Predictive value of conventional ultrasonography combined with three-dimensional speckle tracking imaging for maturation of autologous arteriovenous fistulas in hemodialysis patients

To develop and validate a predictive model for autologous arteriovenous fistula (AVF) maturation in hemodialysis patients using conventional ultrasonography and three-dimensional speckle tracking imaging.

This case-control study enrolled 200 AVF patients from Chongqing Hospital of Traditional Chinese Medicine from July 2021 to June 2024. Clinical data, vascular ultrasound, and cardiac ultrasound parameters were systematically collected. After applying predefined inclusion criteria, 186 patients were stratified into 2 cohorts based on arteriovenous fistula (AVF) maturation status: the spontaneous maturation group (n=111) and the assisted maturation requirement group (n=75). Comparative analysis between the 2 cohorts was conducted using univariate and multivariate logistic regression for variable selection, leading to the construction of a predictive model (model1) for spontaneous AVF maturation. A nomogram was subsequently developed based on model1. Internal validation was performed through 1000 bootstrap resamples with calibration curve analysis. Model discrimination was quantified by the area under the receiver operating characteristic curve (AUC), while clinical utility was assessed via decision curve analysis (DCA). After excluding 104 patients lacking three-dimensional speckle tracking echocardiography data, the remaining 82 subjects were included in novel predictive model development. Three strain parameters, two-dimensional global longitudinal strain (2DGLS), three-dimensional global longitudinal strain (3DGLS), and three-dimensional left ventricular ejection fraction (3DEF), were independently incorporated into multivariable logistic regression analyses to establish three distinct models (designated as model2, model3 and model4 respectively). Model comparisons employed AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Independent predictors for model1 included: 2DEF (OR=1.133, 95%CI: 1.058~1.213), mid-cephalic vein depth (OR=1.453, 95%CI: 1.068~1.978), distal cephalic vein diameter (OR=2.141, 95%CI: 1.120~4.091), post-occlusive brachial artery resistance index (OR=0.004, 95%CI: 0.000~0.140), and postoperative brachial flow (OR=1.004, 95% CI: 1.002~1.007). model1 demonstrated excellent discrimination (AUC=0.869, 95%CI: 0.817~0.921) and calibration (mean absolute error=0.017). DCA showed superior net benefit at 0.1~1.0 threshold probabilities. Compared with model1, non-significant improvements in AUC and IDI, while model4 achieved significant NRI improvements (P<0.05).

The prediction performance of AVF natural maturity prediction models constructed with 2DGLS, 3DGLS, 3DEF, or 2DEF is relatively high; The NRI of the model involving 3DEF is better than that of the model involving 2DEF, indicating that it may have better clinical application value within a specific threshold probability range.