Easy-to-use tool for evaluating the elevated acute kidney injury risk against reduced cardiovascular disease risk during intensive blood pressure control

被引:8
|
作者
Venalainen, Mikko S. [1 ,2 ]
Klen, Riku [1 ,2 ]
Mahmoudian, Mehrad [1 ,2 ,3 ]
Raitakari, Olli T. [4 ,5 ,6 ,7 ]
Elo, Laura L. [1 ,2 ]
机构
[1] Univ Turku, Turku Biosci Ctr, Tykistokatu 6 A, FI-20520 Turku, Finland
[2] Abo Akad Univ, Tykistokatu 6 A, FI-20520 Turku, Finland
[3] Univ Turku, Dept Future Technol, Turku, Finland
[4] Univ Turku, Res Ctr Appl & Prevent Cardiovasc Med, Turku, Finland
[5] Univ Turku, Ctr Populat Hlth Res, Turku, Finland
[6] Turku Univ Hosp, Turku, Finland
[7] Turku Univ Hosp, Dept Clin Physiol & Nucl Med, Turku, Finland
基金
欧洲研究理事会; 芬兰科学院; 欧盟地平线“2020”;
关键词
acute kidney injury; antihypertensive agents; cardiovascular diseases; clinical decision support; hypertension; machine learning; TRIAL; DESIGN; ACCORD; INDEX;
D O I
10.1097/HJH.0000000000002282
中图分类号
R6 [外科学];
学科分类号
1002 ; 100210 ;
摘要
Objective: The Systolic Blood Pressure Intervention Trial (SPRINT) reported that lowering SBP to below 120 mmHg (intensive treatment) reduced cardiovascular morbidity and mortality among adults with hypertension but increased the incidence of adverse events, particularly acute kidney injury (AKI). The goal of this study was to develop an accurate risk estimation tool for comparing the risk of cardiovascular events and adverse kidney-related outcomes between standard and intensive antihypertensive treatment strategies. Methods: By applying Lasso regression on the baseline characteristics and health outcomes of 8760 participants with complete baseline information in the SPRINT trial, we developed predictive models for primary cardiovascular disease (CVD) outcome and incidence of AKI. Both models were validated against an independent test set of the SPRINT trial (one third of data not used for model building) and externally against the cardiovascular and renal outcomes available in Action to Control Cardiovascular Risk in Diabetes Blood Pressure trial, consisting of 4733 participants with type 2 diabetes mellitus. Results: Lasso regression identified a subset of variables that accurately predicted the primary CVD outcome and the incidence of AKI (areas under receiver-operating characteristic curves 0.70 and 0.77, respectively). Based on the validated risk models, an easy-to-use risk assessment tool was developed and made available as an easy-to-use online tool. Conclusion: By predicting the risks of CVD and AKI at baseline, the developed tool can be used to weigh the benefits of intensive versus standard blood pressure control and to identify those who are likely to benefit most from intensive treatment.
引用
收藏
页码:511 / 518
页数:8
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