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(L to R: Sandy Joung; Janet Wei, MD; Qin Fu, PhD; Kelly N. Mouapi, PhD; Jennifer Van Eyk, PhD; Brennan Spiegel, MD; Noel C. Bairey Merz, MD; Shivani Dhawan, MS; Irene van den Broek, PhD; Gilhad Khanian)
Cardiovascular disease is the leading cause of death for both men and women in California.
Tragically, many people develop a heart attack, stroke or other complication of cardiovascular disease because they were under- treated, not taking their medicines, or not receiving the care they needed in the first place; this is especially common among younger women and racial/ethnic minorities.
One reason for this is that early signs of disease can be easily missed, and also because people spend most of their life far away from a doctor or hospital where it is challenging to monitor disease progression.
In this study, researchers will look for the earliest signs of impending disease by monitoring patients remotely, outside the four walls of the hospital or doctor’s office. Patients will wear a specialized watch that measures activity, sleep, heart rate, and stress levels.
They will also report their levels of anxiety, depression, and quality of life using a smartphone or computer.
Finally, they will periodically send a small finger prick blood sample by mail, allowing doctors to measure over 500 different blood chemicals.
By combining these different types of data, the researchers will seek a “signal in the noise” that predicts who may be about to have a heart attack or stroke.
If successful, patients could greatly benefit from more effective prevention and treatment as a result of earlier disease detection, but in order to broadly implement innovative new technologies, it is also important to understand their potential cost impact on the medical system.
The team will therefore perform an economic analysis to estimate the cost effectiveness of this remote monitoring approach.
The team is working to further the prediction capabilities for an impending heart attack or stroke. They will add several analyses and measurements, including: an assessment for genomic risk for heart disease, AI-based modelling of electrical activity of the patient’s heart, and the use of more frequently updated prediction scores. Additionally, the team will explore in more detail why patient compliance with remote data collection has been so high, and they will test an alternative home blood collection device to determine if it improves sample quality and patient adherence.