Try the modernized ClinicalTrials.gov beta website. Learn more about the modernization effort.
Working…
ClinicalTrials.gov
ClinicalTrials.gov Menu

Using Digital Data to Predict CHD

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04574882
Recruitment Status : Recruiting
First Posted : October 5, 2020
Last Update Posted : January 11, 2022
Sponsor:
Information provided by (Responsible Party):
University of Pennsylvania

Study Description
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
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.

Condition or disease Intervention/treatment
Cardiovascular Diseases Other: Survey

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.
Study Design
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Layout table for study information
Study Type : Observational
Estimated Enrollment : 1000 participants
Observational Model: Case-Control
Time Perspective: Cross-Sectional
Official Title: Using Digital Data to Predict Cardiovascular Health and Health Care Utilization
Actual Study Start Date : September 25, 2020
Estimated Primary Completion Date : September 30, 2023
Estimated Study Completion Date : December 14, 2023
Groups and Cohorts
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Group/Cohort Intervention/treatment
Case
Patients ages 40-74 with and without CHD (IICD 10: I63, I20-I25 ) within the last 6 months who receive care in the University of Pennsylvania Health System (UPHS).
 Other: Survey
Interested participants may complete the informed consent online. After informed consent, the participant will be asked to share the digital data types that they use (Facebook, Instagram, Twitter, Google search, step data) and then participants will complete a cross-sectional survey.
Control
Patients aged 40-74 who have non-cardiovascular-related chief compliant.
 Other: Survey
Interested participants may complete the informed consent online. After informed consent, the participant will be asked to share the digital data types that they use (Facebook, Instagram, Twitter, Google search, step data) and then participants will complete a cross-sectional survey.


Outcome Measures
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Primary Outcome Measures :
  1. Latent Dirichlet Allocation Topics - topics / themes discussed between patients with and without heart disease [ Time Frame: Through study completion, an average of 3 years ]
    The primary outcome is topics and features (derived using the Latent Dirichlet Allocation [LDA] method for clustering language data).


Other Outcome Measures:
  1. CHD event [ Time Frame: Through study completion, an average of 3 years ]

    Reliability in predicting CHD related event in patient as measured by Framingham Risk Score.

    The Framingham Risk Score (FRS) is a validated means of predicting cardiovascular disease (CVD) risk. Input variables include age, cigarette smoking, total cholesterol, HDL cholesterol, systolic blood pressure measurement and treatment for hypertension. Point values are calculated based on each of these risks. A 10-year risk score can be derived as a percentage. Risk scores range from 0-20%.

    Low Risk: Less than 10% risk that you will develop a heart attack or die from coronary disease in the next 10 years.

    Intermediate risk: A 10 to 20% risk that you will develop a heart attack or die from coronary disease in the next 10 years.

    High Risk: A greater than 20% risk that you will develop a heart attack or die from coronary disease in the next 10 years.


  2. Health care utilization [ Time Frame: Through study completion, an average of 3 years ]
    Prediction of cost for health care utilization between heart disease and non- heart disease subjects measured by insurance claims data


Eligibility Criteria
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   40 Years to 74 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
We will identify patients ages 40-74 with and without CHD (ICD 9:414.0, ICD 10: I63, I20-I25) who receive care in the University of Pennsylvania Health System.
Criteria

Inclusion Criteria:

  • 40 - 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
Contacts and Locations
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04574882


Locations
Layout table for location information
United States, Pennsylvania
University of Pennsylvania Health System Recruiting
Philadelphia, Pennsylvania, United States, 19101
Contact: Rachelle Schneider    484-723-3171    Rachelle.Schneider@pennmedicine.upenn.edu   
Sponsors and Collaborators
University of Pennsylvania
More Information
Go to 
Top of Page Study Description Study Design Groups and Cohorts Outcome Measures Eligibility Criteria Contacts and Locations More Information
Layout table for additonal information
Responsible Party: University of Pennsylvania
ClinicalTrials.gov Identifier: NCT04574882    
Other Study ID Numbers: 833699
First Posted: October 5, 2020    Key Record Dates
Last Update Posted: January 11, 2022
Last Verified: January 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by University of Pennsylvania:
digital data
digital health
Additional relevant MeSH terms:
Layout table for MeSH terms
Cardiovascular Diseases