Precision medicine with prediction tools in high CV risk patients Symposium “New concepts and models in CV risk management” ESC, Munich August 28, 2018 Frank L.J. Visseren
Faculty Disclosure Declaration of financial interests For the last 3 years and the subsequent 12 months: I I have received a research grant(s)/ in kind support A From current sponsor(s) YES NO B From any institution YES NO II I have been a speaker or participant in accredited CME/CPD A From current sponsor(s) YES NO B From any institution YES NO III I have been a consultant/strategic advisor etc A For current sponsor(s) YES NO B For any institution YES NO IV I am a holder of (a) patent/shares/stock ownerships A Related to presentation YES NO B Not related to presentation YES NO
Faculty Disclosure Declaration of non-financial interests: • Professor of Medicine, epidemiologist • University Medical Center Utrecht • Member of Dutch guideline committees on cardiovascular prevention. Investigator in phase II/III clinical trials
Great challenge for a clinician: translating the results of large clinical trials to individual patients Antithrombotics? Lipid-lowering? Glucose-lowering? Blood pressure- lowering?
Much to consider, much to choose: what for who and when? CV prevention starts with healthy lifestyle. Riskfactor Drugs Dose / combi Treatment goal Lipids Statin Dose? LDL-c <2.5 mmol/l Ezetimibe Combination? LDL-c <1.8 mmol/l PCSK9-mab Even lower Blood pressure ACEi/ARB, Diuretics Dose? SBP <140 mmHg CCB, Betablocker, Combination? SBP <130 mmHg Spironolactone Elderly goal Antithrombotics Antiplatelet (COX, Dose? P2Y12, cAMP) Combination? DOAC Diabetes Metformin, SU,DPP-4 HbA1c <53, <58, <64 insulin, GLP-1, SGLT-2 mmol/mol Inflammation Triglycerides, Lp(a)
Why predicting risk? Identifying high risk patients with modifiable risk factors To improve prognosis Shared and informed decision-making
But ….. Predicting risk is difficult Which score to use? How to interpret and communicate risk? Time consuming in clinical practice?
Accessible risk prediction tools Tool Patient categories Geographical region Heart SCORE Healthy people Europe high and low risk regions QRISK3 Healthy people United Kingdom JBS-3 risk calculator Healthy people United Kingdom ASSIGN score Healthy people Scotland PROCAM score, Various websites Healthy people Germany CUORE Healthy people Italy ASCVD risk-estimator plus Healthy people United States Framingham risk score Healthy people United States Reynolds risk score Healthy people United States Globorisk Healthy people Worldwide UKPDS risk engine V2 Type 2 diabetes United Kingdom ADVANCE risk engine Type 2 diabetes Europe, Asia, Australasia and North America SMART risk score Vascular patients Europe and United States MAGGIC risk calculator Heart failure patients Worldwide Seattle Heart Failure model Heart failure patients Northern-America U-Prevent Healthy people Europe and Northern-America Type 2 diabetes patients Vascular patients Elderly
Distribution of 10-year risk for recurrent CV events in CVD patients www.escardio.org Piepoli ea, EHJ 2016;37, 2315 – 2381 Dorresteijn ea, Heart. 2013 Jun;99(12):866-72 Kaasenbrood ea, Circulation. 2016;134:1419 – 1429
Total CVD risk = unmodifiable + modifiable Risk Factors Age “ Residual cholesterol risk” Sex “Residual blood pressure risk” Family Hx “Residual smoking risk” eGFR “ Residual triglyceride risk” “Residual thrombotic risk” Adapted from: Ridker ea, Eur Heart J. 2016;37(22):1720-2
Challenges in CV risk prediction in apparently healthy people. Specific risk score for elderly problem OK problem Lifetime CVD risk score
CVD risk prediction in elderly (>70 yrs) Lipid-lowering 10-year CV risk (adjusted for competing risks) Not all elderly at high risk BP lowering Clin Res in Cardiol. 2017 Jan;106(1):58-68 De Vries et al, ESC congress 2018, abstract 114
Wouldn’t it be great to have lifetime predictions? Baseline risk Therapy effectiveness Treatment horizon Costs and Therapy harms Competing risks benefit Level of modifiable risk-factors
