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Abstract Introduction Cardiac rehabilitation (CR) plays a key role in secondary prevention of patients (pts) with cardiovascular (CV) disease. The control of CV risk factors is a cornerstone of a comprehensive CR program. Purpose To analyze the impact of CR on CV risk factor optimization. Methods Single-center prospective cohort study including consecutive pts enrolled in a phase 2 CR program, from 2015 to 2023. Body weight, body mass index (BMI), waist circumference (WC), blood pressure (BP), smoking habits, and cholesterol and hemoglobin A1c (HbA1c) levels were evaluated before and after completion of the program. Results were assessed through paired samples t-test or Wilcoxon test. Results A total of 446 pts were included (80% male), with a mean age of 60.5±11.5years. 72.1% of pts had arterial hypertension, 72.2% had dyslipidemia, 26.6% were diabetic and 62.9% were active or past smokers. Mean body weight, BMI and WC were 80.2±14.5 kg, 27.6±4.3 kg/m2 and 102.0±11.2 cm, respectively. There was a statistically significant reduction in systolic BP (-3.2± 17.5mmHg, p0.001), diastolic BP (-2.6±13.0mmHg, p0.001) and WC (-1.2±4.6cm, p0.001), as well as a significant percentage of pts who quit smoking (53%). Regarding cholesterol levels, there was an improvement in total cholesterol (median level 151 vs. 139, Z=-5.450, p0.001), high-density lipoprotein (median level 42 vs. 46, Z=-4.870, p0.001), low-density lipoprotein (LDL) (median level 81 vs. 66, Z=-6.316, p0.001), and triglycerides (median level 115 vs. 101, Z=-2.878, p=0.004). In the subgroup of ischemic pts, only 17.8% were initially below the LDL recommended target (55mg/dL). After CR, a higher percentage of pts achieved adequate lipid control (33%). Differences regarding HbA1c levels, weight and BMI were only significant in subgroup analysis of diabetic and obese pts (Z=-2.128, p=0.033; -1.0±4.4kg, p=0.027; Z=-2.155, p=0.031, respectively). Conclusion Results from this cohort of patients highlight the increasing importance of patient referral to a structured CR program, in order to optimize control of CV risk factors.
Cravo et al. (Sat,) studied this question.