Preventing Heart Attack in BG. [W. Feeman Jr. J. Lu J. Chen]

Transcription

1 Preventing Heart Attack in BG [W. Feeman Jr. J. Lu J. Chen]

2 Motivation Heart attack is one of the leading causes of morbidity and mortality of American Statistically identifying potential risk factors for heart attack is critical in preventive care for public health

3 Current Tool of Diagnosis Framingham Risk Score Developed 30 years ago on the basis of a study in Framingham a town similar to Bowling Green FRC includes age, total cholesterol level, smoking status, and systolic blood pressure as main factors for diagnosis

4 Problem with Framingham Risk Score FRC is limited to the population of Framingham. Prediction errors occur due to unjustified factors included in the calculation of FRC FRC is also limited to the statistical inference techniques 30 year ago. With innovative methods developed in statistics, more accurate diagnosis tools are on call

5 A Statistical research project in Bowling Green forging. Undergraduate research, Academic challenge, and Community health

6 Project Planning William Feeman, Jr. --- A cardiologist in Wood county hospital, a veteran in this research field for 30 years with accumulated clinical experience and data sets Jiabin Lu --- A senior majoring in statistics in the Department of Mathematics and Statistics at BGSU John Chen --- A statistician in the Department of Mathematics and Statistics at BGSU The project was partly supported by the Research Incentive Grant of the Faculty Research Committee at BGSU

7 Project Planning Formulating and process raw data Analyzing risk factors in the Bowling Green area Perform statistical analysis to identify significant risk factors on heart attack Aware the BG public on the statistically significant risk factors for preventive care/intervention

8 Research Results -1 The plot of CRF^2 versus systolic blood pressure in Figure 1 indicates that the squared cholesterol retention fraction is closely related to heart attacks. The significance of systolic blood pressure as a risk factor of heart attack is also portrayed in Figure 1. The outcome of logistic regression numerically confirmed the significance of CRF^2 combined with SBP, P-value

9 Figure 1 cr f SBP- 1 F CRF2: Squar ed f r act i on of hydensi t y chol est r ol i n bl ood The plot is CRF vs syst ol i c bl ood presure.

10 Research Results -2 The Cholesterol retention fraction combined with triglyceride level serves as a clear indicator for heart attacks (Figure 2) There is a linear trend between CRF and triglyceride level. The numerical analysis shows that the cholesterol retention fraction combined with triglyceride level may serve as a diagnosis tool. P-value and 0.019

11 Figure -2 CRF T6 F14 0 1

12 Research Results -3 Body Mess Index is another signal of heart attack, in conjunction with CRF Figure 3 clearly shows that both of the two factors are significant risk factors for heart attacks The output of logistic regression gets the p- values of (CRF) and (BMI) Lower CRF means less high density cholesterol in the blood stream; higher value of BMI increases the risk for the clog of blood vessels.

13 Figure -3 CRF T6 F CRF: Fr act i on of HDC i n bl ood, T6: Tr i gl ycer i de l evel The plot is CRF vs T6.

14 Research Results -4 Body mess index becomes insignificant when systolic blood pressure is used as one of the diagnosis measures. This can be seen from the plot of the data in Figure 4 Numerical analysis supports the above conclusion, the p-value for BMI is

15 Figure -4 SBP BMI F Syst ol i c bl ood pr essur e, BMI: Body mass i ndex The plot is SBP vs BMI.

16 Conclusions The project identifies some interesting risk factors that necessitate further research Due to colinearity, the Framingham score is not as accurate as the combination of risk factors as shown in the data set Further research is needed to reach the final goal of the project