Relationship between cardiovascular disease risk and neck circumference shown in the score risk model

Introduction: The most important way to reduce CVD-related mortality is to apply appropriate treatment according to the risk status of the patients. For this purpose, the SCORE risk model is used in Europe. In addition to these risk models, some anthropometric measurements are known to be associated with CVD risk and risk factors. Objectives: This study aimed to investigate the association of these anthropometric measurements, especially neck circumference (NC), with the SCORE risk chart. Methods: This was planned as a cross-sectional study. The study population were classified according to their SCORE risk values. The relationship of NC and other anthropometric measurements with the total cardiovascular risk indicated by the SCORE risk was investigated. Results: A total of 232 patients were included in the study. The patients participating in the study were analysed in four groups according to the SCORE ten-year total cardiovascular mortality risk. As a result, the NC was statistically significantly lower among the SCORE low and moderate risk group than all other SCORE risk groups (low-high and very high 36(3)–38(4) (IQR) p: 0.026, 36(3)–39(4) (IQR) p < 0.001, 36(3)–40(4) (IQR) p < 0.001), (moderate-high and very high 38(4) vs. 39(4) (IQR) p: 0.02, 38(4) vs. 40(4) (IQR) p < 0.001, 39(4) vs. 40(4) (IQR) p > 0.05). NC was found to have the strongest correlation with SCORE than the other anthropometric measurements. Conclusions: Neck circumference correlates strongly with the SCORE risk model which shows the ten-year cardiovascular mortality risk and can be used in clinical practice to predict CVD risk.

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The Uncertainty of Cardiovascular Disease Risk Calculation—What is the Best Risk Model for the Individual?

Journal of Engineering and Science in Medical Diagnostics and Therapy ◽

10.1115/1.4039103 ◽

2018 ◽

Vol 1 (2) ◽

Author(s):

Ronald S. LaFleur ◽

Laura S. Goshko

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Clinical Decision Making ◽

Risk Model ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Patient Specific ◽

Model Parameters ◽

Risk Models ◽

The Individual

Cardiovascular disease (CVD) continues to be a leading cause of death. Accordingly, risk models attempt to predict an individual's probability of developing the disease. Risk models are incorporated into calculators to determine the risk for a number of clinical conditions, including the ten-year risk of developing CVD. There is significant variability in the published models in terms of how the clinical measurements are converted to risk factors as well as the specific population used to determine b-weights of these risk factors. Adding to model variability is the fact that numbers are an imperfect representation of a person's health status. Acknowledgment of uncertainty must be addressed for reliable clinical decision-making. This paper analyzes 35 published risk calculators and then generalizes them into one “Super Risk formula” to form a common basis for uncertainty calculations to determine the best risk model to use for an individual. Special error arithmetic, the duals method, is used to faithfully propagate error from model parameters, population averages and patient-specific clinical measures to one risk number and its relative uncertainty. A set of sample patients show that the “best model” is specific to the individual and no one model is appropriate for every patient.

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The ADVANCE cardiovascular risk model and current strategies for cardiovascular disease risk evaluation in people with diabetes

CardioVascular Journal of Africa ◽

10.5830/cvja-2013-078 ◽

2013 ◽

Vol 24 (9) ◽

pp. 376-381 ◽

Cited By ~ 9

Author(s):

Andre Pascal Kengne

Keyword(s):

Cardiovascular Disease ◽

Cardiovascular Risk ◽

Risk Evaluation ◽

Risk Model ◽

Disease Risk ◽

Cardiovascular Disease Risk

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Cardiovascular disease: Risk factors and applicability of a risk model in a Greek cohort of renal transplant recipients

World Journal of Transplantation ◽

10.5500/wjt.v7.i1.49 ◽

2017 ◽

Vol 7 (1) ◽

pp. 49

Author(s):

Nikolaos-Andreas Anastasopoulos ◽

Evangelia Dounousi ◽

Evangelos Papachristou ◽

Charalampos Pappas ◽

Eleni Leontaridou ◽

...

