Benjamin Djulbegovic1,2,3, Athanasios Tsalatsanis1, Iztok Hozo4. 1. USF Health Program for Comparative Effectiveness Research, Division for Evidence-Based Medicine, Department of Internal Medicine, University of South Florida, Tampa, FL, USA. 2. Departments of Hematology and Health Outcome Behavior, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. 3. Tampa General Hospital, Tampa, FL, USA. 4. Department of Mathematics, Indiana University, Gary, IN, USA.
Abstract
RATIONALE, AIMS AND OBJECTIVES: The American College of Cardiology and American Heart Association (ACC/AHA) statin guidelines recommend that people with risk of cardio-vascular disease (CVD) ≥7.5% over 10 years should be treated with statins. This recommendation ignores individual patient CVD risks and preferences. We compared the ACC/AHA guidelines to the following management strategies a) individualized statins treatment based on Framingham Risk Score (FRS), b) treat none, c) treat all. METHODS: We employed regret-based decision curve analysis to evaluate the optimal treatment strategy. We used data on 5013 participants from the second generation of the Framingham Heart Study. We assessed regret of each treatment strategy [treat according to FRS vs. treat none vs. treat all] as a function of emotionally felt loss of treatment benefits and incurred treatment harms. We calculated the difference between regret associated with one strategy compared with the other and expressed it as Net Expected Regret Difference (NERD). Two strategies are identical if NERD = 0. RESULTS: Treatment according to ACC/AHA guidelines represents the optimal strategy only if the patient values avoiding heart disease 12 times more than harms related to statins. For values of benefit/harms (B/H) <12, treatment according to FRS represents the optimal strategy. For B/H <3, 'treat none' represents equally acceptable strategy. Adopting a threshold of 10% recommended by other professional organizations would decrease over-treatment by more than 60% without significantly affecting under-treatment. CONCLUSION: Under most realistic scenarios, individualizing statins treatment, or not recommending statins at all, represents the optimal strategy for primary prevention of heart disease.
RATIONALE, AIMS AND OBJECTIVES: The American College of Cardiology and American Heart Association (ACC/AHA) statin guidelines recommend that people with risk of cardio-vascular disease (CVD) ≥7.5% over 10 years should be treated with statins. This recommendation ignores individual patient CVD risks and preferences. We compared the ACC/AHA guidelines to the following management strategies a) individualized statins treatment based on Framingham Risk Score (FRS), b) treat none, c) treat all. METHODS: We employed regret-based decision curve analysis to evaluate the optimal treatment strategy. We used data on 5013 participants from the second generation of the Framingham Heart Study. We assessed regret of each treatment strategy [treat according to FRS vs. treat none vs. treat all] as a function of emotionally felt loss of treatment benefits and incurred treatment harms. We calculated the difference between regret associated with one strategy compared with the other and expressed it as Net Expected Regret Difference (NERD). Two strategies are identical if NERD = 0. RESULTS: Treatment according to ACC/AHA guidelines represents the optimal strategy only if the patient values avoiding heart disease 12 times more than harms related to statins. For values of benefit/harms (B/H) <12, treatment according to FRS represents the optimal strategy. For B/H <3, 'treat none' represents equally acceptable strategy. Adopting a threshold of 10% recommended by other professional organizations would decrease over-treatment by more than 60% without significantly affecting under-treatment. CONCLUSION: Under most realistic scenarios, individualizing statins treatment, or not recommending statins at all, represents the optimal strategy for primary prevention of heart disease.
Authors: Benjamin Djulbegovic; Iztok Hozo; Thomas Mayrhofer; Jef van den Ende; Gordon Guyatt Journal: J Eval Clin Pract Date: 2018-12-21 Impact factor: 2.431