Table 2_A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension.pdf

ObjectiveTo develop clinical prediction models to estimate blood pressure changes in hypertensive patients undergoing renal denervation (RDN).
MethodsThis 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.
ResultsIn 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).
ConclusionThis 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.

Clinical Trial RegistrationClinicalTrials.gov, identifier, ChiCTR2200058696.