Hemodialysis Center, Hefei Third Clinical College (Hefei Third People's Hospital), Anhui Medical University, Hefei, China.
Department of Nephrology, Hefei Third Clinical College (Hefei Third People's Hospital), Anhui Medical University, Hefei, China.
Medicine (Baltimore). 2024 Apr 19;103(16):e37737. doi: 10.1097/MD.0000000000037737.
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.
构建维持性血液透析(MHD)患者动静脉瘘功能障碍的早期临床预测模型,并进行内部和外部验证。我们回顾性分析了 2014 年 1 月至 2023 年 6 月在合肥市第三人民医院诊断为 MHD 的 150 例患者的临床资料。根据动静脉瘘(AVF)功能,将患者分为功能障碍组(n=62)和功能正常组(n=88)。使用最小绝对收缩和选择算子(LASSO)回归模型,通过多因素逻辑回归分析筛选出可能影响 AVF 功能的变量。使用 R 软件构建列线图模型,并计算曲线下面积(AUC)值。通过校准曲线和 Hosmer-Lemeshow 检验评估模型的准确性,使用 bootstrap 方法对模型进行内部验证。AVF 功能障碍组和 AVF 功能正常组之间存在 11 个因素差异,包括年龄、性别、肾衰竭病程、糖尿病、高脂血症、血小板计数(PLT)、钙(Ca)、磷、D-二聚体(D-D)、纤维蛋白原(Fib)和吻合口宽度。这些鉴定出的因素被纳入 LASSO 回归分析的候选预测变量。LASSO 回归确定年龄、性别、糖尿病、高脂血症、吻合口直径、血磷和血清 D-D 水平为 7 个预测因素。无条件二元逻辑回归分析显示,高龄(OR=4.358,95%CI:1.454-13.062)、糖尿病(OR=4.158,95%CI:1.243-13.907)、高脂血症(OR=3.651,95%CI:1.066-12.499)、D-D(OR=1.311,95%CI:1.063-1.616)和高磷血症(OR=4.986,95%CI:2.513-9.892)是 MHD 患者 AVF 功能障碍的独立危险因素。预测模型的 AUC 为 0.934(95%CI:0.897-0.971)。Hosmer-Lemeshow 检验显示模型预测结果与实际临床观察具有高度一致性(χ2=1.553,P=0.092)。内部验证显示 AUC 为 0.911(95%CI:0.866-0.956),校准校准曲线接近理想曲线。高龄、合并糖尿病、高脂血症、血 D-D 水平和高磷血症是 MHD 患者动静脉瘘功能障碍的独立危险因素。