Agreement between the laboratory-based and non-laboratory-based WHO cardiovascular risk charts: a cross-sectional analysis of a national health survey in Peru

Discrimination and calibration performances of non-laboratory-based and laboratory-based cardiovascular risk predictions: a systematic review

BACKGROUND AND OBJECTIVE

This review compares non-laboratory-based and laboratory-based cardiovascular disease (CVD) risk prediction equations in populations targeted for primary prevention.

DESIGN

Systematic review.

METHODS

We searched five databases until 12 March 2024 and used prediction study risk of bias assessment tool to assess bias. Data on hazard ratios (HRs), discrimination (paired c-statistics) and calibration were extracted. Differences in c-statistics and HRs were analysed.

PROTOCOL

PROSPERO (CRD42021291936).

RESULTS

Nine studies (1 238 562 participants, 46 cohorts) identified six unique CVD risk equations. Laboratory predictors (eg, cholesterol and diabetes) had strong HRs, while body mass index in non-laboratory models showed limited effect. Median c-statistics were 0.74 for both models (IQR: lab 0.77-0.72; non-lab 0.76-0.70), with a median absolute difference of 0.01. Calibration measures between laboratory-based and non-laboratory-based equations were similar, although non-calibrated equations often overestimated risk.

CONCLUSION

The discrimination and calibration measures between laboratory-based and non-laboratory-based models show minimal differences, demonstrating the insensitivity of c-statistics and calibration metrics to the inclusion of additional predictors. However, in most reviewed studies, the HRs for these additional predictors were substantial, significantly altering predicted risk, particularly for individuals with higher or lower levels of these predictors compared with the average.

Association of cardiovascular disease risk with liver steatosis and fibrosis in people with HIV in low- and middle-income countries

OBJECTIVE

The aim of this study was to understand the relationship between cardiovascular disease (CVD) risk and liver steatosis and fibrosis among people with HIV (PLWH) at least 40 years of age on antiretroviral therapy (ART) in low and middle-income countries (LMIC).

DESIGN

We used cross-sectional behavioral and clinical data collected during study enrollment visits in 2020-2022 for the Sentinel Research Network of International epidemiology Databases to Evaluate AIDS (SRN of IeDEA).

METHODS

Ten-year CVD risk was calculated using 2019 WHO nonlaboratory and laboratory models. Transient elastography was used to assess liver disease. Presence of steatosis and significant fibrosis were defined by controlled attenuation parameter (CAP) at least 248 dB/m and liver stiffness measurement (LSM) at least 7.1 kPa, respectively. Participants with viral hepatitis, hazardous alcohol consumption, and unsuppressed HIV viral load were excluded from the analysis. Logistic regression was used to estimate odds ratios, adjusting for study site, CD4 +  T cell count, stavudine and didanosine exposure, and in models stratified by sex and geographic region.

RESULTS

There were 1750 participants from nine LMIC. Median CVD risk was 3% for both nonlaboratory and laboratory-based models. Adjusted odds ratios (ORs) for steatosis and significant fibrosis associated with laboratory CVD risk (≥10 vs. <5%) were OR = 1.83 [95% confidence interval (95% CI) = 1.21-2.76; P  = 0.004] and OR = 1.62 (95% CI = 0.85-3.07; P  = 0.14), respectively. Associations of CVD risk with steatosis were stronger in men and among participants at study sites outside Africa.

CONCLUSION

Higher CVD risk was associated with steatosis but not with significant fibrosis in PWH in our LMIC cohort.

Performance of the non-laboratory based 2019 WHO cardiovascular disease risk prediction chart in Eastern Sub-Saharan Africa

BACKGROUND AND AIMS

The World Health Organization (WHO) updated its cardiovascular disease (CVD) risk prediction charts in 2019 to cover 21 global regions. We aimed to assess the performance of an updated non-lab-based risk chart for people with normoglycaemia, impaired fasting glucose (IFG), and diabetes in Eastern Sub-Saharan Africa.

METHODS AND RESULTS

We used data from six WHO STEPS surveys conducted in Eastern Sub-Saharan Africa between 2012 and 2017. We included 9857 participants aged 40-69 years with no CVD history. The agreement between lab- and non-lab-based charts was assessed using Bland-Altman plots and Cohen's kappa. The median age of the participants was 50 years (25-75th percentile: 44-57). The pooled median 10-year CVD risk was 3 % (25-75th percentile: 2-5) using either chart. According to the estimation, 7.5 % and 8.4 % of the participants showed an estimated CVD risk ≥10 % using the non-lab-based chart or the lab-based chart, respectively. The concordance between the two charts was 91.3 %. The non-lab-based chart underestimated the CVD risk in 57.6 % of people with diabetes. In the Bland-Altman plots, the limits of agreement between the two charts were widest among people with diabetes (-0.57-7.54) compared to IFG (-1.75-1.22) and normoglycaemia (-1.74-1.06). Kappa values of 0.79 (substantial agreement), 0.78 (substantial agreement), and 0.43 (moderate agreement) were obtained among people with normoglycaemia, IFG, and diabetes, respectively.

CONCLUSIONS

Given limited healthcare resources, the updated non-lab-based chart is suitable for CVD risk estimation in the general population without diabetes. Lab-based risk estimation is suitable for individuals with diabetes to avoid risk underestimation.

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.

