Comparative efficacy of five balloons for treating autogenous arteriovenous fistula stenosis: a Bayesian network meta-analysis

Liu, Yu; Wang, Jukun; Chen, Xin; Zhang, Chao; Luo, Tao

doi:10.21037/apm-21-2898

Cited by 3 publications

(1 citation statement)

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“…Hemodialysis is a pivotal therapeutic intervention for chronic renal failure and acute kidney injury. [1] Among these methodologies, the arteriovenous fistula (AVF) remains the principal vascular access route for patients undergoing maintenance hemodialysis (MHD), [2,3] showing considerable efficacy among MHD patients. [4] However, dysfunctional AVFs compromise therapeutic outcomes and quality of life and pose risks for grave complications, including infections, arterial aneurysms, bleeding, and thrombosis.…”

Section: Introductionmentioning

confidence: 99%

Retrospective analysis of predictive factors for AVF dysfunction in patients undergoing MHD

Wang,

Yang,

Zhao

2024

Medicine

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To construct an early clinical prediction model for AVF dysfunction in patients undergoing Maintenance Hemodialysis (MHD) and perform internal and external verifications. We retrospectively examined clinical data from 150 patients diagnosed with MHD at Hefei Third People’s Hospital from January 2014 to June 2023. Depending on arteriovenous fistula (AVF) functionality, patients were categorized into dysfunctional (n = 62) and functional (n = 88) cohorts. Using the least absolute shrinkage and selection operator(LASSO) regression model, variables potentially influencing AVF functionality were filtered using selected variables that underwent multifactorial logistic regression analysis. The Nomogram model was constructed using the R software, and the Area Under Curve(AUC) value was calculated. The model’s accuracy was appraised through the calibration curve and Hosmer–Lemeshow test, with the model undergoing internal validation using the bootstrap method. There were 11 factors exhibiting differences between the group of patients with AVF dysfunction and the group with normal AVF function, including age, sex, course of renal failure, diabetes, hyperlipidemia, Platelet count (PLT), Calcium (Ca), Phosphorus, D-dimer (D-D), Fibrinogen (Fib), and Anastomotic width. These identified factors are included as candidate predictive variables in the LASSO regression analysis. LASSO regression identified age, sex, diabetes, hyperlipidemia, anastomotic diameter, blood phosphorus, and serum D-D levels as 7 predictive factors. Unconditional binary logistic regression analysis revealed that advanced age (OR = 4.358, 95% CI: 1.454–13.062), diabetes (OR = 4.158, 95% CI: 1.243–13.907), hyperlipidemia (OR = 3.651, 95% CI: 1.066–12.499), D-D (OR = 1.311, 95% CI: 1.063–1.616), and hyperphosphatemia (OR = 4.986, 95% CI: 2.513–9.892) emerged as independent risk factors for AVF dysfunction in MHD patients. The AUC of the predictive model was 0.934 (95% CI: 0.897–0.971). The Hosmer-Lemeshow test showed high consistency between the model’s predictive results and actual clinical observations (χ2 = 1.553, P = .092). Internal validation revealed an AUC of 0.911 (95% CI: 0.866–0.956), with the Calibration calibration curve nearing the ideal curve. Advanced age, coexisting diabetes, hyperlipidemia, blood D-D levels, and hyperphosphatemia are independent risk factors for AVF dysfunction in patients undergoing MHD.

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Section: Introductionmentioning

confidence: 99%