Physical activity recommendation by cardiologists

Funding Acknowledgements

Type of funding sources: None.

OnBehalf

Exercise Cardiology Council

Background. Large number of studies confirm benefits of physical activity (PA) to improve health. Recommendation guidelines lack tools for health professionals training in PA prescription.

Purpose. To show cardiologists knowledge degree and attitude regarding existing recommendations on PA.

Methods. Observational and cross-sectional study. n = 299. A virtual survey describes cardiologist profile, knowledge degree, planning and satisfaction while making PA recommendation.

Results. Cardiologists profile in Table 1. Mean age 47.27 (SD 11.92). Sedentary lifestyle is considered a cardiovascular risk factor (CVRF) by 93.24%. 70% report performing PA complying with the recommendations of world health organization. 98.99% recommend PA while 80.74% also prescribe it. Prescribing PA, cardiologists self-rating is adequate-very adequate in 83%. Instruments used are guidelines (57.33%), self-perception (32%), expert opinion (25.33%). Most suggested mode of exercise is walking or jogging. Only 25.67% combine resistance and strength exercises. Only 13% choose to combine four parameters (heart rate, time, mode, intensity). Only 31.85% have undergone postgraduate training or education on PA prescription. 

Conclusions. Approximately 25% of cardiologists use valid or formal indicators to prescribe PA. Cardiologist with CVRF perform less PA than the rest. It is observed that PA prescription is influenced by PA degree performed by the physician. Lack of training seems to be the biggest obstacle to PA prescription generalization.

n % Cardiologists 299 100 MaleFemale 195104 65,2234,78 Sector Private 172 58,11 Public 26 8,78 Both 98 33,11 Cardiovascular risk factors Hypertension 61 20,33 Dyslipidemia 59 19,67 Diabetes 15 5,00 Smoking 23 7,67 Overweight 84 28 Coronary heart disease 11 3,67

P1552Different forms of evaluation of atherosclerotic load and its different predicted powers compared with framingham risk score

Introduction

The screening of carotid and/or ileo-femoral subclinical atherosclerosis using vascular ultrasound could be a cost-effective tool in the reclassification of cardiovascular risk in primary cardiovascular prevention. But until today we don't have evidence indicating which atherosclerotic load indicator variable (such as plaque area, number of atherosclerotic plaques or affected territories) has the most predictive power for future cardiovascular events.

Objectives

a. Evaluate the prevalence of carotid and ileo-femoral subclinical atherosclerosis (SubAth). b. Evaluate the predictive power of each atherosclerotic load indicator variable for cardiovascular events (CVE). c. to compare those methods with Framingham Score for prediction of CVE.

Material and methods

We retrospectively evaluated patients from our registry called CARFARE (CARDIOMETABOLIC RISK FACTORS REGISTRY) conducted in the context of a cardiovascular primary prevention program (n=6108). In this analysis we included only those patients who completed a follow up ≥24 months from the first vascular evaluation. Exclusion criteria: <40 or >65 years old, BMI>40 kg/m2, previous cardiovascular and/or cerebrovascular event, chronic stable angina. We used Framingham score for the cardiovascular risk assessment. We evaluated the following atherosclerotic load variables using high resolution vascular ultrasonography (Mannheim Consensus): carotid plaques area (AR-C), ileo-femoral plaques area (AR-IF), and total (carotid+ileo-femoral, AR-TOT), number of affected territories (N-TER). Primary composite endpoints (P-CVE): myocardial infarction, stroke, hospitalization due to angina or acute heart failure. For prediction power analysis we used ROC curves and logistic regressions (unadjusted and adjusted by age, sex, BMI, major cardiovascular risk factors and treatment).

Results

We included 1431 patients (55.0±9.78 y.o., 38% women, follow-up period of 789±23 days). The total P-CVE rate was 3.77% during this period (54 events). The overall prevalence of atherosclerosis in this population was 60.7%. The AUC of the ROC curves for P-CVE prediction was 0.648, for Framingham 0.706, for AR-C 0.726, for AR-IF 0.746, for AR-TOT and 0.79 for N-TER (graph). The AUC of AR-TOT and N-TER were significantly higher than the AUC of the Framingham score (p=0.017 and p=0.0004, respectively) for prediction of P-CVE. The dichotomization criteria according to ROC were: AR-C>5.80 mm2, AR-IF>23.0 mm2, AR-TOT>43.9 mm2, and N-TER>1. In the logistic regressions adjusted for prediction of P-CVE the OR were: Framingham 1.05 (95% CI: 1.02–1.07), AR-C 4.00 (95% CI: 1.89–8.47), AR-IF 4.01 (95% CI: 2.02–8.32), AR-TOT 4.35 (95% CI: 2.13- 8.91) and N-TER 6.95 (95% CI: 3.05–15.8).

Comparison of ROC curves for CVE

Conclusions

The carotid/ileo-femoral SubAth scan was a more potent predictor of cardiovascular events than the Framingham score, particularly those variables that indicate extension of multiterritorial affection like AR-TOT or N-TER.