PCORI funded the development of PCORnet®, the National Patient-Centered Clinical Research Network, to make research faster, easier, and less costly to conduct. PCORnet is made up of Partner Networks of healthcare systems, patients and communities, and health plans that harness the power of large amounts of health data.
PCORI supports brief, descriptive projects to assess the feasibility of conducting research using data gathered and shared securely through PCORnet. This project is one of several designed to test the network while addressing priorities identified by PCORI and its stakeholders.
To test the potential of using PCORnet data to categorize patients at risk for heart disease, identify their use of lipid-lowering therapies, and describe types of providers who prescribe these medications
|Retrospective descriptive analysis
|Data Sources and Data Sets
|Records of patients eligible for lipid-lowering therapies who received care between 2015 and 2017 from 1 of 18 health systems belonging to 7 PCORnet Partner Networks
|Review of EHRs, including diagnosis codes, lab data, and prescription data
- Heart disease risk groups
- Prevalence of lipid-lowering therapy use among patients at risk for heart disease
- Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor prescribers and prescribing trends
The project team started with electronic health record (EHR) data (ICD-9/10-CM codes and lab data) for 17,520,612 patients. The team narrowed this sample to 4,081,535 patients who were at risk for heart disease. These patients received care between January 2015 and March 2017 from 1 of 18 health systems belonging to seven PCORnet Partner Networks. The project team linked data from each network using the PCORnet common data model (CDM). The CDM organizes data into a standard structure for researchers’ use.
Patients met criteria for one of three heart disease risk groups. The groups from lowest to highest risk were
- Having dyslipidemia
- Having LDL cholesterol ≥130 mg/dL and not being on lipid-lowering treatment
- Having coronary heart disease or coronary artery disease (CHD or CAD)
To achieve the project’s aims, the project team used EHR data on demographics, comorbid conditions, weight, height, blood pressure, and smoking status. They also used information about treatment, including encounters, diagnoses, procedures, prescribing, dispensing, and lab results.
Of patients who met criteria for one of the three risk groups, 55% had dyslipidemia, 11% had LDL cholesterol ≥130 mg/dL and were not on any lipid-lowering treatment, and 23% had CHD or CAD.
Among patients, 74% were white, 11% were black, 3% were Asian, 1% were American Indian or Alaska Native, 0.2% were Native Hawaiian or Pacific Islander, and 11% were of unknown ethnicity. Women accounted for 51% of patients. The average age was 62; 30% of patients were overweight and 42% had obesity. Also, 63% had hypertension and 30% had diabetes.
The project team used EHR data to calculate 10-year atherosclerotic cardiovascular disease risk (ASCVD) for each risk group when the necessary demographics, vitals, and lab results were available:
- Dyslipidemia: 11.5%
- LDL cholesterol ≥130 mg/dL and untreated: 6.1%
- Patients with CHD or CAD have heart disease and therefore did not have 10-year ASCVD risk scores
Information about use of lipid-lowering medication was available for 51% of patients in the three risk groups. Among these patients, more than half with dyslipidemia (51%) and CHD or CAD (59%) used some type of lipid-lowering medication. Patients with CHD or CAD were most likely to use a PCSK9 inhibitor. These patients also used other heart disease medicines such as aspirin, angiotensin converting enzyme (ACE) inhibitors, calcium channel blockers, and beta blockers more often than patients in other risk categories.
The project team could not categorize the provider type for 52% of PCSK9 inhibitor prescriptions because of missing information. The most frequent prescriber type was cardiologist.
PCSK9 inhibitor prescriptions increased between 2015 and 2017 for patients with CHD or CAD. PCSK9 inhibitor prescribing remained low and stable over time for patients with dyslipidemia or familial hypercholesterolemia.
Aggregate-level data, rather than raw data, were available from the different Partner Networks. Raw data could have clarified differences in how sites documented certain data, such as lab results. Encounter-level data could have allowed the team to study changes in individual patients’ medication over time.
Approximately half of PCSK9 inhibitor prescriptions were missing information on provider type because some health systems did not include this field as part of the medical record. Some records were missing medication history or laboratory records; these missing data could have added important information.
Conclusions and Relevance
The project team was able to identify patients in three heart disease risk groups and characterize each group regarding demographics, comorbid conditions, cardiovascular risk factors, and medication use. The team identified low PCSK9 inhibitor use among patients receiving treatment and found evidence that cardiologists represented the largest share of PCSK9 inhibitor prescriptions.
Future research could pursue ways to obtain complete data, such as incorporating crosswalks to provider type, capturing raw patient- or encounter-level data, and capturing information on patients with no medication history or laboratory records.