Using PCORnet to Understand PCSK9 Inhibitor Use among Patients at Risk for Heart Disease

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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.

What was the project about?

Heart disease is the leading cause of death in the United States, causing about one in three deaths. Having high cholesterol levels raises a person’s risk of getting heart disease. Statins are the medicine used most often to lower cholesterol. Medicines called PCSK9 inhibitors became available recently. These medicines lower cholesterol but are not yet used widely.

PCORnet created a shared database system that includes information about test results and treatments from patients’ electronic health records, or EHRs. This shared data system includes information from 11 Clinical Data Research Networks that are part of PCORnet. In this project, the team looked at what information was available about these tests and treatments in the database. They wanted to learn whether the database had enough information to be useful for answering patient-centered questions about treatment to lower cholesterol. Patient-centered research respects patient preferences, needs, and values. When research is patient-centered, the patient’s values guide all healthcare decisions.

What were the results?

What types of patients were at risk for heart disease?
The project team looked at records for 4,081,535 patients. Among these patients, 74 percent were white, 11 percent were black, 3 percent were Asian, 1 percent were American Indian or Alaska Native, 0.2 percent were Native Hawaiian or other Pacific Islander, and 11 percent were of unknown ethnicity. The average age was 62. Women made up 51 percent of patients. Also, 30 percent were overweight, and 42 percent had obesity. High blood pressure affected 63 percent of patients, and 30 percent had diabetes.

How many patients who were eligible for medicines to lower cholesterol used these medicines?
The project team separated patients into three risk groups for heart disease. These groups included patients who had

• High cholesterol
• High cholesterol but who did not take medicines
• One of two forms of heart disease, called coronary heart disease, or CHD, or coronary artery disease, or CAD

Just over half of patient records had information about medicines to lower cholesterol. The project team looked at records that included information about medicines. More than half of patients with high cholesterol, CHD, or CAD used medicines to lower cholesterol. Patients with CHD or CAD had the highest use of PCSK9 inhibitors.

Which healthcare providers prescribed PCSK9 inhibitors?
About half of the prescriptions for PCSK9 inhibitors were missing information on who prescribed the medicines. Cardiologists, or heart doctors, wrote most of the remaining prescriptions.

How did prescriptions for PCSK9 inhibitors change over time?
Healthcare providers wrote more prescriptions for PCSK9 inhibitors over time for patients with CHD or CAD than for patients in the other groups. Prescriptions remained low and did not change over time for patients with high cholesterol or a family history of high cholesterol.

What did the project team do?

The project team looked at EHR data from 18 health systems. The team linked information from each system’s records using the PCORnet common data model (CDM). The CDM organizes information into a standard format for researchers to use. The team was interested in patients who received care at the health systems between January 2015 and March 2017.

The project included 4,081,535 patients who fit into one of three risk groups for heart disease. The team collected information about patients’ health, health problems, risk factors for heart disease, prescriptions, and whether patients used medicines to lower cholesterol.

What were the limits of the project?

Some of the database’s information was not specific enough to answer the project’s questions. About half of PCSK9 inhibitor prescriptions did not include who wrote the prescription. The project team did not look at patient records that were missing certain types of information. These records could have added valuable information.

How can people use the results?

Research teams can use these results when planning studies that use the database.

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.

Objective

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

Project Design

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.

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.

Limitations

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 Needs

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.