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Development of a cost-effective CVD prediction model using lifestyle factors. A cohort study in Pakistan
Parveen Naheeda1, Khan Sharifullah1, Shah Saeed Ullah2
1School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Insights
A new cardiovascular disease (CVD) risk model uses lifestyle factors like age and waist circumference, achieving 72.9% accuracy without lab tests. This rapid, low-cost tool is ideal for developing nations like Pakistan.
Area of Science:
- Public Health
- Cardiology
- Epidemiology
Background:
- Cardiovascular diseases (CVD) are a leading cause of death in Pakistan, linked to lifestyle factors.
- Current risk assessment tools rely on laboratory tests, with a gap in models based on lifestyle.
- Poor adherence to healthy lifestyles and lack of awareness exacerbate CVD prevalence.
Purpose of the Study:
- To develop an alternative cardiovascular disease (CVD) risk estimation model.
- The model focuses on lifestyle factors and physical attributes, excluding laboratory investigations.
- It aims to serve as a practical alternative to existing clinical risk scores.
Main Methods:
- A regression model was formulated using clinical and lifestyle data from 160 subjects.
- Independent variables included BMI, waist circumference, physical activity, smoking, diet, and stress.
- Cardiovascular disease risk probability from the QRISK model served as the dependent variable.
Main Results:
- Chronological age, waist circumference, BMI, and strength significantly influenced CVD risk probability.
- The developed model demonstrated 72.9% accuracy in predicting CVD risk for the study population.
- Key lifestyle indicators were identified as significant predictors of cardiovascular risk.
Conclusions:
- The proposed model accurately estimates CVD risk using only easily measurable, non-clinical features.
- It offers a rapid and cost-effective method for risk assessment.
- This approach is particularly suitable for resource-limited settings, such as Pakistan.
Background:
Cardiovascular diseases (CVD) such as hypertension and ischemic heart diseases cause 35 to 40% of deaths every year in Pakistan. Several lifestyle factors such as dietary habits, lack of exercise, mental stress, body habitus (i.e., body mass index, waist), personal habits (smoking, sleep, fitness) and clinical conditions (i.e., diabetes, dyslipidemia and hypertension) have been shown to be strongly associated with the etiology of CVD. Epidemiological studies in Pakistan have shown poor adherence of people to healthy lifestyle and lack of knowledge in adopting healthy alternatives. There are well validated cardiovascular risk estimation tools (QRISK model) that cn predict the probability of future cardiac events. The existing tools are based on laboratory investigations of biochemical test but there is no widely accepted tool available that predicts the CVD risk probability based on lifestyle factors.
Aims:
Aim of the current study was to develop alternative CVD risk estimation model based on lifestyle factors and physical attributes (without using laboratory investigation) using QRISK model as the gold standard.
Study Design:
Clinical and lifestyle data of one hundred and sixty subjects were collected to formulate a regression model for predicting CVD risk probability.
Methods:
Lifestyle factors as independent variables (IV) include BMI, waist circumference, physical activities (stamina, strength, flexibility, posture), smoking, general illnesses, dietary intake, stress and physical characteristics. CVD risk probability of QRISK Intervention computed through clinical variables was used as a dependent variable (DV) in present research. Chronological age was also included in analysis in addition to selected lifestyle factors. Regression analysis, principal component analysis and bivariate correlations were applied to assess the relationship among predictor variables and cardiovascular risk score.
Results:
Chronological age, waist circumference, BMI and strength showed significant effect on CVD risk probability. The proposed model can be used to calculate CVD risk probability with 72.9% accuracy for the targeted population.
Conclusion:
The model involves only those features which can be measured without any clinical test. The proposed model is rapid and less costly hence appropriate for use in developing countries like Pakistan.
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