1 Cardiovascular risk calculator for diabetic patients 이해영서울대학병원순환기내과
2 Contents Need of risk engine for diabetic patients Three main approaches of global cardiovascular risk estimation in diabetes Validation of cardiovascular risk scores for diabetic patients Impact of cardiovascular risk assessment on clinical practice and outcomes Summary and conclusion
3 Diabetes mellitus as CVD equivalent Survival (%) Nondiabetic subjects without prior MI Diabetic subjects without prior MI Nondiabetic subjects with prior MI Diabetic subjects with prior MI Year Haffner et al. N Engl J Med. 1998;339:
4 High prevalence of CAD among diabetic patients % of patients Type 2 diabetes Non-diabetes /3 of diabetic patients die of cardiovascular disease, including CHD, stroke, and peripheral vascular disease >65 Ages Harris MI, National Diabetes Data group
5 JAMA, Nov
6 > 20% of high risk diabetic patients have significant degree of coronary artery stenosis Study population Men: 50 YO with 3 yrs of DM or 40 YO with 5 yrs of DM Women: 55 YO with 3 yrs of DM or 45 YO with 5 yrs of DM Use of antidiabetic medication for 1 year No Hx of ASCVD Highest degree of CCTA stenosis Normal: 31.3% Mild: 46.1% Moderate (50-69%, CAC score > 100): 11.9% Severe (>70%): 10.7%arial
7 No significant difference in primary endpoint (Death/MI/Unstable Angina) HR = 0.80 ( )
8 Diabetes mellitus position statement In asymptomatic patients, routine (& general) screening for coronary artery disease is not recommended, because it does not improve outcomes as long as CVD risk factors are treated May be due to wide application of risk factor management, the overall CV event rate becomes lower than previous era
9 Diabetes mellitus position statement In asymptomatic patients, routine (& general) screening for coronary artery disease is not recommended, because it does not improve outcomes as long as CVD risk factors are treated May be due to wide application of risk factor management, the overall CV event rate becomes lower than previous era
10 Overall CV risk in diabetic patients lower than 20%... Detection of Ischemia in Asymptomatic Diabetics [DIAD] pts with DM (mean age 61%, ½ ), ½ got myocardial perfusion imaging, f/u 5yrs No diff: MI/CAD death: 2.7% screened vs 3% not No others significantly diff either DYNAMIT pts with DM + > 2 CV risk factors Treadmill test 21.5 % prevalence of silent ischemia f/u 3.5yrs No diff: MI/CAD death: 2.6% screened vs 2.4% not No others significantly diff either.
11 Suggested flow chart for CV prevention stratified for high risk diabetic patients
12 Contents Need of risk engine for diabetic patients Three main approaches of global cardiovascular risk estimation in diabetes Validation of cardiovascular risk scores for diabetic patients Impact of cardiovascular risk assessment on clinical practice and outcomes Summary and conclusion
13 Overview of notion of global risk assessment Coefficient of each risk factor indicates their relative contribution to overall CVD risk. Risk models developed using logistic or survival regressions Validation based on Derivation sample (internal validation) Independent populations (external validation) Discrimination ability to correctly classify individuals who go on to develop a CV event and those who remain event free Characterized by AUC or the C-statistic C-statistic ranges from 0.5 (uninformative test) to 1.0 (perfect discrimination) In general, a C-statistic > 0.7 considered acceptable Calibration: concordance between predicted risk and observed risk assessed by comparing risk estimates from the model with actual event rates in the test population Recalibration of the risk model by adjusting the baseline risk estimates to fit the target population Reclassification: change in risk categories subsequent to adding a newly described risk factor or marker to an existing model
14 3 approaches of global CV risk estimation in diabetes CVD risk equivalent (20% risk) approach Unifying risk models regardless DM status Separate models for diabetic patients
15 CVD risk equivalent (20% risk) approach Classifying all individuals with diabetes as having a 10-year absolute CVD risk of at least 20%. CVD risk is not uniformly distributed among people with diabetes. Various studies suggests that multivariable risk prediction to be significantly better than classification of diabetes as a cardiovascular risk equivalent Using ATPIII models, 71.9% of the diabetic WHS participants had a predicted 10-year risk < 20%.
