Development of a CVD mortality risk score using nutritional predictors: A risk prediction model in the Golestan Cohort Study

Abstract

Background and aim

We aimed to develop a dietary score using prediction model method for evaluating the risk of cardiovascular disease (CVD) mortality and suggesting a simple and practical scoring system within the healthcare context.

Method and results

A total of 43878 adult participants (aged 37–80 years) from the Golestan Cohort Study (GCS) were included in analysis. A random split of the subjects into the derivation (n = 28930) and the validation sets (n = 14948) was done. The Cox proportional hazard model was used to develop prediction model for the 8-year risk of CVD mortality. The model's discrimination and calibration were assessed by C-statistic and calibration plot, respectively. To enhance clinical utility, we devised a point-based scoring system derived from our model. This prediction model was developed by nine predictors including age, physical activity level (MET minutes/week), waist-to-hip ratio, tea intake (cup/day), vegetable intake (gr/1000 kcal/day), white meat intake (gr/1000 kcal/day), salt intake (gr/1000 kcal/day), dairy intake (Cup/1000 kcal/day), and percentage of protein intake. The model had an acceptable discrimination in both derivation (C-statistic: 0.76, p < 0.001) and validation (C- statistic: 0.77, p < 0.001) samples. Also, the calibration of model in both derivation and validation datasets was 0.81.

Conclusion

This is the first attempt to develop a risk prediction model of CVD mortality and the risk scoring system by the majority of nutritional predictors in a large cohort study. This nutritional risk assessment tool is suitable for motivating at-risk individuals to make lifestyle and dietary pattern changes to reduce future risk to prevent health problems.