A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension
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 clinica...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1637388/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849430060886392832 |
|---|---|
| author | Yishuan Zhang Ruiqing He Chen Chen Hong Zhang Lingyan Li Rongxue Xiao Shuangyu Chen Shuyi Wu Zongjun Liu Junqing Gao |
| author_facet | Yishuan Zhang Ruiqing He Chen Chen Hong Zhang Lingyan Li Rongxue Xiao Shuangyu Chen Shuyi Wu Zongjun Liu Junqing Gao |
| author_sort | Yishuan Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9bcba447e0fc435d876de75547654c03 |
| institution | Kabale University |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-9bcba447e0fc435d876de75547654c032025-08-20T03:28:09ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-07-011210.3389/fcvm.2025.16373881637388A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertensionYishuan Zhang0Ruiqing He1Chen Chen2Hong Zhang3Lingyan Li4Rongxue Xiao5Shuangyu Chen6Shuyi Wu7Zongjun Liu8Junqing Gao9Shanghai Putuo Central School of Clinical Medicine, Anhui Medical University, Shanghai, ChinaDepartment of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaDepartment of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaShanghai Putuo Central School of Clinical Medicine, Anhui Medical University, Shanghai, ChinaDepartment of Cardiology, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaObjectiveTo 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.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1637388/fullrenal denervationclinical prediction modelresistant hypertensionindex of microvascular resistanceblood pressure change |
| spellingShingle | Yishuan Zhang Ruiqing He Chen Chen Hong Zhang Lingyan Li Rongxue Xiao Shuangyu Chen Shuyi Wu Zongjun Liu Junqing Gao A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension Frontiers in Cardiovascular Medicine renal denervation clinical prediction model resistant hypertension index of microvascular resistance blood pressure change |
| title | A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension |
| title_full | A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension |
| title_fullStr | A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension |
| title_full_unstemmed | A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension |
| title_short | A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension |
| title_sort | clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension |
| topic | renal denervation clinical prediction model resistant hypertension index of microvascular resistance blood pressure change |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1637388/full |
| work_keys_str_mv | AT yishuanzhang aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT ruiqinghe aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT chenchen aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT hongzhang aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT lingyanli aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT rongxuexiao aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT shuangyuchen aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT shuyiwu aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT zongjunliu aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT junqinggao aclinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT yishuanzhang clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT ruiqinghe clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT chenchen clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT hongzhang clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT lingyanli clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT rongxuexiao clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT shuangyuchen clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT shuyiwu clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT zongjunliu clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension AT junqinggao clinicalpredictionmodelforbloodpressurechangesafterrenaldenervationinpatientswithresistanthypertension |