Agreement between Framingham, IraPEN and non-laboratory WHO-EMR risk score calculators for cardiovascular risk prediction in a large Iranian population

Atherosclerotic cardiovascular disease risk among Ghanaians: A comparison of the risk assessment tools

Objectives

Risk stratification is a cornerstone for preventing atherosclerotic cardiovascular disease (ASCVD). Ghana has yet to develop a locally derived and validated ASCVD risk model. A critical first step towards this goal is assessing how the commonly available risk models perform in the Ghanaian population. This study compares the agreement and correlation between four ASCVD risk assessment models commonly used in Ghana.

Methods

The Ghana Heart Study collected data from four regions in Ghana (Ashanti, Greater Accra, Northern, and Central regions) and excluded people with a self-declared history of ASCVD. The 10-year fatal/non-fatal ASCVD risk of participants aged 40-74 was calculated using mobile-based apps for Pooled Cohort Equation (PCE), laboratory-based WHO/ISH CVD risk, laboratory-based Framingham risk (FRS), and Globorisk, categorizing them as low, intermediate, or high risk. The risk categories were compared using the Kappa statistic and Spearman correlation.

Results

A total of 615 participants were included in this analysis (median age 55 [Inter quartile range 46, 64]) years with 365 (59.3 %) females. The WHO/ISH risk score categorized 504 (82.0 %), 58 (9.4 %), and 53 (8.6 %) as low-, intermediate-, and high-risk, respectively. The PCE categorized 345 (56.1 %), 181 (29.4 %), and 89 (14.5 %) as low-, intermediate- and high-risk, respectively. The Globorisk categorized 236 (38.4 %), 273 (44.4 %), and 106 (17.2 %) as low-, intermediate-, and high-risk, respectively. Significant differences in the risk categorization by region of residence and age group were noted. There was substantial agreement between the PCE vs FRS (Kappa = 0.8, 95 % CI 0.7 - 0.8), PCE vs Globorisk (Kappa = 0.6; 95 % CI 0.6 - 0.7), and FRS vs Globorisk (Kappa = 0.6; 95 % CI 0.6 - 0.7). However, there was only fair agreement between the WHO vs Globorisk (Kappa = 0.3; 95 % CI 0.3-0.4) and moderate agreement between the WHO vs PCE and WHO vs FRS.

Conclusion

There are significant differences in the ASCVD risk prediction tools in the Ghanaian population, posing a threat to primary prevention. Therefore, there is a need for locally derived and validated tools.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

A comparison of laboratory-based and office-based Framingham risk scores to predict 10-year risk of cardiovascular diseases: a population-based study

BACKGROUND

Two versions of Framingham's 10-year risk score are defined for cardiovascular diseases, namely laboratory-based and office-based models. The former is mainly employed in high-income countries, but unfortunately, it is not cost-effective or practical to utilize it in countries with poor facilities. Therefore, the present study aims to identify the agreement and correlation between laboratory-based and office-based Framingham models.

METHODS

Using laboratory-based and office-based Framingham models, this cross-sectional study used data from 8944 participants without a history of CVDs and stroke at baseline in the Fasa cohort study to predict the 10-year risk of CVDs. The laboratory-based model included age, sex, diabetes, smoking status, systolic blood pressure (SBP), treatment of hypertension, total cholesterol, and high-density lipoprotein (HDL); and the office-based model included age, sex, diabetes, smoking status, SBP, treatment of hypertension, and body mass index (BMI). The agreement between risk categories of laboratory-based and office-based Framingham models (low [< 10%], moderate [from 10 to < 20%], high [≥ 20%]) was assessed by kappa coefficients and percent agreement. Then, the correlation between the risk scores was estimated using correlation coefficients and illustrated using scatter plots. Finally, agreements, correlation coefficient, and scatter plots for laboratory-based and office-based Framingham models were analyzed by stratified Framingham risk score factors including sex, age, BMI categories, hypertension, smoking, and diabetes status.

RESULTS

The two models showed substantial agreement at 89.40% with a kappa coefficient of 0.75. The agreement was substantial in all men (kappa = 0.73) and women (kappa = 0.72), people aged < 60 years (kappa = 0.73) and aged ≥ 60 years (kappa = 0.69), smokers (kappa = 0.70) and non-smokers (kappa = 0.75), people with hypertension (kappa = 0.73) and without hypertension (kappa = 0.75), diabetics (kappa = 0.71) and non-diabetics (kappa = 0.75), people with normal BMI (kappa = 0.75) and people with overweight and obesity (kappa = 0.76). There was also a very strong positive correlation (r ≥ 0.92) between laboratory-based and office-based models in terms of age, sex, BMI, hypertension, smoking status and diabetes status.

CONCLUSIONS

The current study showed that there was a substantial agreement between the office-based and laboratory-based models, and there was a very strong positive correlation between the risk scores in the entire population as well across subgroups. Although differences were observed in some subgroups, these differences were small and not clinically relevant. Therefore, office-based models are suitable in low-middle-income countries (LMICs) with limited laboratory resources and facilities because they are more convenient and accessible. However, the validity of the office-based model must be assessed in longitudinal studies in LMICs.

