DCRI
Durham, North Carolina, 27705, United States
NCT Number: NCT03911284
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.
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Observational
Durham, North Carolina, 27705, United States
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,
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.
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
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
Time frame: September 2018-March 2020
PERCENTAGE OF ELIGIBLE ADULTS ON GUIDELINE-RECOMMENDED SECONDARY PREVENTION THERAPY
Time frame: September 2018-March 2020
PERCENTAGE OF ADULTS MEETING BLOOD PRESSURE GOALS
Time frame: September 2018- March 2020
PERCENTAGE OF ADULTS ON STATIN THERAPY
Time frame: September 2018-March2020
PERCENTAGE OF ADULTS MEETING LDL-C GOALS
Time frame: May 2019-March 2020
WE WILL ASSESS THE POSITIVE PREDICTIVE VALUE OF EHR-BASED ALGORITHMS TO IDENTIFY STROKE
Time frame: May 2019-March 2020
WE WILL ASSESS THE POSITIVE PREDICTIVE VALUE OF EHR-BASED ALGORITHMS TO IDENTIFY TRANSIENT ISCHEMIC ATTACK
Time frame: May 2019-March 2020
WE WILL ASSESS THE POSITIVE PREDICTIVE VALUE OF EHR-BASED ALGORITHMS TO IDENTIFY MYOCARDIAL INFARCTION
Time frame: May 2019-March 2020
WE WILL ASSESS THE POSITIVE PREDICTIVE VALUE OF EHR-BASED ALGORITHMS TO IDENTIFY UNSTABLE ANGINA
Duke University
Other
The Learning Registry Creating the Learning Healthcare Platform of the Future: High Risk ASCVD Registry for Longitudinal Follow Up
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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