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A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial
A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial
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A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial
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A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial
A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial

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A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial
A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial
Journal Article

A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial

2025
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Overview
Current hypertension guidelines lack personalized strategies for blood pressure control. While the Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated benefits of intensive blood pressure lowering, identifying optimal candidates for such treatment remains challenging. We developed and validated a risk stratification model using data from 9139 SPRINT participants. The model incorporated 11 clinical variables through multivariable Cox regression analysis. Patients were stratified into low‐, medium‐, and high‐risk groups. The study protocol was registered at ClinicalTrials.gov (NCT01206062). The model showed good discrimination with C‐indices of 0.7354 (95% CI: 0.7065–0.7710) and 0.6894 (95% CI: 0.6545–0.7266) at 3 years for training and validation sets, respectively. Intensive treatment significantly reduced cardiovascular events in medium‐risk (3.17% vs. 5.11%, p = 0.0376) and high‐risk groups (9.34% vs. 11.86%, p = 0.0269), while showing a nonsignificant trend in the low‐risk group (2.87% vs. 3.34%, p = 0.0870). The Rank‐Weighted Average Treatment Effect analysis (16.06) supported potential benefits from individualized treatment allocation. No increased risk of severe adverse events was observed across risk groups. Our risk stratification model effectively identifies patients who derive significant cardiovascular benefits from intensive blood pressure lowering, particularly in medium‐ and high‐risk groups. This approach could guide more personalized hypertension management strategies.