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Platelet distribution widths and white blood cell are associated with cardiovascular diseases: data mining approaches
Mohadeseh Poudineh1, Amin Mansoori2,3, Elias Sadooghi Rad4
1Student Research Committee, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran.
Insights
Age, white blood cell count, and platelet distribution width are key indicators for cardiovascular disease (CVD) risk in Iranian adults. These hematologic factors significantly correlate with CVD development, highlighting their importance in risk assessment.
Area of Science:
- Cardiovascular Health
- Hematology
- Epidemiology
Background:
- Cardiovascular diseases (CVDs) pose a significant global health burden.
- Understanding the association between hematologic factors and CVD is crucial for risk stratification.
Purpose of the Study:
- To investigate the relationship between various hematologic factors and cardiovascular diseases (CVDs) in a cohort of Iranian adults.
- To identify key hematologic predictors of CVD development.
Main Methods:
- A prospective study involving 9,704 Iranian adults aged 35-65.
- Data collection included demographic characteristics and hematologic factors.
- Logistic regression, decision tree, and bootstrap forest analyses were employed to assess associations.
Main Results:
- Age, white blood cell (WBC) count, and platelet distribution width (PDW) were strongly associated with CVD development (p<.001).
- WBC (OR: 1.22) and age (OR: 1.12) were identified as significant risk factors.
- Decision tree and bootstrap forest models highlighted age, WBC, and PDW as the most crucial factors, with sex and smoking status also noted.
Conclusions:
- Age, WBC, and PDW are confirmed as the most critical factors influencing CVD development.
- These findings underscore the importance of monitoring hematologic parameters for CVD risk assessment in this population.
Objective:
To investigate the association between cardiovascular diseases (CVDs) and haematologic factors in a cohort of Iranian adults.
Method:
For a total population of 9,704 aged 35 to 65, a prospective study was designed. Haematologic factors and demographic characteristics (such as gender, age, and smoking status) were completed for all participants. The association between haematologic factors and CVDs was assessed through logistic regression (LR) analysis, decision tree (DT), and bootstrap forest (BF).
Results:
Almost all of the included factors were significantly associated with CVD (p<.001). Among the included factors, were: age, white blood cell (WBC), and platelet distribution width (PDW) had the strongest correlation with the development of CVD. For unit OR interpretation, WBC has been represented as the most remarkable risk factor for CVD (OR: 1.22 (CI 95% (1.18, 1.27))). Also, age is associated with an increase in the odds of CVD + occurrence (OR: 1.12 (CI 95% (1.11, 1.13))). Moreover, males are times more likely to develop CVD than females (OR: 1.39 (CI 95% (1.22, 1.58))). In DT model, age is the best classifier factor in CVD development, followed by WBC and PDW. Furthermore, based on the BF algorithm, the most crucial factors correlated with CVD are age, WBC, PDW, sex, and smoking status.
Conclusion:
The obtained result from LR, DT, and BF models confirmed that age, WBC, and PDW are the most crucial factors for the development of CVD.
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