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J Am Med Inform Assoc. 2019 Feb 1;26(2):155-158. doi: 10.1093/jamia/ocy151.

A population approach using cholesterol imputation to identify adults with high cardiovascular risk: a report from AHRQ's EvidenceNow initiative.

Author information

1
The Division of General Medicine and Clinical Epidemiology and the Cecil G. Sheps Center for Health Services Research, the University of North Carolina, Chapel Hill, North Carolina, USA.
2
Department of Global Public Health, School of Public Health, University of Washington, Seattle, Washington, USA.
3
The Department of Medicine, The Dell Medical School, University of Texas, Austin, Texas, USA.
4
The Department of Biostatistics, The Gillings School of Global Public Health, the University of North Carolina, Chapel Hill, North Carolina, USA.

Abstract

Large practice networks have access to EHR data that can be used to drive important improvements in population health. However, missing data often limit improvement efforts. Our goal was to determine the proportion of patients in a cohort of small primary care practices who lacked cholesterol data to calculate ASCVD risk scores and then gauge the extent that imputation can accurately identify individuals already at high risk. 219 practices enrolled. Patients between the ages of 40 and 79 years qualified for risk calculation. For patients who lacked cholesterol data, we measured the effect of employing a conservative estimation strategy using a total cholesterol of 170 mg/dl and HDL-cholesterol of 50 mg/dl in the ASCVD risk equation to identify patients with ≥ 10%, 10-year ASCVD risk who were eligible for risk reduction interventions then compared this to a rigorous formal imputation methodology. 345 440 patients, average age 58 years, qualified for risk scores. 108 515 patients were missing cholesterol information. Using the "good value" estimation methodology, 40 565 had risk scores ≥ 10% compared to 43 205 using formal imputation. However, the latter strategy yielded a lower specificity and higher false positive rate. Estimates using either strategy achieved ASCVD risk stratification quickly and accurately identified high risk patients who could benefit from intervention.

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