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Purpose <p>This study aimed to develop a prediction model to preoperatively identify patients with unilateral severe renal artery stenosis (RAS) most likely to experience blood pressure improvement after revascularization.</p> Methods <p>In this retrospective study, 193 patients with unilateral severe RAS who underwent revascularization between 2018 and 2024 were analyzed. LASSO regression was used for variable selection, and the selected predictors were entered into a multivariable logistic regression model to develop the nomogram based on clinical and Doppler ultrasound parameters. Model performance was evaluated through internal validation and temporal validation.</p> Results <p>The final model included acceleration time (AT) ≥ 70 ms, estimated glomerular filtration rate (eGFR) ≥ 30 mL/min/1.73&#xa0;m², resistive index (RI) of the contralateral kidney and new-onset hypertension. The model demonstrated a C-statistic of 0.907 and a Brier score of 0.12. Its discriminative ability was confirmed by internal validation (C-statistic 0.888, 95% CI 0.797–0.961), and it achieved a C-statistic of 0.826 with a Brier score of 0.17 in the temporal validation cohort. Decision curve analysis confirmed clinical utility across a threshold probability range of 22–82%.</p> Conclusion <p>This nomogram may serve as an effective tool for estimating the probability of blood pressure response to revascularization and support preprocedural risk stratification in patients with unilateral severe RAS.</p>

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A doppler ultrasound–based model for predicting blood pressure response after revascularization in unilateral severe renal artery stenosis

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Fuzhou Yi,
  • Zhifang Yang,
  • Enheng Cai,
  • Yifei Yu,
  • Xiaofeng Ni,
  • Yunyun Hu,
  • Xiaoyu Li,
  • Zhe Zhang,
  • Yuanyuan Kang,
  • Jianzhong Xu,
  • Ri Ji

摘要

Purpose

This study aimed to develop a prediction model to preoperatively identify patients with unilateral severe renal artery stenosis (RAS) most likely to experience blood pressure improvement after revascularization.

Methods

In this retrospective study, 193 patients with unilateral severe RAS who underwent revascularization between 2018 and 2024 were analyzed. LASSO regression was used for variable selection, and the selected predictors were entered into a multivariable logistic regression model to develop the nomogram based on clinical and Doppler ultrasound parameters. Model performance was evaluated through internal validation and temporal validation.

Results

The final model included acceleration time (AT) ≥ 70 ms, estimated glomerular filtration rate (eGFR) ≥ 30 mL/min/1.73 m², resistive index (RI) of the contralateral kidney and new-onset hypertension. The model demonstrated a C-statistic of 0.907 and a Brier score of 0.12. Its discriminative ability was confirmed by internal validation (C-statistic 0.888, 95% CI 0.797–0.961), and it achieved a C-statistic of 0.826 with a Brier score of 0.17 in the temporal validation cohort. Decision curve analysis confirmed clinical utility across a threshold probability range of 22–82%.

Conclusion

This nomogram may serve as an effective tool for estimating the probability of blood pressure response to revascularization and support preprocedural risk stratification in patients with unilateral severe RAS.