The Learning Registry

The Learning Registry Creating the Learning Healthcare Platform of the Future: High Risk ASCVD Registry for Longitudinal Follow Up

The Learning Registry is a retrospective, exempt study. Researchers form the Duke Clinical Research Institute (DCRI) will utilize de-identified data managed by Cerner for population health analytics as part of a ongoing registry of patients with atherosclerotic cardiovascular disease.

Cerner is an electronic health record company utilized by a large number of health systems in the United States. As part of their services to the health systems that they work with, they have created platform for population health management called HealtheIntent. HealtheIntent uses individual data from patients at a health system collected through the EMR as well as other data streams in the health system (i.e. cost data), aggregates the data, and stores it on an Amazon Web Services cloud, accessible to both Cerner and the health systems, to perform large scale population health analytics. These data may be linked as well by Cerner to the National Death Index or other data sources depending on the individual relationship with the sites.

For this retrospective study, the Study Start Date is the date contracts were executed; Primary Completion Date is the date the final dataset is available for analysis and manuscript development; Study Completion Date is the date the study is completed. Enrollment is the number of patient charts reviewed.

Study Overview

• Status: Completed
• Conditions: Atherosclerotic Cardiovascular Disease

Detailed Description

DCRI will utilize data on patients with ASCVD from the Cerner's health systems who have agreed to participate in the Learning Registry. Cerner will de-identify the data and place it into a cloud-based server hosted by Amazon Web Services that is password-protected and accessible only to DCRI researchers.

The research goals for the Learning Registry are,

1. Evaluate treatment patterns and gaps in care for patients with atherosclerotic cardiovascular disease.

   Across adults with ASCVD, treatment patterns for those groups will be evaluated, including aspirin, beta blockers, ace inhibitors, P2Y12 inhibitors, statins, and other antithrombotics. Factors associated with appropriate therapy use will be assessed, including patient level factors (age, race, sex, insurance status), clinical factors (type of ASCVD, comorbidities including kidney disease, atrial fibrillation, hypertension, diabetes, duration since last event), and system level factors (type of provider seen, frequency of contact with the healthcare system). Stratified analyses will include, but is not limited to, adults with PAD and adults who would have been eligible for the COMPASS trial.

2. Estimate the attributable risk of modifiable risk factors on recurrent cardiovascular events in adults with established ASCVD.

Patients who present with myocardial infarction, unstable angina, stroke, and TIA will be identified. Those who have been previously seen in the outpatient setting will be evaluated for the presence of pre-existing cardiovascular disease to identify those with CVD prior to their event. Risk factor control prior to the event will be assessed. The amount of disease potentially preventable by risk factor control, as well as utilization of novel secondary preventive therapies including rivaroxaban will be evaluated.

• Study Type: Observational
• Enrollment (Actual): 2000

Contacts and Locations

Study Locations

• United States
  • North Carolina
    • Durham, North Carolina, United States, 27705
      • DCRI

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

patients with atherosclerotic cardiovascular disease

Description

Inclusion Criteria:

• Adults 18 and older For the overall ASCVD population, diagnose codes for cardiopulmonary disease, including disease of the pulmonary circulation, cardiac disease, and vascular disease will be used to identify the population to be de-identified. At MU, this will include patients back to 2012, and at Seton this will include patients back to 2014, which reflect dates of data availability at each health system. For both institutions, data collection for patients identified will stop at the end of 2019.

For the chart review population, patients with hospitalization for cerebrovascular disease (e.g. stroke/TIA), coronary heart disease (i.e. myocardial infarction), and peripheral vascular disease (e.g. limb ischemia) will be eligible.

Exclusion Criteria:

• none

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Retrospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Utilization of guideline-directed medical therapy in adults with ASCVD	PERCENTAGE OF ELIGIBLE ADULTS ON GUIDELINE-RECOMMENDED SECONDARY PREVENTION THERAPY	September 2018-March 2020
Utilization of guideline-directed medical therapy in adults with ASCVD	PERCENTAGE OF ADULTS MEETING BLOOD PRESSURE GOALS	September 2018-March 2020
Utilization of guideline-directed medical therapy in adults with ASCVD	PERCENTAGE OF ADULTS ON STATIN THERAPY	September 2018- March 2020
Utilization of guideline-directed medical therapy in adults with ASCVD	PERCENTAGE OF ADULTS MEETING LDL-C GOALS	September 2018-March2020

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of EHR data to identify cardiovascular events and comorbidities.	WE WILL ASSESS THE POSITIVE PREDICTIVE VALUE OF EHR-BASED ALGORITHMS TO IDENTIFY STROKE	May 2019-March 2020
Accuracy of EHR data to identify cardiovascular events and comorbidities.	WE WILL ASSESS THE POSITIVE PREDICTIVE VALUE OF EHR-BASED ALGORITHMS TO IDENTIFY TRANSIENT ISCHEMIC ATTACK	May 2019-March 2020
Accuracy of EHR data to identify cardiovascular events and comorbidities.	WE WILL ASSESS THE POSITIVE PREDICTIVE VALUE OF EHR-BASED ALGORITHMS TO IDENTIFY MYOCARDIAL INFARCTION	May 2019-March 2020
Accuracy of EHR data to identify cardiovascular events and comorbidities.	WE WILL ASSESS THE POSITIVE PREDICTIVE VALUE OF EHR-BASED ALGORITHMS TO IDENTIFY UNSTABLE ANGINA	May 2019-March 2020

Collaborators and Investigators

• Sponsor: Duke University
• Collaborators: Janssen Scientific Affairs, LLC
• Investigators: Principal Investigator: Nishant Shah, MD, Duke University

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2018
• Primary Completion (Actual): December 31, 2022
• Study Completion (Actual): December 31, 2022

Study Registration Dates

• First Submitted: August 29, 2018
• First Submitted That Met QC Criteria: April 8, 2019
• First Posted (Actual): April 11, 2019

Study Record Updates

• Last Update Posted (Actual): January 20, 2023
• Last Update Submitted That Met QC Criteria: January 19, 2023
• Last Verified: December 1, 2022

More Information

Terms related to this study

• Keywords: ASCVD
• Additional Relevant MeSH Terms: Vascular Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Cardiovascular Diseases; Atherosclerosis
• Other Study ID Numbers: Pro00094697

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No