Lifetime predictions with Age as Time-Scale 10-year risk Lifetime risk Geskus, Biometrics 2011;67:39-49 Dorresteijn ea, BMJ. 2016 Mar 30;352:i1548
Lifetime prediction of CV events in vascular patients: SMART-REACH model C-statistic 0.67 (95% CI 0.66-0.68) C-statistic 0.68 (95% CI 0.67-0.70) Lifetime risk prediction for Kaasenbrood ea, JAHA 2018 epub CVD patients! Dorresteijn, ESC congress 2018, abstract 3141
CV risk prediction in apparently healthy people <70 years: LIFE-CVD model Lifetime CV risk score in primary prevention N Jaspers, ESC congress 2018, Young Investigators Award Finalist, abstract 1149
Externally validated scores for Lifetime CV risk and Lifetime therapy benefit Lifetime CV risk score in primary prevention • Apparently healthy people: LIFE-CVD model 1 - Derived and externally validated in cohorts: ARIC, MESA, EPIC, Heinz Nixdorf Recall (total n=69,523) • Lifetime risk prediction for CVD patients! Patients with CV disease: SMART-REACH model 2 - Derived and externally validated in cohorts: SMART, REACH (total n = 40,388) • Patients with DM2: DIAL model 3 - Derived and externally validated in cohorts: Swedish NDR, Scottish diab reg, ADVANCE, ACCORD, ASCOT, ALLHAT, SMART (total n = 587,151) CV risk prediction in elderly • Elderly patients: Elderly model 4,5 - Derived and externally validated in cohorts: PROSPER, SMART, ASCOT, HYVET (total n=11,090) 1 Jaspers, ESC congress 2018, abstract 1149 4 Clin Res in Cardiol. 2017 Jan;106(1):58-68 2 Kaasenbrood et al, JAHA 2018 5 DeVries et al, ESC congress 2018, abstract 114 3 Berkelmans et al, in revision
Individual lifetime treatment effects: gain in CVD-free life Lifetime risk Treatment effects CVD-free life Much to consider, much to choose: what for who Externally validated Lifetime CV risk scores and lifetime and when? therapy benefit CV prevention starts with healthy lifestyle. Lifetime CV risk score in primary prevention • Patients free of CV disease or DM2: LIFE-CVD model 1 Riskfactor Drugs Dose / combi Treatment goal - Derived and externally validated in cohorts: ARIC, MESA, EPIC, Heinz Nixdorf Recall (total n=69,523) Lipids Statin Dose? LDL-c <2.5 mmol/l Ezetimibe Combination? LDL-c <1.8 mmol/l = + • Patients with CV disease: SMART-REACH model 2 Lifetime risk prediction for CVD patients! PCSK9-mab Lower better? Blood pressure ACEi/ARB, Diuretics Dose? SBP <140 mmHg - Derived and externally validated in cohorts: SMART, REACH (total n = 40,388) CCB, Betablocker, Combination? SBP <130 mmHg • Spironolactone Elderly? Patients with DM2: DIAL model 3 Antithrombotics Antiplatelet (COX, Dose? - Derived and externally validated in cohorts: Swedish NDR, Scottish diab reg, ADVANCE, ACCORD, P2Y12) Combination? ASCOT, ALLHAT, SMART (total n = 587,151) DOAC CV risk prediction in elderly • Elderly patients: Elderly model 4,5 Diabetes SGLT-2 HbA1c <53, <58, <64 GLP-1 mmol/mol - Derived and externally validated in cohorts: PROSPER, SMART, ASCOT, HYVET (total n=11,090) New treatments Inflammation 1 Jaspers, ESC congress 2018, abstract 1149 2 Kaasenbrood et al, JAHA 2018 4 Clin Res in Cardiol. 2017 Jan;106(1):58-68 Triglycerides, Lp(a) 3 Berkelmans et al, in revision 5 DeVries et al, ESC congress 2018, abstract 114
Interactive calculator www.U-Prevent.com
Patient example A Systolic blood pressure 145 mmHg www.U-Prevent.com
Patient example A www.U-Prevent.com
Patient example A www.U-Prevent.com
Patient example A www.U-Prevent.com
Patient example A www.U-Prevent.com
Conclusion • Prediction tools facilitate Precision Medicine • Lifetime CVD risk prediction for: – Apparently healthy people – Patients with vascular disease – Patients with Diabetes Mellitus • Specific elderly 10-year CVD risk score • Estimating gain in CVD-free life from (combination of) lipid lowering, blood pressure lowering, antithrombotic treatment
Treating based on level of risk factors Treating based on risk Treating based on level of risk factors Treating based on individual treatment benefit Benefit-based Medicine!
www.U-Prevent.com
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