16 Unifying risk models regardless DM status Global CVD risk assessment for people with or without diabetes. The rationale is that there is not interaction between the diabetes status and other cardiovascular risk factors. everything else being equal a subject with diabetes will not always have a higher risk than non-diabetic subject with the same level of other risk factors (e.g., blood pressure) Framingham cardiovascular risk equations, Pooled cohort equations
17 Characteristics of cardiovascular risk scores
18 Separate models for diabetic patients Risk factors affect cardiovascular disease risk in different ways in people with and without diabetes. Assumed that duration of diagnosed diabetes will contribute more to risk estimates than age increment. Therefore to allow a more rational use of predictive information from age in people with diabetes, it has to be split into two components (i.e. age at diabetes diagnosis, known duration of diabetes). Classical CV risk factors including smoking, BP, lipid might affect CVD risk similarly in people with and without diabetes, with no evidence of interaction. Risk factors having diabetes-specific characteristics Prescriptions of cardiovascular risk reducing therapies Hemoglobin A1c (HbA1c) Urinary albumin excretion Marker of microvascular complications (especially retinopathy) Constructing different model for diabetic patients allows efficient use of predictive information captured by diabetes-specific factors UKPDS risk engines, etc
19 Schema for estimating the 4-year risk of CVD by the ADVANCE model equation
20 Cardiovascular risk models for T2DM Cardiovascular disease prediction models Action in Diabetes and Vascular disease: 5yr PreterAx and diamicron-mr Controlled Evaluation (ADVANCE) risk engine: 4yr Fremantle risk score: 5yr New Zealand Diabetes Cohort Study (DCS) risk score: 5yr Swedish National Diabetes Registry risk score: 5yr Cardiovascular Health Study (CHS) risk score:10yr Coronary heart disease prediction models DCS risk score: 5yr Diabetes Audit and Research in Tayside Scotland (DARTS) risk score: 5yr Hong Kong Diabetes Register risk score: 5yr Atherosclerosis Risk in Communities (ARIC) risk score: 10yr UKPDS risk engine: 5yr
21 Contents Need of risk engine for diabetic patients Three main approaches of global cardiovascular risk estimation in diabetes Validation of cardiovascular risk scores for diabetic patients Impact of cardiovascular risk assessment on clinical practice and outcomes Summary and conclusion
22 Framingham equation under-estimate CHD risk in diabetes up to 50% Predicted Observed UKPDS CHD event (%/yr) (Diabetes) WOSCOPS CHD event (%/yr) (Non-diabetes) (Yeo et al Diabet Med 2001; 18: ) Cardiff CHD event Male (Diabetes) (% / 4 yrs) Female Stevens et al Diabet Med 2005; 22: 228
23 Why under-estimation? Model based on largely non-dm population (Framingham calculator) Traditional risk factors do not account for excess CHD death in diabetes. Other important factors not included in risk calculation (ie small dense LDL, microalbuminuria, hypercoagulable state, impaired fibrinolysis, endothelial dysfunction, inflammatory states, insulin resistance etc)
24 UKPDS risk engine is not a better alternative Comparison between UKPDS risk engine and Framingham equation SH Song et al Diabetic Med 2004; 21: Mean CHD risk (over 10 yrs) in type 2 diabetes UKPDS risk engine better 15% threshold Framingham calculator better male female JBS UKPDS Conclusion: Overall, UKPDS risk engine estimated higher CHD risk score. At high risk (>30%), UKPDS risk engine consistently estimated higher risk score than Framingham equation. At lower risk levels (~15%) where clinical decision to start statin occurs (as per NICE), UKPDS risk engine and Framingham equation equivalent.