A comparative assessment between Globorisk and WHO cardiovascular disease risk scores: a population-based study

The Globorisk and WHO cardiovascular risk prediction models are country-specific and region-specific, respectively. The goal of this study was to assess the agreement and correlation between the WHO and Globorisk 10-year cardiovascular disease risk prediction models. The baseline data of 6796 individuals aged 40-74 years who participated in the Fasa cohort study without a history of cardiovascular disease or stroke at baseline were included. In the WHO and Globorisk models scores were calculated using age, sex, systolic blood pressure (SBP), current smoking, diabetes, and total cholesterol for laboratory-based risk and age, sex, SBP, current smoking, and body mass index (BMI) for non-laboratory-based risk (office-based or BMI-based). In Globorisk and WHO risk agreement across risk categories (low, moderate, and high) was examined using the kappa statistic. Also, Pearson correlation coefficients and scatter plots were used to assess the correlation between Globorisk and WHO models. Bland-Altman plots were presented for determination agreement between Globorisk and WHO risk scores in individual's level. In laboratory-based models, agreement across categories was substantial in the overall population (kappa values: 0.75) and also for females (kappa values: 0.74) and males (kappa values: 0.76), when evaluated separately. In non-laboratory-based models, agreement across categories was substantial for the whole population (kappa values: 0.78), and almost perfect for among males (kappa values: 0.82) and substantial for females (kappa values: 0.73). The results showed a very strong positive correlation (r ≥ 0.95) between WHO and Globorisk laboratory-based scores for the whole population, males, and females and also a very strong positive correlation (r > 0.95) between WHO and Globorisk non-laboratory-based scores for the whole population, males, and females. In the laboratory-based models, the limit of agreements was better in males (95%CI 2.1 to - 4.2%) than females (95%CI 4.3 to - 7.3%). Also, in the non-laboratory-based models, the limit of agreements was better in males (95%CI 2.9 to - 4.0%) than females (95%CI 3.2 to - 6.1%). There was a good agreement between both the laboratory-based and the non-laboratory-based WHO models and the Globorisk models. The correlation between two models was very strongly positive. However, in the Globorisk models, more people were in high-risk group than in the WHO models. The scatter plots and Bland-Altman plots showed systematic differences between the two scores that vary according to the level of risk. So, for these models may be necessary to modify the cut points of risk groups. The validity of these models must be determined for this population.

Laboratory-based versus non-laboratory-based World Health Organization risk equations for assessment of cardiovascular disease risk

BACKGROUND

The WHO model has laboratory-based and non-laboratory-based versions for 10-year risk prediction of cardiovascular diseases. Due to the fact that in some settings, there may not be the necessary facilities for risk assessment with a laboratory-based model, the present study aimed to determine the agreement between laboratory-based and non-laboratory-based WHO cardiovascular risk equations.

METHODS

In this cross-sectional study, we used the baseline data of 6796 individuals without a history of cardiovascular disease and stroke who participated in the Fasa cohort study. The risk factors of the laboratory-based model included age, sex, systolic blood pressure (SBP), diabetes, smoking and total cholesterol, while the non-laboratory-based model included age, sex, SBP, smoking and BMI. Kappa coefficients was used to determine the agreement between the grouped risk and Bland-Altman plots were used to determine the agreement between the scores of the two models. Sensitivity and specificity of non-laboratory-based model were measured at the high-risk threshold.

RESULTS

In the whole population, the agreement between the grouped risk of the two models was substantial (percent agreement = 79.0%, kappa = 0.68). The agreement was better in males than in females. A substantial agreement was observed in all males (percent agreement = 79.8%, kappa = 0.70) and males < 60 years old (percent agreement = 79.9%, kappa = 0.67). The agreement in males ≥ 60 years old was moderate (percent agreement = 79.7%, kappa = 0.59). The agreement among females was also substantial (percent agreement = 78.3%, kappa = 0.66). The agreement for females < 60 years old, (percent agreement = 78.8%, kappa = 0.61) was substantial and for females ≥ 60 years old, (percent agreement = 75.8%, kappa = 0.46) was moderate. According to Bland-Altman plots, the limit of agreement was (95%CI: -4.2% to 4.3%) for males and (95%CI: -4.1% to 4.6%) for females. The range of agreement was suitable for both males < 60 years (95%CI: -3.8% to 4.0%) and females < 60 years (95%CI: -3.6% to 3.9%). However, it was not suitable for males ≥ 60 years (95% CI: -5.8% to 5.5%) and females ≥ 60 years (95%CI: -5.7% to 7.4%). At the high-risk threshold of 20% in non-laboratory and laboratory-based models, the sensitivity of the non-laboratory-based model was 25.7%, 70.7%, 35.7%, and 35.4% for males < 60 years, males ≥ 60 years, females < 60 years, and females ≥ 60 years, respectively. At the high-risk threshold of 10% in non-laboratory-based and 20% in laboratory-based models, the non-laboratory model has high sensitivity of 100% for males ≥ 60 years, females < 60 years, females ≥ 60 years, and 91.4% for males < 60 years.

CONCLUSION

A good agreement was observed between laboratory-based and non-laboratory-based versions of the WHO risk model. Also, at the risk threshold of 10% to detect high-risk individuals, the non-laboratory-based model has acceptable sensitivity for practical risk assessment and the screening programs in settings where resources are limited and people do not have access to laboratory tests.