Mikko S Venäläinen1, Riku Klén1, Mehrad Mahmoudian1,2, Olli T Raitakari3,4,5, Laura L Elo1. 1. Turku Bioscience Centre, University of Turku and Åbo Akademi University. 2. Department of Future Technologies. 3. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku. 4. Centre for Population Health Research, University of Turku and Turku University Hospital. 5. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland.
Abstract
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.
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.
Authors: Mikko S Venäläinen; Valtteri J Panula; Riku Klén; Jaason J Haapakoski; Antti P Eskelinen; Mikko J Manninen; Jukka S Kettunen; Ari-Pekka Puhto; Anna I Vasara; Keijo T Mäkelä; Laura L Elo Journal: JB JS Open Access Date: 2021-01-25
Authors: Mikko S Venäläinen; Eetu Heervä; Outi Hirvonen; Sohrab Saraei; Tomi Suomi; Toni Mikkola; Maarit Bärlund; Sirkku Jyrkkiö; Tarja Laitinen; Laura L Elo Journal: Cancer Med Date: 2021-12-03 Impact factor: 4.452