2型糖尿病合并肾病患者心脏瓣膜钙化风险预测模型的建立与验证

王艳辉.2型糖尿病合并肾病患者心脏瓣膜钙化风险预测模型的建立与验证[J].内科急危重症杂志,2025,31(4):316-320

DOI：10.11768/nkjwzzzz20250405

英文关键词:

基金项目:

作者	单位	E-mail
王艳辉	保定市中医院	307764622@qq.com

摘要点击次数: 11

全文下载次数: 14

中文摘要:

摘要目的：探究2型糖尿病（T2DM）合并肾病患者心脏瓣膜钙化（CVC）的影响因素。方法：选择100例T2DM合并肾病患者为研究对象，按照有无CVC分为钙化组32例和非钙化组68例。采用多因素Logistic回归分析T2DM合并肾病患者CVC的独立危险因素，构建预测模型并验证。通过限制性立方样条模型分析残肾功能（RRF）与CVC之间的关系。结果：与非钙化组比较，钙化组患者T2DM病程较长、血尿素氮（BUN）、肌酐（SCr）、成纤维细胞生长因子23（FGF23）、C反应蛋白（CRP）、血磷、全段甲状旁腺激素（iPTH）水平较高，血白蛋白（ALB）水平、肾小球滤过率（eGFR）、RRF较低（P均<0.05）。多因素Logistic回归分析显示ALB<35.64 g/L、血磷>1.97 mmol/L、iPTH>123.75 pg/mL为T2DM合并肾病患者发生CVC的独立危险因素（P均＜0.05），RRF≥1.00 mL/min为保护因素（P＜0.05）。限制性立方样条模型分析显示， RRF与T2DM合并肾病患者发生CVC存在非线性剂量-反应关系，随着RRF的下降，CVC风险增加。构建T2DM合并肾病患者CVC的预测模型的方程为P=ea/(l+ea)，a=-7.432+0.909×ALB+1.188×血磷+0.886×iPTH-0.970×RRF。模型的一致性指数（C-index）为0.856（95%CI: 0.827~0.874），校正曲线与理想曲线拟合良好，模型具有较高的准确性。受试者工作特征（ROC）曲线下面积（AUC）为0.862（95%CI: 0.831~0.883）。预测模型的临床决策曲线阈值概率在0.01～0.95，净获益率＞0，提示其有效性较好。结论：ALB<35.64 g/L、血磷>1.97 mmol/L、iPTH>123.75 pg/mL为T2DM合并肾病患者发生CVC的独立危险因素，RRF≥1.00 mL/min为保护因素。T2DM合并肾病患者肾功能不全可增加CVC风险。

英文摘要:

Abstract Objective: To explore the influencing factors of cardiac valve calcification (CVC) in type 2 diabetes mellitus (T2DM) patients with nephropathy. Methods: A total of 100 patients with T2DM complicated with kidney disease were selected as the research subjects, and were divided into a calcification group of 32 cases and a non-calcification group of 68 cases according to the presence or absence of CVC. The multiple logistic regression analysis was done to identify independent risk factors for CVC in T2DM patients with concomitant kidney disease, constructing a predictive model and validating it. The relationship between residual renal function (RRF) and CVC was analyzed using a restricted cubic spline model. Results: The patients in the calcification group had a longer course of T2DM, higher levels of urea nitrogen (BUN), creatinine (SCr), fibroblast growth factor 23 (FGF23), C-reactive protein (CRP), blood phosphorus, whole parathyroid hormone (iPTH), lower levels of albumin (ALB), glomerular filtration rate (eGFR), and RRF than in the non-calcification group (all P<0.05). Multivariate logistic regression analysis showed that ALB< 35.64 g/L, blood phosphorus> 1.97 mmol/L, and iPTH> 123.75 pg/mL were independent risk factors for CVC in T2DM patients with kidney disease (all P<0.05), while RRF≥ 1.00 mL/min was a protective factor (P< 0.05). The analysis of the restricted cubic spline model showed that there was a non-linear dose-response relationship between RRF and CVC in T2DM patients with kidney disease. As RRF decreased, the risk of CVC increased. The equation for constructing a predictive model of CVC in patients with T2DM complicated with kidney disease was as follows: P=ea/(l+ea), a=-7.432+0.909 × whether ALB< 35.64 g/L (1 or 0)+1.188×whether blood phosphorus >1.97 mmol/L (1 or 0)+0.886×whether iPTH >123.75 pg/mL (1 or 0) -0.970×whether RRF >1.00 mL/min (1 or 0). The consistency index (C-index) of the model was 0.856 (95% CI: 0.827-0.874), and the calibration curve fitted well with the ideal curve, indicating that the model had high accuracy. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.862 (95% CI: 0.831-0.883). The threshold probability of the clinical decision curve of the prediction model was between 0.01 and 0.95, and the net benefit rate was greater than 0, indicating its good effectiveness. Conclusion: ALB< 35.64 g/L, blood phosphorus> 1.97 mmol/L and iPTH> 123.75 pg/mL are independent risk factors for CVC in T2DM patients with concomitant kidney disease, while RRF≥ 1.00 mL/min is a protective factor. Renal dysfunction in patients with T2DM combined with kidney disease can increase the risk of CVC.