Title: Using Math-Physical Medicine to Study the Probability of Having a Heart Attack or Stroke Based on Combination of Metabolic Conditions, Lifestyle, and Metabolism Index
Biography: Gerald C. Hsu
Background and Aim:
The author has extended his 8-year T2D research along with ~1.5M data to examine the relationship among metabolic conditions, lifestyle, metabolism index, and the probability of having a heart attack or stroke.
Material and Method:
In 2014, he researched and built a metabolism model to measure the multiple interactions of four metabolic disease outputs and six lifestyle inputs.
Initially, he chose age, gender, race, family history, smoking, drinking, substance abuse, personal medical history, and waistline to establish a “static” baseline. He then applied the hemodynamics concept to develop a “dynamic” macro-simulated model for blood blockage and artery rupture.
He utilized 368,513 data which include 72,893 metabolic conditions (obesity, diabetes, hypertension, hyperlipidemia) and 295,620 lifestyle conditions (food, exercise, water, sleep, stress, daily life routine) within 2,274 days (1/2012-3/2018) to compute three different sets of risk probabilities separately. Finally, he integrated them into one overall risk probability. He also conducted sensitivity analyses to cover the probability variance by using different weighting factors (WF).
The risk probabilities are:
74% in 2000 (followed by three cardiac episodes 2001-2006)
From 69% in 2012 decrease to 26.4% in 2017 (compatible with 26.7% by Framingham Study)
WF sensitivity: +/- 10% to 18%
The mathematical simulation results are validated by past 17-years 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.