Clinical trial NCT04574882: Using Digital Data to Predict CHD
Official title: Using Digital Data to Predict Cardiovascular Health and Health Care Utilization
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Cardiovascular Diseases
Keywords: digital data
digital health
Lead sponsor: University of Pennsylvania
Sponsor class: OTHER
First posted: 2020-10-05
Enrollment: 780
Sex: ALL
Minimum age (years): 30
Maximum age (years): 74
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: We will identify patients ages 30-74 with and without CHD (ICD 9:414.0, ICD 10: I63, I20-I25)

Brief summary: This project seeks to identify and characterize features derived from digital data (e.g. social media, online search, mobile media) which are associated with coronary heart disease (CHD) and related risk factors, and develop models that use digital data and conventional predictive models to predict CHD risk and health care utilization.

Detailed description: Cardiovascular disease is the leading cause of death in the US. While secondary prevention approaches have improved longevity of patients, risk factors and adverse health behaviors (e.g., physical inactivity, smoking) are highly prevalent, and in most contemporary series, less than 1% of adults meet all factors of ideal CV health. The logistics and practicalities of meeting the goal of ideal CV health have not been clearly elucidated. Practice guidelines recommend using the Framingham risk score (FRS) or other risk prediction tools to classify patients' risk of CV disease. These models however are imprecise and there is increasing focus on identifying markers that provide better measures of risk. As digital platforms are increasingly used to document lifestyle and health behaviors, data from digital sources may provide a window into manifestations of novel risk factors and potentially a better characterization of existing risk factors. While it seems like a cliche to mention the profound impact of digital data on everyday lives, there is indeed great substance in the opportunities these new media provide for understanding behavioral, social, and environmental determinants of health. This project seeks to identify and characterize features derived from digital data (e.g. social media, online search, mobile media) which are associated with coronary heart disease (CHD) and related risk factors, and develop models that use digital data and conventional predictive models to predict CHD risk and health care utilization.

Eligibility criteria: Inclusion Criteria:

* 30 - 74 years of age
* Willing to sign informed consent
* Primarily English speaking (for language analysis)
* Has an account on any of the following digital data platforms (Facebook, Instagram, Twitter Reddit, Google (gmail), or smartphone or wearable device such as Apple Health, Fitbit, Samsung Health, MapMyFitness or Garmin) and willing to share data
* If has social media account, Instagram or Facebook, willing to share historical and prospective data (60 days) If has Google (gmail) account, willing to download and share google takeout zip file
* If has smartphone or wearable device, willing to share step data
* Willing to share access to medical health records
* Willing to share healthcare insurance information

Exclusion Criteria:

* Patient does not meet age inclusion criteria above
* Does not use and post on digital data sources we are studying or unwilling to donate data
* Patient is in severe distress, e.g. respiratory, physical, or emotional distress
* Patient is intoxicated, unconscious, or unable to appropriately respond to questions

Primary outcomes:
measure: Latent Dirichlet Allocation Topics - topics / themes discussed between patients with and without heart disease
timeFrame: Through study completion, an average of 3 years
Secondary outcomes:
Other outcomes:
measure: CHD event
timeFrame: Through study completion, an average of 3 years
measure: Health care utilization
timeFrame: Through study completion, an average of 3 years
Locations:
city: Philadelphia
country: United States
facility: University of Pennsylvania Health System
state: Pennsylvania
Study officials:
References: