Performance of non-laboratory Framingham risk scores using self-reported data for remote cardiovascular risk assessment.

Traditional cardiovascular disease (CVD) risk scores, such as the Framingham Risk Score (FRS), rely on laboratory and clinical measurements often unavailable in large-scale remote health surveys. Validated tools using self-reported data could expand the feasibility of risk stratification in resource-limited settings. To assess the discriminatory capacity and agreement of adapted FRS models using only self-reported data, compared to the original FRS based on clinical and laboratory inputs. This cross-sectional study used data from 6,966 Brazilian adults aged 30-74 years from the 2013 National Health Survey. Three FRS models without laboratory inputs were evaluated: FRS-BMI (with measured BMI), FRS-BMI-HwT (using self-reported BMI and hypertension diagnosis with treatment data), and FRS-BMI-HnT (using self-reported BMI and hypertension diagnosis without treatment data). The original FRS served as the reference method. Concordance was assessed for CVD-risk ≥ 5%, ≥ 10%, and ≥ 20% using Receiver Operating Characteristic (ROC) curves and a suite of statistical measures for reliability and agreement [Intraclass Correlation Coefficient (ICC), Bland-Altman, Cohen's Kappa]. Optimal cutoffs were identified by maximizing Youden's index, with stability assessed through bootstrap validation. Additional continuous accuracy metrics were computed, including Brier Score, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Integrated Discrimination Improvement (IDI). Prevalence of CVD-risk ≥ 5%, ≥ 10%, and ≥ 20% by FRS-Original was 55.4%, 35.9%, and 20.4%, respectively. FRS-BMI achieved the highest AUCs (0.90, 0.88, 0.83), followed by FRS-BMI-HwT and FRS-BMI-HnT (0.86-0.83 and 0.86-0.78). Concordance was substantial for CVD-risk ≥ 5% and 10% (kappa > 0.70) and moderate for ≥ 20% (kappa > 0.55). All models demonstrated excellent predictive accuracy (Brier Score < 0.01) with minimal IDI values (-0.0014 to 0.0056), indicating nearly identical discrimination between adapted and original models. Bootstrap validation confirmed excellent stability of optimal thresholds (bias < 0.02). The adapted models slightly underestimated risk (mean score difference: -0.36 to -0.61). Regression models showed consistent associations with key risk factors across all versions. Self-reported FRS models demonstrated strong discriminatory capacity and high agreement with the original FRS, supporting their use in telephone and online surveys where laboratory data are unavailable. These pragmatic tools offer reliable alternatives for CVD-risk stratification in remote, low-resource, or large-scale epidemiological research.