Integrating Inflammation and Lipid Metabolism Biomarkers for Early Risk Stratification in Acute Cerebral Infarction: A Nomogram-Based Approach.

To assess the prognostic value of the homocysteine-to-high-density lipoprotein cholesterol (Hcy/HDL-C) ratio and the high-sensitivity C-reactive protein-to-albumin (hs-CRP/Alb) ratio as biomarkers for predicting functional outcomes in acute cerebral infarction (ACI) patients treated with recombinant tissue plasminogen activator (rt-PA).

A retrospective analysis was conducted on 204 ACI patients who received rt-PA. Patients were classified into two groups based on their functional outcomes at 3 months poststroke: good (modified Rankin scale [mRS] ≤ 2) and poor (mRS > 2). Logistic regression and restricted cubic spline (RCS) analyses were performed to evaluate the association between Hcy/HDL-C and hs-CRP/Alb ratios with functional outcomes. A predictive nomogram was developed incorporating these biomarkers, baseline NIHSS scores, and atrial fibrillation. The performance of this nomogram was compared to traditional risk models.

Elevated Hcy/HDL-C and hs-CRP/Alb ratios were identified as independent predictors of poor functional outcomes in ACI patients (p < 0.05). The nomogram, incorporating these biomarkers along with NIHSS scores and atrial fibrillation, demonstrated superior predictive performance with a C-index of 0.936 and an AUC of 0.935, outperforming traditional risk models (C-index = 0.782). Subgroup analysis revealed that Hcy/HDL-C was more predictive in patients with large-artery atherosclerosis (LAA), while hs-CRP/Alb showed stronger prognostic value in patients with cardioembolic strokes.

The Hcy/HDL-C and hs-CRP/Alb ratios serve as independent and valuable biomarkers for predicting poor outcomes in ACI patients post-rt-PA treatment. The nomogram incorporating these biomarkers provides superior prognostic accuracy and could be a useful tool for personalized risk assessment and management in ACI patients following thrombolytic therapy.
Cardiovascular diseases
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Authors

Ding Ding, Dai Dai, Cao Cao, Wang Wang, Zhu Zhu
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