Modeling on the Perfusion Index for the Students of the Statistics Department in Mawlana Bhashani Science and Technology University

Abstract

This study aimed to explore the key determinants of the perfusion index (PI)—a physiological marker reflecting the ratio of pulsatile to non-pulsatile blood flow in peripheral tissue, measured using a pulse oximeter. The research sought not only to identify factors influencing the increase or decrease in PI but also to develop a predictive model based on associated variables. Data were collected from Statistics students at MBSTU through stratified random sampling, using a structured questionnaire comprising 37 items. Relevant physiological measurements, including weight, height, pulse rate, systolic and diastolic blood pressure, and PI, were obtained using appropriate instruments. Univariate and bivariate analyses were conducted to assess significant relationships. PI was found to be positively correlated with diastolic pressure, body weight and exercise duration. A predictive model incorporating smoking status, exercise time, and body weight yielded an R² of 0.774, suggesting strong explanatory power. Further research could enhance model accuracy by increasing the sample size, incorporating more variables, and including a broader age range. As PI reflects cardiovascular health and heart strength, promoting physical activity and a healthy diet is essential for improving perfusion.