Zhang Yishuan, He Ruiqing, Chen Chen, Zhang Hong, Li Lingyan, Xiao Rongxue, Chen Shuangyu, Wu Shuyi, Liu Zongjun, Gao Junqing
Shanghai Putuo Central School of Clinical Medicine, Anhui Medical University, Shanghai, China.
Department of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Cardiovasc Med. 2025 Jul 21;12:1637388. doi: 10.3389/fcvm.2025.1637388. eCollection 2025.
To develop clinical prediction models to estimate blood pressure changes in hypertensive patients undergoing renal denervation (RDN).
This single-center, prospective interventional study enrolled 70 hypertensive patients undergoing RDN between July 2022 and December 2023, with clinical data collected systematically before and after the procedure. Variable selection for modeling was performed through a rigorous process incorporating univariate analysis and clinical relevance assessment. Subsequently, two distinct clinical prediction models were developed and subjected to comparative evaluation. The primary outcomes were the absolute changes in systolic blood pressure (SBP) and diastolic blood pressure (DBP) at 6 months after RDN.
In both Ordinary Least Squares (OLS) and Ridge regression models, seven variables [including index of microvascular resistance (IMR), preoperative SBP, age and creatinine] were significantly associated with SBP change, while four variables were significantly associated with DBP change. In the prediction model on SBP change, compared to the OLS model, the Ridge regression exhibited lower prediction errors [mean absolute error [MAE]: 6.40 vs. 6.95; mean squared error [MSE]: 65.58 vs. 76.15] and a higher R² (0.79 vs. 0.72). In the DBP model, the Ridge regression also achieved a lower MAE (3.62 vs. 3.73) and a higher R² (0.77 vs. 0.71).
This study developed and compared predictive models for estimating blood pressure response at 6-month follow-up after RDN in patients with resistant hypertension. The Ridge regression model exhibited superior predictive accuracy and model stability. The model indicated that IMR was a factor associated with postoperative blood pressure reduction.
ClinicalTrials.gov, identifier, ChiCTR2200058696.
开发临床预测模型，以估计接受肾去神经支配术（RDN）的高血压患者的血压变化。
这项单中心前瞻性干预研究纳入了2022年7月至2023年12月期间接受RDN的70例高血压患者，在手术前后系统收集临床数据。通过纳入单变量分析和临床相关性评估的严格过程进行建模的变量选择。随后，开发了两个不同的临床预测模型并进行比较评估。主要结局是RDN术后6个月时收缩压（SBP）和舒张压（DBP）的绝对变化。
在普通最小二乘法（OLS）和岭回归模型中，七个变量[包括微血管阻力指数（IMR）、术前SBP、年龄和肌酐]与SBP变化显著相关，而四个变量与DBP变化显著相关。在SBP变化预测模型中，与OLS模型相比，岭回归表现出更低的预测误差[平均绝对误差（MAE）：6.40对6.95；均方误差（MSE）：65.58对76.15]和更高的R²（0.79对0.72）。在DBP模型中，岭回归也实现了更低的MAE（3.62对3.73）和更高的R²（0.77对0.71）。
本研究开发并比较了预测模型，以估计顽固性高血压患者RDN术后6个月随访时的血压反应。岭回归模型表现出卓越的预测准确性和模型稳定性。该模型表明IMR是与术后血压降低相关的一个因素。
ClinicalTrials.gov，标识符，ChiCTR2200058696。
