Improvement of evaluation in Chinese patients with atherosclerotic cardiovascular disease using the very-high-risk refinement: a population-based study

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Medical University of Bialystok, Poland Background Cardiovascular disease (CVD) is a major, worldwide problem and remain the dominant cause of premature mortality in the word. Simultaneously the metabolic syndrome is a growing problem. The aim of this study was to investigate the cardiometabolic profile among cardiovascular risk classes, and to estimate CV risk using various calculators. Methods The longitudinal, population-based study, was conducted in 2017-2020. A total of 931 individuals aged 20-79 were included. Anthropometric and biochemical profiles were measured according to a standardized protocols. The study population was divided into CV risk classes according to the latest recommendation. Comparisons variables between subgroups were conducted using Dwass-Steele-Critchlow-Fligner test. To estimate CV risk were used: the Systematic Coronary Risk Estimation system, Framingham Risk Score and LIFEtime-perspective model for individualizing CardioVascular Disease prevention strategies in apparently healthy people (LIFE-CVD). Results The mean age was 49.1± 15.5 years, 43.2% were male. Percentages of low-risk, moderate-risk, high-risk and very-high CV risk were 46.1%, 22.8%, 13.5%, 17.6%, respectively. Most of the analyzed anthropometric, body composition and laboratory parameters did not differ between the moderate and high CV risk participants, whereas the low risk group differed significantly. In the moderate and high-risk groups, abdominal distribution of adipose tissue dominated with significantly elevated parameters of insulin resistance. Interestingly, estimating lifetime risk of myocardial infarction, stroke or CV death using LIFE-CVD calculator yielded similar results in moderate and high CV risk classes. Conclusion The participants belonging to moderate and high CV risk classes have a very similar unfavorable cardiometabolic profile which may result in the similar lifetime CV risk. This may imply the need for more aggressive pharmacological and non-pharmacological management of CV risk factors in the moderate CV risk population. It would be advisable to consider combining the moderate and high risk classes into one high CV risk class, or it may be worth adding one of the parameters of abdominal fat distribution to the CV risk calculators as an expression of increased insulin resistance. Abstract Figure 1.

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Patient characteristics and acute cardiovascular event rates among patients with very high‐risk and non‐very high‐risk atherosclerotic cardiovascular disease

Clinical Cardiology ◽

10.1002/clc.23706 ◽

2021 ◽

Author(s):

Gregg C. Fonarow ◽

Mikhail N. Kosiborod ◽

Pallavi B. Rane ◽

Sasikiran Nunna ◽

Guillermo Villa ◽

...

Keyword(s):

Cardiovascular Disease ◽

High Risk ◽

Cardiovascular Event ◽

Patient Characteristics ◽

Atherosclerotic Cardiovascular Disease ◽

Acute Cardiovascular Event ◽

Event Rates ◽

Very High

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Abstract P019: A Single Model For Predicting Major Adverse Cardiovascular Events In Individuals With And Without History Of Atherosclerotic Cardiovascular Disease: The Atherosclerosis Risk In Communities (aric) Study

Circulation ◽

10.1161/circ.143.suppl_1.p019 ◽

2021 ◽

Vol 143 (Suppl_1) ◽

Author(s):

Yejin Mok ◽

Lena Mathews ◽

Ron C Hoogeveen ◽

Michael J Blaha ◽

Christie M Ballantyne ◽

...

Keyword(s):

Myocardial Infarction ◽

Cardiovascular Disease ◽

High Risk ◽

Risk Stratification ◽

Cardiovascular Events ◽

Major Adverse Cardiovascular Events ◽

Atherosclerotic Cardiovascular Disease ◽

Single Model ◽

History Of ◽

Very High

Background: In the 2018 AHA/ACC Cholesterol guideline, risk stratification is an essential element. The use of a Pooled Cohort Equation (PCE) is recommended for individuals without atherosclerotic cardiovascular disease (ASCVD), and the new dichotomous classification of very high-risk vs. high-risk has been introduced for patients with ASCVD. These distinct risk stratification systems mainly rely on traditional risk factors, raising the possibility that a single model can predict major adverse cardiovascular events (MACEs) in persons with and without ASCVD. Methods: We studied 11,335 ARIC participants with (n=885) and without (n=10,450) a history of ASCVD (myocardial infarction, ischemic stroke, and symptomatic peripheral artery disease) at baseline (1996-98). We modeled factors in the PCE and the new classification for ASCVD patients (Figure legend) in a single CVD prediction model. We examined their associations with MACEs (myocardial infarction, stroke, and heart failure) using Cox models and evaluated the discrimination and calibration for a single model including those factors. Results: During a median follow-up of 18.4 years, there were 3,658 MACEs (3,105 in participants without ASCVD). In general, the factors in the PCE and the risk classification system for ASCVD patients were associated similarly with MACEs regardless of baseline ASCVD status, although age and systolic blood pressure showed significant interactions. A single model with these predictors and the relevant interaction terms showed good calibration and discrimination for those with and without ASCVD (c-statistic=0.729 and 0.704, respectively) (Figure). Conclusion: A single CVD prediction model performed well in persons with and without ASCVD. This approach will provide a specific predicted risk to ASCVD patients (instead of dichotomy of very high vs. high risk) and eliminate a practice gap between primary vs. secondary prevention due to different risk prediction tools.

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