Mean Arterial Pressure Classification: A Better Tool for Statistical Interpretation of Blood Pressure Related Risk Covariates

Purpose: Both Systolic blood pressure (SBP) and diastolic blood pressure (DBP) are equally important to analyze the associations between blood pressure and its associated risk covariates. Quantitative analyses however, sometime provide separate results for SBP and DBP. It is more evident in people with systolic or diastolic hypertension. It sometime becomes difficult to interpret while performing statistical analyses. Mean arterial pressure (MAP) which is a time-weighted average of the arterial pressure over the whole cardiac cycle is a very useful tool for biological and medical science. But, till date to the best of our knowledge, no classifications available like blood pressures. So, in this paper a classification of MAP was formulated following the blood pressure classification as recommended by World Health Organization (WHO) and European Society of Hypertension and European Society of Cardiology (ESH/ESC). The resultant value of MAP was then classified into several categories like, optimal, normal, high normal and so on. The present article is therefore, an attempt to postulate the MAP classification as innovative method for better statistical analyses, screening and analyses in association studies related to blood pressures.

Materials and Methods: SBP & DBP were measured on right arm in sitting posture by means of aneroid sphygmomanometer and stethoscope. Pulse pressure and MAP was computed as per the standard formula. Necessary statistical analyses were performed using SPSS (version 14.0).

Results: It was found that MAP was a better predictor of blood pressure associated with risk covariates like Body Mass Index (BMI) and Waist Circumference (WC) as compare to SBP and DBP separately. Both correlation and stepwise regression analyses shows that the MAP is no less significant than SBP and DBP by considering blood pressure as dependent and BMI & WC as independent variables.

Conclusion: A researcher can therefore use this MAP classification for data analysis as it will yield only one statistical result instead of two separate results (i.e. SBP and DBP) as to observe the relation of blood pressure (MAP) with different risk covariates. The vascular complications associated with hypertension including stroke, cardiovascular disease, chronic renal failure etc. require regular screening to avoid serious organ damage. Classification of MAP would therefore be more effective than blood pressure classifications not only in clinical practice but in public health as well. MAP classification would immensely help in translating large epidemiological data in to meaningful statistical interpretations.