# Red Blood Cell, White Blood Cell, and Platelet Counts as Differentiating Factors in Cardiovascular Patients with and Without Current Myocardial Infarction

## TL;DR

This study shows that blood cell counts can help distinguish cardiovascular patients with and without recent heart attacks, using advanced statistical methods.

## Contribution

The study introduces a dual analytic approach combining parametric methods and bootstrap resampling to improve inference stability in clinical research.

## Key findings

- Patients with myocardial infarction had lower RBC and higher WBC counts compared to those without.
- Group differences in RBC and WBC were most pronounced in the second and third quartiles of their distributions.
- Bootstrap resampling validated traditional statistical findings and improved robustness in skewed or small datasets.

## Abstract

Cardiovascular diseases continue to pose a major global health burden, contributing significantly to mortality rates worldwide. This study aimed to explore the association between myocardial infarction and basic hematological parameters—red blood cells (RBCs), white blood cells (WBCs), and platelets (PLTs)—which are routinely assessed in clinical diagnostics. The analysis was conducted on a cohort of 743 adults hospitalized with diagnosed cardiovascular conditions. To identify blood parameters that distinguish patients with a history of first-time myocardial infarction from those who had never experienced such an event, we employed a dual analytic approach. Standard parametric methods were complemented with bootstrap resampling to strengthen inference and mitigate the impact of sampling variability. Patients with myocardial infarction showed decreased RBC and elevated WBC counts relative to those without infarction. These associations were non-linear, with the most pronounced group differences observed within the second and third quartiles of RBC and WBC distributions, while minimal differences appeared at the distributional extremes. No significant differences were found in platelet count (PLT) between the groups. Bootstrap validation not only corroborated findings obtained through traditional statistics, but also enhanced the robustness of the results, providing improved estimates under data conditions prone to skewness or small sample artifacts. This approach enabled the detection of nuanced patterns that might elude classical inference. Our findings emphasize the utility of resampling techniques in clinical research settings, particularly where inference stability is critical. Incorporating such methods in future investigations may advance statistical rigor, increase reproducibility, and better capture complex biological relationships in medical datasets.