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Severe complications of acute cerebral infarction (ACI) can have a significant impact on patient prognosis. As a marker of coagulation/fibrinolysis activation, D-dimer is valuable in predicting these complications. The two studies focus on cerebrovascular-cardiac syndrome (CCS) and hemorrhagic transformation (HT) following thrombolysis, respectively, and aim to investigate the predictive performance of D-dimer in combination with other markers for specific complications. This will provide a basis for the early clinical identification of high-risk patients.
Case-control study on cerebrovascular-cardiac syndrome [1]: A total of 146 elderly patients with ACI were enrolled and divided into a CCS group (56 patients) and a non-CCS group (90 patients). The baseline characteristics and levels of D-dimer (D-D), troponin T (TNT) and pro-brain natriuretic peptide (pro-BNP) were compared between the two groups. Independent risk factors were identified using multivariate logistic regression analysis, and ROC curves were plotted to assess the predictive value of individual and combined markers.
Study on Hemorrhagic Transformation (Case-Control) [2]: The study included 166 ACI patients who underwent intravenous thrombolysis with alteplase and were divided into an HT group (36 patients) and a non-HT group (130 patients). Following univariate analysis, variables showing statistically significant differences were incorporated into a decision tree analysis to develop a risk stratification model, and the predictive performance of this model was evaluated using ROC curves.
In the CCS group, levels of D-dimer, troponin T (TNT) and brain natriuretic peptide (pro-BNP) were all significantly higher than in the non-CCS group (P < 0.05). Age, hypertension, D-dimer, TNT and pro-BNP were independent risk factors for CCS (P < 0.05).
Individual Prediction: At a D-dimer cutoff value of 2.05 mg/L, the area under the curve (AUC) was 0.717 (sensitivity 68.7%, specificity 87.2%); at a troponin T cutoff value of 38.41 ng/L, the AUC was 0.799; and at a brain natriuretic peptide (BNP) cutoff value of 1268.36 pg/mL, the AUC was 0.743.
Combined prediction: The area under the curve (AUC) for the combination of the three markers increased to 0.825 (sensitivity 72.0%, specificity 83.3%), which outperforms any single marker.
In the HT group, markers such as D-D, TNF-α and FIB exhibited statistically significant differences compared to the non-HT group (P < 0.05).
The decision tree model identified five independent risk factors: D-dimer >2.58 mg/L 24 hours after thrombolysis; TNF-α >161.74 ng/L; NIHSS score >15 before thrombolysis; fibrinogen <2.25 mg/L 24 hours after thrombolysis; and a history of extensive cerebral infarction.
The area under the ROC curve (AUC) for this model was 0.909 (95% confidence interval (CI): 0.874–0.945), demonstrating excellent predictive performance with a sensitivity of 86.11% and a specificity of 92.31%.
D-dimer testing is a valuable tool for predicting complications of ACI. In predicting CCS, combining D-dimer with troponin T (TNT) and N-terminal pro-brain natriuretic peptide (pro-BNP) improves predictive performance (area under the curve (AUC) of 0.825) [1]. In predicting heart failure (HT), a decision tree model based on D-dimer and other factors achieved an AUC of 0.909, demonstrating excellent risk stratification capability [2]. Therefore, clinicians should prioritize the dynamic monitoring of D-dimer levels and combine this with other indicators to achieve an early, precise warning of complications.
References:
[1] Han Jing, Zhang Jing, Ding Yan. The Prognostic Value of Serum D-Dimer, Troponin T, and Brain Natriuretic Peptide Prohormone in Predicting Cerebral-Cardiac Syndrome in Elderly Patients with Acute Cerebral Infarction [J]. Armed Police Medicine. 2025, 36(8): 674-678
[2] Yang Hua, Li Zhiwen, Cao Minglei, Yin Haiqing, Wei Yuqing. “Fibrinogen, Tumor Necrosis Factor-α, and D-dimer Can Predict the Risk of Hemorrhagic Transformation After Thrombolysis in Patients with Acute Cerebral Infarction.” Journal of Internal Medicine and Critical Care, 2023, 29(4): 293-297
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