Keyword(s):

Risk Factors ◽

Cardiovascular Disease ◽

Renal Transplant ◽

Risk Model ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Cardiovascular Disease Risk Factors ◽

Renal Transplant Recipients ◽

Transplant Recipients

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Agreement between laboratory-based and non-laboratory-based Framingham risk score in Southern Iran

Scientific Reports ◽

10.1038/s41598-021-90188-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fatemeh Rezaei ◽

Mozhgan Seif ◽

Abdullah Gandomkar ◽

Mohammad Reza Fattahi ◽

Jafar Hasanzadeh

Keyword(s):

Cardiovascular Disease ◽

Risk Model ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Large Population ◽

Risk Groups ◽

Limits Of Agreement ◽

Current Smoking ◽

Treatment Status ◽

Southern Iran

AbstractThe Framingham 10-year cardiovascular disease risk is measured by laboratory-based and non-laboratory-based models. This study aimed to determine the agreement between these two models in a large population in Southern Iran. In this study, the baseline data of 8138 individuals participated in the Pars cohort study were used. The participants had no history of cardiovascular disease or stroke. For the laboratory-based risk model, scores were determined based on age, sex, current smoking, diabetes, systolic blood pressure (SBP) and treatment status, total cholesterol, and High-Density Lipoprotein. For the non-laboratory-based risk model, scores were determined based on age, sex, current smoking, diabetes, SBP and treatment status, and Body Mass Index. The agreement between these two models was determined by Bland Altman plots for agreement between the scores and kappa statistic for agreement across the risk groups. Bland Altman plots showed that the limits of agreement were reasonable for females < 60 years old (95% CI: −2.27–4.61%), but of concern for those ≥ 60 years old (95% CI: −3.45–9.67%), males < 60 years old (95% CI: −2.05–8.91%), and males ≥ 60 years old (95% CI: −3.01–15.23%). The limits of agreement were wider for males ≥ 60 years old in comparison to other age groups. According to the risk groups, the agreement was better in females than in males, which was moderate for females < 60 years old (kappa = 0.57) and those ≥ 60 years old (kappa = 0.51). The agreement was fair for the males < 60 years old (kappa = 0.39) and slight for those ≥ 60 years old (Kappa = 0.14). The results showed that in overall participants, the agreement between the two risk scores was moderate according to risk grouping. Therefore, our results suggest that the non-laboratory-based risk model can be used in resource-limited settings where individuals cannot afford laboratory tests and extensive laboratories are not available.

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The Association between Insulin Resistance and Cardiovascular Disease Risk: A Community-Based Cross-Sectional Study among Taiwanese People Aged over 50 Years

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17197195 ◽

2020 ◽

Vol 17 (19) ◽

pp. 7195

Author(s):

Mei-Chun Lu ◽

Wei-Ching Fang ◽

Wen-Cheng Li ◽

Wei-Chung Yeh ◽

Ying-Hua Shieh ◽

...

Keyword(s):

Insulin Resistance ◽

Cardiovascular Disease ◽

Risk Model ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Model Assessment ◽

Cvd Risk ◽

Community Based ◽

Cross Sectional ◽

Study Results

Background and Aims: Previous studies have implied that insulin resistance (IR) could represent a major underlying abnormality leading to cardiovascular disease (CVD). The aim of this study was to evaluate the relationships between IR (estimated by the homeostasis model assessment of IR (HOMA-IR) index) and CVD risk among middle-aged and elderly Taiwanese individuals. Methods: In this cross-sectional, community-based study, a total of 320 participants were interviewed to collect demographical parameters and blood samples. The recruited participants were divided into tertiles according to their levels of HOMA-IR. The Framingham risk score (FRS) was calculated according to the 2008 general CVD risk model from the Framingham Heart Study. Results: The HOMA-IR index was significantly correlated with the FRS, with a Pearson’s coefficient of 0.22. In the multiple logistic regression model, a higher HOMA-IR level was significantly associated with a high FRS (FRS ≥ 20%) (highest tertile vs. lowest tertile of HOMA-IR, crude OR = 3.69; 95% CI = 1.79–7.62), even after adjusting for smoking, fasting plasma glucose (FPG), and systolic blood pressure (SBP) (highest tertile vs. lowest tertile of HOMA-IR, adjusted OR = 11.51; 95% CI = 2.55–51.94). The area under the receiver operating characteristic curve for the HOMA-IR index as the predictor of high FRS was 0.627, and the optimal HOMA-IR cutoff value was 1.215 (sensitivity = 83.6%, specificity = 42.9%). Conclusions: We considered that HOMA-IR is an independent factor but that it cannot be used solely for evaluating the CVD risk due to the low AUC value. Further prospective cohort studies are warranted to better assess the relationship between CVD risk and insulin resistance.

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