News: Researchers use population health data to determine cardiovascular disease risk
A recent study in the Journal of the American Medical Informatics Association found that using population health data could help identify patients with a high risk of developing cardiovascular disease even among those not previously identified due to lack of relevant cholesterol data. The study used calculations derived from other data within electronic health records (EHR) which primary care doctors then used to proactively engage patients, Revenue Cycle Advisor reported.
Researchers from the University of North Carolina (UNC) School of Medicine and the UNC Gillings School of Global Public Health formed a network of 219 primary care clinics across North Carolina (from urban, suburban, and rural areas) to examine 345,440 patients age 40–79. Of that pool, 108,515 did not have cholesterol scores (total cholesterol and HDL levels)—one of the factors used to determine precise cardiovascular risk—in their EHRs.
The researchers performed two separate analyses by using the data from the patients who had cholesterol numbers, along with other factors, for determining cardiovascular disease risk, such as smoking status, weight, diabetes, gender, etc., according to Revenue Cycle Advisor.
Researchers then created an equation to estimate cholesterol levels for patients with missing data and used it to calculate atherosclerotic cardiovascular disease (ASCVD) risk scores. Using this estimation method, the researchers determined some 43,205 patients were at a greater than 10%, 10-year ASCVD risk and were eligible for risk reduction interventions, such as aspirin or statins. These patients had not been previously identified as being high-risk.
The researchers also ran a simpler analysis by plugging in conservative cholesterol numbers for the patients lacking data. Even with this conservative estimate, 40,565 patients (37% of those lacking cholesterol data) were found to have risk scores greater than or equal to 10%.
The researchers concluded that the first estimation method had a higher false-positive rate than the conservative one but using either analysis strategy still accurately identified high-risk patients who could benefit from intervention.