Abstract 13737: An Electronic Health Record-Based Algorithm to Optimize Secondary Prevention Using Lipoprotein(a), Apolipoprotein B, Cholesterol, and Myocardial Infarction Diagnosis: ABCDs Prevention Program

Abstract

Background : According to the 2022 American Heart Association Heart Disease and Stroke Statistics, coronary heart disease remains the leading cause of death attributable to cardiovascular disease (CVD). In 2016 only ~1 in 4 Medicare beneficiaries who were eligible for cardiac rehab (CR) participated. We explored the use of cardiovascular biomarkers and electronic health records (EHRs) to facilitate the early identification of patients at high risk for CVD. Methods : The EHR algorithm uses a combination of cardiovascular biomarkers and ICD diagnosis codes to automatically identify and refer patients to select secondary prevention programs: (1) traditional CR, (2) lipid clinic, (3) virtual CR (Corrie Health Program). In order to determine program eligibility our model uses criteria of lipoprotein(a) >70 nmol/L, apolipoprotein B >90 mg/dL, low-density lipoprotein cholesterol >150 mg/dL, triglycerides >200 mg/dL , and/or a diagnosis of ST-elevation myocardial infarction (MI) or non-ST-elevation MI as depicted in Figure 1. Lipid biomarker cutoffs were defined to maximize sensitivity for familial hyperlipidemia. Results : In a test environment 90% of patients deemed eligible by the algorithm received appropriate referrals to traditional, virtual (Corrie) CR and lipid clinics. The algorithm is currently being used in both inpatient units and outpatient clinics at Johns Hopkins Hospital and Johns Hopkins Bayview. Sixty-six percent of the orders placed increased referrals to CR and lipid clinic and 33% of the orders placed increased referrals to virtual cardiac rehab. Conclusions : Building an EHR-based algorithm to individualize CVD prevention using cardiovascular biomarkers and diagnoses may increase early identification and intervention on high-risk patients.