25 UKPDS risk engine overestimates CHD/CVD risk External validation using 1861 T2DM patients from EPIC-NL, EPIC-Potsdam cohort By UKPDS risk engine the mean predicted 8 year risk was 15.9% while the observed 8 year CHD risk was 4.9%, resulting in an overestimation of 224%. For 8 year CVD risk, the UKPDS risk engine overestimated the CVD risk by 112%, as the observed 8 year CVD risk was 7.5%. Discriminative ability is moderate (c-statistics ) and the calibration poor, with a severely overestimated CHD risk prediction.
26 UKPDS risk engine overestimates CHD/CVD risk Calibration plots for 5 year calculated risk for (a) CHD and (b) CVD. Values depict observed and predicted values with 95% CI. The dotted 45 line denotes ideal agreement between predicted and observed risk.
27 van der Leeuw J, et al. Heart 2015;101:
28 Moderate discrimination power Discrimination was moderate for all 10 prediction models, with c-statistics ranging from 0.54 (95% CI 0.46 to 0.63) to 0.76 (95% CI 0.67 to 0.84). Discrimination in external validation using EPIC-NL, EPIC- Potsdam and Secondary Manifestations of ARTerial disease (SMART).
29 Overestimation of risk Expected to observed ratio of the prediction models in EPIC-NL, EPIC-Potsdam and SMART
30 Overestimation mainly occurs in high-risk patients
31 Why overestimate? UKPDS risk engine was developed from a cohort that started including patients in Treatment of type 2 diabetes and prevention of CVD has improved since 1977 and the risk of developing CVD has declined with better treatment of type 2 diabetes As diabetes is now detected at an earlier stage, therapeutic intervention can be initiated earlier, reducing CVD risk even further. Modifiable risk factors have changed over time: smoking is less common and there are better treatments for hypertension and to lower HbA1c concentrations. The slight overestimation of risk in high risk is probably less relevant in clinical practice as these patients would meet the treatment threshold anyway, even if actual risk is somewhat lower.
32 Contents Need of risk engine for diabetic patients Three main approaches of global cardiovascular risk estimation in diabetes Validation of cardiovascular risk scores for diabetic patients Impact of cardiovascular risk assessment on clinical practice and outcomes Summary and conclusion
33 Impact studies of the CVD prediction models 3 studies used a randomised controlled trial design and examined the impact of the Framingham prediction model on treatment and prevention of CVD. Half of them allocated to an intervention group for which CV prediction model was noted on the patient s records, while in control group it was not. No difference observed in prescription of glucose control, blood pressure lowering and lipid-modifying treatments. However, restricting analysis to the high-risk group, patients in the intervention group were more likely to receive lipid-modifying or blood pressure-lowering prescriptions. Hall LE, et al. British Medical Journal. 2003;326:251-2
34 Individualized CAD screening in asymptomatic diabetic patients based on risk score model Classical risk factors + DM characteristic factors (duration of diabetes, ratio of total cholesterol to HDL, neuropathy) Medicine. 2015;94:e508
35 Incorporation of CVD prediction models in clinical guidelines IDF21 and NICE guidelines: UKPDS risk engine Canadian Diabetes Association: UKPDS risk engine, PROCAM, Strong Heart prediction model EASD: Framingham, DECODE Australian National Vascular Disease Prevention Alliance: Framingham prediction model, UKPDS risk engine JBS developed risk charts based on the Framingham prediction model
36 Summary and conclusion Current diabetes guidelines do not recommend routine screening for coronary artery disease in asymptomatic patients. But it is suggested that those at highest risk (10- yr risk 20%) for cardiac events may benefit from cardiovascular screening. The risk calculation can assist clinicians to identify T2DM patients with low or high risk of CVD. There is tendency of overestimation in high risk patients, however it is less relevant in clinical practice as these patients would meet the treatment threshold anyway, even if actual risk is somewhat lower. To enhance prediction of CVD and CHD in patients with T2DM, there is a need to update or construct a new and improved diabetes-specific model with better performance and better external validity.
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