Research Article
Volume 7 Issue 3 - 2020
Enhanced Prediction of the Population at Risk of Atherothrombotic Disease: Back to Framingham
William E Feeman Jr*
Bowling Green Study, Bowling Green, OH, USA
*Corresponding Author: William E Feeman Jr, Bowling Green Study, Bowling Green, OH, USA.
Received: November 05, 2019; Published: February 12, 2020




Abstract

Background: There are many tools that attempt to predict the population at risk of atherothrombotic disease (ATD). These tools are not well accepted and are often not accurate; most are not used at all. The Framingham Heart Study pioneered the prediction of the population at risk of ATD and still remains the basis upon which the current predictive tools are based. The predictive tool to be discussed in this paper is founded upon the original fundamental tenets devised by the Framingham Study and is based upon the risk factor complex of 869 people who developed some clinical form of ATD in the Bowling Green Study (BGS) during the 4 November 1974-1 January 2019 timeframe.

Objectives: The author will demonstrate that it is possible to predict the various risk profiles of the population at risk of ATD. This is important because the primary prevention of ATD requires knowledge of whom is at risk and an estimation of what that risk is.

Methods: The author has performed a chart review to collect a database of the ATD risk factors of the 869 people who developed some form of clinical ATD during the study timeframe. The ATD risk factors include dyslipidemia, cigarette smoking, and hypertension, with some contribution by the very high blood sugar levels of uncontrolled diabetes. He has analyzed this risk factor data to create a tool that defines the population at risk of ATD with high accuracy.

Results: Using the Cholesterol Retention Fraction (CRF, defined as low-density lipoprotein [LDL] cholesterol minus high-density lipoprotein [HDL] cholesterol, the difference divided by low-density lipoprotein cholesterol) as a measure of dyslipidemia and systolic blood pressure (SBP) as measure of hypertension, the author created a graph with the CRF on the ordinate and SBP on the abscissa. When the CRF-SBP plots of all of the author’s ATD patients are plotted on the BGS graph, a scattergram appearance is noted. Once current cigarette smoking patients have been excluded, the CRF-SBP plots of the remaining ATD patients fall into a mainstream collection that lies above the CRF demarcation line at 0.70. A relatively few outliers exist. A threshold line, based on the principle of the fewest false positives, is generated. The average age at ATD onset, multi-system ATD onset, and age at death with respect to the threshold line is described. The area on the BGS graph can be divided into 48 CRF-SBP cohorts based on CRF sextiles and SBP octiles. The average age of ATD onset is determined for each of the 48 CRF-SBP cohorts and risk is assigned according to average age of ATD onset in each cohort. Highest risk is assigned to those people whose cohorts are characterized by an average age of ATD onset of 64 years or less; intermediate risk, by an average age of ATD onset of 65-74 years, and lowest risk, by an average age of ATD onset of 75 years and older. The incidence of ATD per these same CRF-SBP cohorts, in the general population, reveals that the incidence of ATD is lowest where the CRF and SBP cohorts are lowest, in the southwest corner of the graph. Finally, the cumulative ATD incidence per CRF sextile can give an estimation as to when dyslipidemic therapy should be initiated.

Conclusion: Based of the characteristics of patients with known clinical ATD, the author has generated a graph that defines the ATD population with high accuracy. People whose CRF-SBP plots lie above the threshold line can be expected to develop clinical ATD at some point in their lives, depending upon the severity of their risk factors and the length of time those risk factors have been operative. People whose CRF-SBP plots lie below the threshold line, in the absence of cigarette smoking, are at little risk of ATD events until very late in life. For people at risk of ATD, that risk can be further defined by dividing the BGS graph area into CRF-SBP cohorts as described.

Keywords: Atherothrombotic Disease; Prediction of the Population at Risk; Cholesterol Retention Fraction; Cigarette Smoking; Hypertension

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Citation: William E Feeman Jr. “Enhanced Prediction of the Population at Risk of Atherothrombotic Disease: Back to Framingham”. EC Cardiology 7.3 (2020): 01-19.

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