sfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, IR.
Department of Epidemiology and Biostatistics, School of Public Health, Isfahan University of Medical Sciences, Isfahan, IR.
Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politècnica de Catalunya, BarcelonaTech (UPC), Barcelona, ES.
Biomedical Engineering Department, Engineering faculty, University of Isfahan, Isfahan, IR.
Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, CA.
Cardiovascular Medicine Division, Brigham and Women’s Hospital, Boston, US.
Epidemiology and Biostatistics Department, Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, NZ.
Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, CA.
Background: Developing simplified risk assessment model based on non-laboratory risk factors that could determine cardiovascular risk as accurately as laboratory-based one can be valuable, particularly in developing countries where there are limited resources.
Objective: To develop a simplified non-laboratory cardiovascular disease risk assessment chart based on previously reported laboratory-based chart and evaluate internal and external validation, and recalibration of both risk models to assess the performance of risk scoring tools in other population.
Methods: A 10-year non-laboratory-based risk prediction chart was developed for fatal and non-fatal CVD using Cox Proportional Hazard regression. Data from the Isfahan Cohort Study (ICS), a population-based study among 6504 adults aged ≥ 35 years, followed-up for at least ten years was used for the non-laboratory-based model derivation. Participants were followed up until the occurrence of CVD events. Tehran Lipid and Glucose Study (TLGS) data was used to evaluate the external validity of both non-laboratory and laboratory risk assessment models in other populations rather than one used in the model derivation.
Results: The discrimination and calibration analysis of the non-laboratory model showed the following values of Harrell’s C: 0.73 (95% CI 0.71-0.74), and Nam-D’Agostino χ2:11.01 (p = 0.27), respectively. The non-laboratory model was in agreement and classified high risk and low risk patients as accurately as the laboratory one. Both non-laboratory and laboratory risk prediction models showed good discrimination in the external validation, with Harrell’s C of 0.77 (95% CI 0.75-0.78) and 0.78 (95% CI 0.76-0.79), respectively.
Conclusions: Our simplified risk assessment model based on non-laboratory risk factors could determine cardiovascular risk as accurately as laboratory-based one. This approach can provide simple risk assessment tool where laboratory testing is unavailable, inconvenient, and costly.