Agreement between ten-years cardiovascular disease risk assessment tools: An application to Iranian population in Shahedieh Cohort Study

Background and aim

Cardiovascular risk-prediction models are efficient primary prevention tools to detect high-risk individuals. The study aims to use three tools to estimate the 10-year risk of developing cardiovascular disease (CVD) and investigate their agreement in an Iranian adult population.

Methods

The current cross-sectional study was carried out on 8569 adults between 35 and 70 who participated in the first phase of the Shahedieh cohort study in Yazd, Iran, and were free of CVDs (cardiac ischemia or myocardial infarction or stroke). World Health Organization/International Society of Hypertension (WHO/ISH) chart, Laboratory-Based (LB) and Non-Laboratory-Based (NLB) Framingham Risk Score (FRS) were used to predict the 10-year risk of developing CVD. The agreement across tools was determined by Kappa.

Results

WHO/ISH chart indicated the highest prevalence of low CVD risk for males (96.10%) and females (96.50%), while NLB Framingham had the highest prevalence of high CVD risk for males (19.40%) and females (5.30%). In total, there was substantial agreement between both FRS models (Kappa = o.70), while there was a slight agreement between WHO/ISH and both FRS tools. For under 60 years males and females, substantial agreements were observed between FRS methods (kappa = 0.73 and kappa = 0.68). For males and females over 60 years, this agreement was moderate and substantial, respectively (kappa = 0.54 and kappa = 0.64). WHO/ISH and LB Framingham model had substantial agreement for over 60 years females (kappa = 0.61).

Conclusions

Framingham models classified more participants in the high-risk category than WHO/ISH. Due to the lethality of CVDs, categorizing individuals based on FRS can ensure that most of the real high-risk people are detected. Remarkable agreement between FRS methods in all sex-age groups suggested using the NLB Framingham model as a primary screening tool, especially in a shortage of resources condition.

Impact of modifiable risk factors on prediction of 10-year cardiovascular disease utilizing framingham risk score in Southwest Iran

BACKGROUND

This cohort study was conducted to examine the association between modifiable risk factors, including hypertension, smoking, physical activity, diabetes, cholesterol, and high-density lipoprotein with Framingham risk score in the prediction of 10-year-risk of cardiovascular diseases (CVD) between men and women in an Arab community of Southwest Iran, Hoveyzeh.

MATERIALS AND METHODS

A total of 8,526 people aged 35-70 participated in this cohort study. Framingham was used to estimate the 10-year risk of CVD. Also, the linear regression models were used to assess the relationship between modifiable risk factors and the 10-year risk of CVD. Finally, the area under the receiver operating characteristic curve (AUC) was used to measure the ability of modifiable risk factors to predict the 10-year risk of CVD.

RESULTS

Our results of linear regression models showed that hypertension, smoking, PA, diabetes, cholesterol, and HDL were independently associated with the CVD risk in men and women. Also, AUC analysis showed that hypertension and diabetes have the largest AUC in men 0.841; 0.778 and in women 0.776; 0.715, respectively. However, physical activity had the highest AUC just in women 0.717.

CONCLUSION

Hypertension and diabetes in both gender and physical activity in women are the most important determinant for the prediction of CVD risk in Hoveyzeh. Our cohort study may be useful for adopting strategies to reduce CVD progression through lifestyle changes.

Illness perception and cardiovascular risk factors in patients with myocardial infarction undergoing percutaneous coronary intervention in Iran

Background

Knowing of perception of the illness, and cardiovascular risk factors in patients with myocardial infarction is crucial in engaging in effective secondary prevention. This study aimed to examine illness perception and cardiovascular risk factors in patients with myocardial infarction undergoing percutaneous coronary intervention.

Methods

The participants comprised 131 patients undergoing a first-time percutaneous coronary intervention at a metropolitan, tertiary referral hospital in Tabriz, Iran. The convenience sampling method was employed to select the research sample within a six-month period. The instruments used were as follows: (1) Demographic and health information form, (2) The Brief Illness Perception Questionnaire (3) The Health Risk Assessment framework developed by the Centers for Disease Control and Prevention. The design of the study was descriptive, cross sectional. The continuous variables were analyzed using Independent t-test and analysis of variance (ANOVA); and categorical variables were compared using the chi-square test.

Results

Most participants had a positive family history of cardiovascular disease (54.2%), with 66.4% of participants having at least one cardiovascular risk factor such as diabetes (36.6%) hypertension (32.8%) and dyslipidemia (16%). Most participants were physically inactive (78.6%), about 48.9% were overweight, 34.4% suffered from obesity and 26% were smokers. Illness perception in this study was seen to be high (6.21), with highest scores occurring in the illness control dimension (6.83) and lowest scores occurring in the understanding dimension (3.77). There was a significant relationship between illness perception and physical activity, nutrition, sleep and general health. Direct significant relationships between biometric values (cholesterol, glucose, blood pressure); psychological factors (depression, anxiety and stress) and illness perception were also found to exist.

Conclusions

Low scores in two dimensions of illness perception may lead to psychological consequences such as stress, anxiety, and depression. The relationship between illness perception and some risk factors of cardiovascular disease such as physical activity, diet and biometric values, reveal the need for more attention to patient education and counselling.

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

The 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.