The atherogenic index of plasma (AIP) is a marker of atherosclerosis, while frailty reflects cumulative physiological decline. However, the combined impact

The purpose of this study was to determine predictors of hemodialysis access patency after drug-coated balloon angioplasty (DCBA) in patients with arteriovenous fistula (AVF). The clinical and imaging data of patients with hemodialysis access dysfunction treated with drug-coated balloon (DCB) in our hospital from January 2020 to January 2024 were analyzed retrospectively, the patency of hemodialysis access was followed up. The length of the DCB is 4 cm. Kaplan-Meier analysis was performed for determination of primary patency and secondary patency in this population. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify predictors of primary patency after DCBA. A total of 173 hemodialysis patients treated with DCB were included in the study. The median survival time of primary and secondary patency were 443 and 1035 days, and the primary patency rates at 6, 12, 18, 24, 30 and 36 months were 85.9%, 64.1%, 34.1%, 21.3%, 12.0% and 6.0%, respectively. The secondary patency rates at 6, 12, 18, 24, 30 and 36 months were 98.8%, 93.8%, 89.8%, 81.9%, 76.1% and 48.6%, respectively. Multivariate analysis using Cox proportional hazard regression models showed that lesion length>4 cm(p=0.002), tandem stenosis(p=0.007) and thrombosis (p < 0.001) were independent risk factors for primary patency after DCBA in patients with AVF. However, lesion length (p=0.021), residual stenosis (p=0.022), and thrombosis (p=0.018) were the independent predictors of primary patency in patients with single lesions.Lesion length>4 cm, tandem stenosis and thrombosis were found to be predictors of primary patency after using 4 cm length DCB. However, in patients with single lesions, lesion length, residual stenosis, and thrombosis serve as independent predictors of primary patency.

Background: The atherogenic index of plasma (AIP) is a marker of lipids and atherosclerosis. However, the association between the AIP and the risk of unstable carotid plaque remains unclear. Methods: A total of 10,732 patients were enrolled. Carotid ultrasound was used to assess the stability of carotid plaque. The AIP was calculated using the formula AIP = log (TG/HDL-C). Logistic regression was used to investigate the association between the AIP and unstable carotid plaque. The nonlinear association between the AIP and unstable carotid plaque was analyzed using restricted cubic splines (RCSs), and a two-segment logistic regression risk model was constructed for both sides of the inflection point. Results: Of the total number of patients, 7834 had stable carotid plaque and 2898 had unstable carotid plaque. Our findings demonstrated an inverted U-shaped association between the AIP and the risk of unstable carotid plaque (threshold = 0.10). When the AIP was <0.10, a significant positive association with unstable carotid plaque emerged (OR: 1.24, 95% CI: 1.07, 1.44); when the AIP was ≥0.10, it showed a significant negative association with unstable carotid plaque (OR: 0.78, 95% CI: 0.70, 0.88). Conclusions: Our findings demonstrated an inverted U-shaped association between the AIP and the risk of unstable carotid plaque, with a threshold of 0.10. The AIP could serve as a potential biomarker for unstable carotid plaque risk assessment.