Department of Hematology, Zhoukou Central Hospital, Zhoukou 466000, Henan Province, China.
- Publication Type:Journal Article
- Keywords:
acute promyelocytic leukemia;
coagulation;
logistic regression model;
neural network model
- MeSH:
Humans;
Leukemia, Promyelocytic, Acute/therapy*;
Blood Coagulation;
Retrospective Studies;
Male;
Female;
Risk Factors;
Logistic Models;
Middle Aged;
Adult;
ROC Curve
- From:
Journal of Experimental Hematology
2025;33(1):45-53
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To analyze the changes in coagulation during the treatment of acute promyelocytic leukemia (APL) and explore the influencing factors of coagulation in patients with APL.
METHODS:Data of 166 APL patients admitted to our hospital from November 2018 to May 2023 were retrospectively analyzed, and the changes of various clinical indicators before and during treatment were compared. 166 APL patients were divided into abnormal coagulation group (n =115) and normal coagulation group (n =51) according to whether they experienced coagulation dysfunction. The basic information, clinical data and laboratory indicators of the two groups were compared. Multivariate logistic regression analysis was used to screen risk factors for coagulation dysfunction and established logistic regression model. Then we developed a neural network model and ranked the importance of the influencing factors, and used receiver operating characteristic (ROC) curves to evaluate the predictive performance of the two models.
RESULTS:The comparative results of various clinical indicators in 166 APL patients before and during treatment showed that systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triacylglycerol (TG), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), platelet (PLT) and fibrinogen (FIB) were significantly increased during the treatment (P < 0.05), while glycosylated hemoglobin (HbA1c), high density lipoprotein cholesterol (HDL-C), blood urea nitrogen (BUN), serum creatinine (SCr), high-sensitivity C reactive protein (hs-CRP), IL-6, TNF-α, TGF-β, white blood cells (WBC), absolute neutrophil count (ANC), prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer (D-D), fibrinogen degradation products (FDP) and lactate dehydrogenase (LDH) were significantly decreased during the treatment (P < 0.05). The proportion of patients with hemorrhage and high-risk APL in the abnormal coagulation group was significantly higher than that in the normal coagulation group (P < 0.05). The levels of IL-6, TNF-α, WBC, ANC, D-D, FDP and LDH in the abnormal coagulation group were significantly higher than those in the normal coagulation group (P < 0.05). The influencing factors selected by univariate analysis were incorporated into logistic regression analysis and neural network model to predict the risk of coagulation dysfunction in APL patients. ROC curves showed that the AUC of the two models were 096 and 0.908, the sensitivity were 0.824 and 0.892, the specificity were 0.940 and 0.904, the Youden index were 064 and 0.796, and the accuracy were 0.882 and 0.898, respectively.
CONCLUSION:High risk stratification, hemorrhage, elevated WBC, LDH, ANC and FDP levels are independent risk factors for coagulation dysfunction in APL patients. The logistic regression model and neural network model based on these risk factors demonstrate good predictive performance for coagulation dysfunction in APL patients.
