Analysis of coagulation and fibrinolysis biomarkers for prognostic assessment and clinical efficacy evalua-tion in patients with intracerebral hemorrhage
10.3969/j.issn.1006-5725.2025.12.011
- VernacularTitle:凝血纤溶标志物检测对脑出血患者预后的分析和临床效能评价
- Author:
Shouping LIU
1
;
Yinlin TANG
;
Yanfang CHENG
;
Qian ZHOU
Author Information
1. 南方医科大学南方医院检验医学科(广东 广州 510515)
- Publication Type:Journal Article
- Keywords:
intracerebral hemorrhage;
coagulation-fibrinolysis biomarkers;
thrombin-antithrombin complex;
d-dimer;
prognostic prediction;
lasso regression;
logistic regression
- From:
The Journal of Practical Medicine
2025;41(12):1846-1852
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the prognostic implications of coagulation-fibrinolysis biomarkers in intracerebral hemorrhage(ICH)and to construct a multivariable logistic regression model for individualized risk prediction.Methods A total of 101 ICH patients who were admitted to Nanfang Hospital of Southern Medical University from January 2020 to December 2023 were retrospectively enrolled.These patients were stratified into a poor outcome group(ΔGCS≤0)and a good outcome group(ΔGCS>0)according to the difference in Glasgow Coma Scale(GCS)scores between discharge and admission.Coagulation and fibrinolysis markers collected upon admission were analyzed.The Least Absolute Shrinkage and Selection Operator(LASSO)regression was employed to screen variables.A logistic regression model was constructed using 70%of the cases(the training set),while the remaining 30%were utilized for validation.The performance of the model was evaluated through receiver operating characteristic(ROC)curves,calibration plots,Hosmer-Lemeshow goodness-of-fit test,and decision curve analysis(DCA).Results Univariate analysis indicated that thrombin-antithrombin complex(TAT),D-dimer,and age exhibited significant differences between the two outcome groups(P<0.05).These three variables were selected via LASSO regression and incorporated into the logistic model.The final model equation was expressed as:logit(P)=-6.234+1.132×TAT+0.867×D-dimer+0.699×Age.In the training set,the area under the ROC curve(AUC)was 0.795.The calibration curve demonstrated excellent agreement between the predicted and observed outcomes,with a Hosmer-Lemeshow test P-value of 0.8568.DCA revealed that the model achieved net clinical benefit across a broad range of risk thresholds(0.1～0.8).Conclusions TAT,D-dimer,and age are independent predictors of poor prognosis in patients with ICH.The logistic regression model based on these variables demon-strates favorable discriminatory ability and clinical utility.The nomogram derived from this model enables individu-alized risk assessment and may aid clinicians in early prognostic evaluation and treatment planning.
