Day 1 :
eclaireMD Foundation, USA
Keynote: Using Math-Physical Medicine to Study the Probability of Having a Heart Attack or Stroke Based on Lifestyle Management
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The author received an honorable PhD in mathematics and majored in engineering at MIT. He attended different universities over 17 years and studied seven academic disciplines. He has spent 20,000 hours in T2D research. First, he studied six metabolic diseases and food nutrition during 2010-2013, then conducted research during 2014-2018. His approach is “math-physics and quantitative medicine” based on mathematics, physics, engineering modeling, signal processing, computer science, big data analytics, statistics, machine learning, and AI. His main focus is on preventive medicine using prediction tools. He believes that the better the prediction, the more control you have.
Background and Aim:
The author has extended his 8-year T2D research to focus on the relationship between lifestyle for managing metabolic diseases and the probability of having a heart attack or stroke.
Material and Method:
He has developed several big data numerical simulation models using ~1.5M data. Initially, he chose age, gender, race, family history, smoking, drinking, drug abuse, medical health conditions, and weight/waistline to establish a static baseline. He then developed a mathematical simulation model to combine all key elements of lifestyle management, including food, exercise, stress, sleep, water intake, life routine to conduct his dynamic computations. He utilized 295,620 data of these six categories within the past 2,274 days to compute the probability of having a heart attack or stroke in the near future. Finally, he conducted sensitivity analyses to cover the probability variance using different weighting factors (WF).
Comparing the results from the worst year, 2000, to the health-improving period of 2012-2018, the probability values are:
2000 with BMI 31: 83%
(Three episodes of chest pain during 2001-2006)
2012 with BMI 29: 70%
2018 with BMI 25: 33%
(Normalization Range: 0% - 100%)
In summary, within eight years, he has an average of 34% probability with +/- 18% variance of WF sensitivity.
The mathematical simulation results are validated by past health examination reports. This big data dynamic simulation approach using math-physical medicine will provide an early warning to patients with chronic disease of having a heart attack or stroke in the future.
Center for Organizational Research USA
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Damien Byas Currently serving as a professor in a Master of Public Health (MPH) Program, Senior Research Fellow, and an adjunct professor for an MPH program
Statement of the Problem: The World Health Organization (2017) recently reported that “worldwide, at least 2.8 million people die each year as a result of being overweight or obese, and an estimated 35.8 million (2.3%) of global Disability-adjusted life years (DALYs) are caused by overweight or obesity. The purpose of this study was examine identifiable risk factors and disease outcomes which may be associated with obesity prevalence rates in children and adult populations. Methodology & Theoretical Orientation: This study examined inpatient pediatric patients using the Kids´ Inpatient Database (KID), Healthcare Cost and Utilization Project (HCUP), and the Agency for Healthcare Research and Quality (AHRQ, 2014;2016). A large randomly drawn sample (N = 524,581) of boys (n = 244,553) and girls (n = 280,028) ages 5 to 12, was examined in this research study to test for the association between obesity prevalence and disease related outcomes. Additionally, a small adult sample of adults ages 19 to 55 (N = 143), enrolled in an undergraduate level city college program, were assessed to determine if there was a relationship between obesity prevalence and the outcomes of heart disease risk and type 2 Diabetes risk. The Pearson Chi Square test was applied to measure for significant variable associations in this research study in addition to the application of the Cramer’s V analysis to examine for strength of variable associations. A multiple regression analysis was applied to determine if heart disease risk and type 2 diabetes risk were significant predictors of obesity prevalence in adult groups. Findings: The research found that there were significant associations between obesity and health outcomes in children (p < .001) and that the factors of heart disease risk and type 2 diabetes risk were significant predictors for obesity prevalence in adults (p < .05). Conclusion & Significance: The outcome of this research study provides support for improved efforts to develop more effective strategies to promote positive healthy lifestyles in adults and children’s populations.
Stony Brook University, USA
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Shade Akande has completed her Doctor of Nursing practice from Stony Brook University. NY, USA in the year 2015. She has given numerous podium presentations related to nursing practice. As a Clinician, she has the expertise, leadership and motivation to successfully contribute to the mission and values of programs and the institution as a whole. She is dedicated to continuously deliver excellent and quality care to the population with increased productivity and positive outcome, fostering education and to embrace the concept of continuous performance improvement
Background: Despite guideline-driven pharmacological therapies and careful transitional care, the rates of preventable hospital re-admission of heart failure patients and associated costs remain unacceptably high in the SNF populations. Transfer to SNF is one strategy to limit hospitalizations. As such, 25% of patients are still symptomatic at time of discharge.
Purpose: The objective of this study is to identify patient factors affecting re-admissions of HF patients residing in SNF within 30-days.
Methods: A retrospective electronic chart review was completed on patients >65 years with HF who were admitted into large medical center between 2012 and 2014. Descriptive statistics and univariate analyses using the chi-square test or Fisher’s exact test for categorical variables and the Mann-Whitney test for continuous data was used to compare patients readmitted within 30 days vs. those who were not readmitted within 30 days. Significant factors associated with readmission in the univariate analysis (p<0.10) were included for a multivariate logistic regression model. A receiver operating characteristic (ROC) curve was constructed to look at the final model’s ability to predict the outcome. A numerical measure of the accuracy of the model was obtained from the area under the curve (AUC), where an area of 1.0 signifies near perfect accuracy. The analysis of LOS was accomplished by applying standard methods of survival analysis, i.e., computing the Kaplan-Meier product limit curves, where the data were stratified by readmission within 30 days (Yes vs. No). No data were considered ‘censored’. The groups were compared using the log-rank test. The median rates for each group were obtained from the Kaplan-Meier/Product-Limit Estimates and their corresponding 95% confidence intervals were computed, using Greenwood’s formula to calculate the standard error. Unless otherwise specified, a result was considered statistically significant at the p<0.05 level of significance.
Results: Fifteen variables: creatinine, weight difference, CKD, Angina, Arrhythmia, VHD, Tobacco, ADL, independent in bathing, independent in the toilet, S3 Heart sounds present, HJR, AF, Nitrates, and Hydralazine, were identified for the multivariate logistic regression as potential risk factors associated with “readmission within 30 days”. Based on 23 readmissions within 30 days, our final model included only 2 predictor variables. Creatinine and ADLs were included in the final model as this subset of predictors was found to be the best for prediction of “readmission within 30 days”. Creatinine (p<0.0087) and ADLs (p<0.0077) were both significantly associated with readmission within 30 days in the final logistic regression model. Every 1-unit increase in creatinine is associated with an 87% increase in the odds of being readmitted within 30 days (OR = 1.87). Those patients who require assistance with ADLs are over 9 times more likely to be readmitted within 30 days (OR=9.25) as compared to patients who are independent.