Author + information
- Prashant Patel,
- Amy Kolinovsky,
- Jeffrey Ruhl,
- Sarath Krishnamurthy,
- Dominik Beer,
- Lester Kirchner,
- Raghu Metpally,
- David Carey and
- Vishal Mehra
Background: In this study we utilize a population-based approach using electronic health record (EHR) based algorithms to identify familial hypercholesterolemia (FH), a traditionally underdiagnosed and undertreated condition. We report the trends of major adverse cardiovascular events(MACE) and mortality associated with this diagnosis.
Methods: In our 1.18 million EHR eligible cohort, International Classification of Disease (ICD)-defined hyperlipidemia was categorized into FH and non FH groups using a validated EHR algorithm designed using Dutch Lipid Clinic Network criteria. A priori associated variables/confounders were used for multivariate analyses using binary logistic regression. Kaplan-Meier survival curves were created.
Results: FH constituted 2.8% (32,609) of the entire EHR cohort and 12.6% of 258,045 patients with hyperlipidemia. FH had a higher incidence of CAD (26.2% vs 18% p<0.0001), MI (11% vs 5.6%, p<0.0001) and PAD (9.2% vs 5.6%, p <0.0001) and lower probability of survival (Figure 1). FH was a significant predictor of MACE (OR, CI, p-value: 2.13, 2.07-2.20, <0.01) or Death (1.24, 1.19-1.29, <0.01) after adjusting for traditional risk factors.
Conclusions: EHR based algorithms discovered a disproportionately high prevalence of FH in our medical cohort, which was associated with worse outcomes. This data-driven approach allows for a more precise method to identify traditionally high risk groups within large populations allowing for targeted prevention strategies.
Moderated Poster Contributions
Prevention Moderated Poster Theater, Poster Hall, Hall C
Sunday, March 19, 2017, 10:00 a.m.-10:10 a.m.
Session Title: Contemporary Issues in Familial Hypercholesterolemia
Abstract Category: 32. Prevention: Clinical
Presentation Number: 1304M-05
- 2017 American College of Cardiology Foundation