Framingham cardiovascular risk distribution among adults with data sufficient for risk calculation in the Jeddah First Health Cluster non-communicable disease program: A cross-sectional electronic health-record study

Objectives: Cardiovascular disease is among the leading causes of morbidity and mortality in Saudi Arabia, driven by a high prevalence of metabolic and behavioral risk factors, and risk-prediction tools such as the Framingham Risk Score (FRS) are widely applied in primary-care and population-screening settings. However, population- level risk stratification depends not only on a valid risk model but also on the completeness of the routine data that model requires. Two gaps therefore remain: Limited evidence describes how estimated cardiovascular risk is distributed within integrated Saudi health systems, and the feasibility of using routinely collected electronic health-record (EHR) data for population-level risk stratification in these systems is unestablished. This study characterized the distribution and clustering of cardiovascular risk, and the completeness of the underlying EHR data, among adults enrolled in the non-communicable disease (NCD) program of the Jeddah First Health Cluster. Methods: A cross-sectional analytical study was conducted using routinely collected EHR data from 230,628 adults enrolled in the NCD Program. The completeness of the eight variables required for FRS calculation was first quantified across this full enrolled population; the 10,165 enrollees (4.4%) with data sufficient for risk calculation constituted the analytic sample. Within this sample, 10-year cardiovascular risk was estimated with the FRS, categorized as low (<10%), intermediate (10–19.9%), or high (≥20%), and described overall and by age, sex, and number of co-occurring modifiable risk factors. Analyzed and non-analyzed enrollees were then compared using standardized differences. Because the candidate predictors are components of the FRS itself, no regression predicting calculated risk was undertaken; effect sizes were emphasized given the large sample size. Results: Within the analytic sample ( n = 10,165), the mean age was 52.6 ± 12.0 years, diabetes mellitus was present in 49.0%, and 41.2% were male. Overall, 51.4% were classified as low risk, 24.1% as intermediate, and 24.5% as high risk according to the Framingham model. High-risk prevalence was progressively higher across age groups, from 0.4% among those aged 30–39 years to 58.9% among those aged ≥60 years and was markedly higher in men than women (42.7% vs. 11.8%). High-risk prevalence was also higher with a greater number of co-occurring modifiable risk factors, from 1.3% among patients with none to 66.0% among those with three or more. Critically, across the full enrolled population of 230,628 adults, the clinical and laboratory data required This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. ©2026 Published by Scientific Scholar on behalf of International Journal of Health Sciences for risk estimation were unrecorded for the majority (systolic blood pressure 72.0%, total cholesterol 88.6%, and high-density lipoprotein cholesterol 92.4%), and the 10,165 analyzed patients were substantially older and higher-risk than the 220,463 excluded enrollees (diabetes 49.0% vs. 9.4%; and antihypertensive treatment 21.6% vs. 4.1%). Conclusion: Cardiovascular risk was substantial and strongly patterned by age and sex among NCD-program enrollees with data sufficient for risk calculation and was markedly higher where modifiable risk factors co-occurred. However, missing blood pressure and lipid measurements meant that risk could not be calculated for the large majority of enrollees. This documentation gap is itself an actionable health-system finding: Strengthening the structured capture of blood-pressure and lipid data is a prerequisite for the data-driven, preventive care envisaged under Vision 2030.