Appraising non-HDL-C, systolic pressure, and a nomogram-based diagnostic model as auxiliary biomarkers in confirming acute ischemic stroke and transient ischemic attack

Acute ischemic stroke (AIS) is characterized by the abrupt onset of neurological dysfunction stemming from focal brain ischemia, confirmed through imaging evidence of infarction. In contrast, transient ischemic attack (TIA) manifests with neurological deficits in the absence of infarction, with imaging serving as the definitive diagnostic criterion. This study aims to assess the diagnostic value of combining non-high-density lipoprotein cholesterol (non-HDL-C) and blood pressure (BP) in differentiating AIS from TIA. We recruited 207 untreated AIS patients diagnosed within 72 h and 99 age- and gender-matched TIA patients. Upon admission, serum non-HDL-C levels, other lipid profiles, and BP measurements were obtained. Binary logistic regression was employed to identify risk factors, while receiver operator characteristic (ROC) curves were used to evaluate diagnostic performance. Furthermore, least absolute shrinkage and selection operator (LASSO) regression coupled with multivariate logistic regression was utilized to develop a nomogram model. The AIS group exhibited higher prevalence rates of hypertension, diabetes, family history of diabetes, and smoking (P < 0.05). Notably, non-HDL-C, systolic BP, diastolic BP, and other lipid markers were significantly elevated in the AIS group (P < 0.05). Multivariate analysis pinpointed non-HDL-C (OR [odds ratio] = 1.663, 95% CI [confidence interval]: 1.239-2.234, P < 0.01) and systolic BP (OR = 1.035, 95% CI: 1.012-1.057, P < 0.01) as independent risk factors. ROC analysis revealed that systolic BP alone achieved an AUC of 0.686 (sensitivity: 78.7%, specificity: 51.5%), whereas the combination of systolic BP and non-HDL-C enhanced diagnostic accuracy (AUC [area under the ROC curve] = 0.736, sensitivity: 75.4%, specificity: 64.6%). A nomogram incorporating low-density lipoprotein cholesterol (LDL-C), glucose (GLU), homocysteine, and smoking demonstrated high predictive accuracy, with training and validation AUCs of 0.769 and 0.704, respectively. Non-HDL-C and systolic BP emerge as independent risk factors for AIS, and their combined use augments diagnostic precision in differentiating AIS from TIA. A nomogram model presents a practical differentiation tool, particularly in settings with limited resources.