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Updated: Jun 24, 2026
A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology
Published on: February 7, 2015
A consultation-based method is equal to SCORE and an extensive laboratory-based method in predicting risk of future
Ulla Petersson1, Carl Johan Ostgren, Lars Brudin
1Primary Health Care Centre, Söderåkra, Sweden. ullape@ltkalmar.se
Insights
A simple, non-laboratory risk assessment accurately predicts cardiovascular disease (CVD) risk, matching the performance of the established SCORE method and complex lab tests. This approach simplifies CVD risk evaluation in primary care settings.
Area of Science:
- Cardiology
- Preventive Medicine
- Epidemiology
Background:
- Cardiovascular disease (CVD) is a leading global cause of mortality.
- Accurate prediction of cardiovascular risk is crucial for effective prevention strategies.
Purpose of the Study:
- To compare the predictive accuracy of a non-laboratory risk assessment model with the Systemic COronary Risk Evaluation (SCORE) and a laboratory-based model for cardiovascular events.
- To evaluate the utility of easily obtainable clinical data versus complex laboratory analyses in predicting long-term CVD risk.
Main Methods:
- A 17-year follow-up study in Southern Sweden involving 689 participants aged 40-59 without pre-existing CVD.
- Comparison of a consultation-based risk assessment (age, sex, smoking, diabetes, hypertension, blood pressure, waist/height ratio, family history) against SCORE and a laboratory-based model.
- Laboratory analyses included blood glucose, lipids, insulin, IGF-I, IGFBP-1, CRP, ADMA, and SDMA.
Main Results:
- The non-laboratory model predicted cardiovascular events as accurately as the SCORE algorithm (HR: 2.72 vs. 2.73).
- Adding extensive laboratory measurements did not improve CVD risk prediction compared to the non-laboratory model (HR: 2.72).
- The predictive accuracy (c-statistics) of the consultation model was comparable to SCORE and the laboratory-based model, with no significant differences.
Conclusions:
- A risk algorithm using non-laboratory data from a single primary care consultation effectively predicts long-term cardiovascular risk.
- This consultation-based method offers comparable accuracy to established risk scores and complex laboratory assessments.
- The findings support the use of simpler, non-laboratory methods for cardiovascular risk assessment in primary care settings.
Background:
As cardiovascular disease (CVD) is one of the most common causes of mortality worldwide, much interest has been focused on reliable methods to predict cardiovascular risk.
Design:
A cross-sectional, population-based screening study with 17-year follow-up in Southern Sweden.
Methods:
We compared a non-laboratory, consultation-based risk assessment method comprising age, sex, present smoking, prevalent diabetes or hypertension at baseline, blood pressure (systolic > or =140 or diastolic > or =90), waist/height ratio and family history of CVD to Systemic COronary Risk Evaluation (SCORE) and a third model including several laboratory analyses, respectively, in predicting CVD risk. The study included clinical baseline data on 689 participants aged 40-59 years without CVD. Blood samples were analyzed for blood glucose, serum lipids, insulin, insulin-like growth factor-I, insulin-like growth factor binding protein-1, C-reactive protein, asymmetric dimethyl arginine and symmetric dimethyl arginine. During 17 years, the incidence of total CVD (first event) and death was registered.
Results:
A non-laboratory-based risk assessment model, including variables easily obtained during one consultation visit to a general practitioner, predicted cardiovascular events as accurately [hazard ratio (HR): 2.72; 95% confidence interval (CI): 2.18-3.39, P<0.001] as the established SCORE algorithm (HR: 2.73; 95% CI: 2.10-3.55, P<0.001), which requires laboratory testing. Furthermore, adding a combination of sophisticated laboratory measurements covering lipids, inflammation and endothelial dysfunction, did not confer any additional value to the prediction of CVD risk (HR: 2.72; 95% CI: 2.19-3.37, P<0.001). The c-statistics for the consultation model (0.794; 95% CI: 0.762-0.823) was not significantly different from SCORE (0.767; 95% CI: 0.733-0.798, P=0.12) or the extended model (0.806; 95% CI: 0.774-0.835, P=0.55).
Conclusion:
A risk algorithm based on non-laboratory data from a single primary care consultation predicted long-term cardiovascular risk as accurately as either SCORE or an elaborate laboratory-based method in a defined middle-aged population.
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