EBM Tools for Practice: Using EHR-Based Clinical Information Systems to Improve Detection and Treatment of FH

Historically, familial hypercholesterolemia (FH), remains an underdiagnosed and undertreated disease. It is estimated that 90% of individuals with FH remain undiagnosed in the United States.(1) FH patients have ten to twenty-fold higher risk of cardiovascular disease. However, cardiovascular risk in this subgroup of patients does not differ significantly from the general population with appropriate early diagnosis and initiation of treatment. (2) Electronic health record (EHR) tools can improve detection, leading to early initiation of treatment of FH and thereby significantly improving mortality. Although important, an EHR tool does not by itself diagnose FH. However, it can help identify patients meeting diagnostic criteria of FH and enable appropriate care.(3) As such, this tool should be available in every health system. In addition, Patel et al. demonstrated that it is possible to identify patients within an integrated health system with probable FH and analyze the major adverse cardiovascular events, mortality, and cost of medical care in this subset of population.(4) In a 1.18 million EHR- eligible cohort, using International Classification of Diseases, Ninth Revision (ICD -9) code, hyperlipidemia was categorized into FH and non- FH groups. An EHR algorithm was designed using the modified Dutch Lipid Clinic Network criteria. FH was identified in 32,613 individuals, which was 2.7% of the entire cohort and 13.7% of patients with hyperlipidemia. FH had higher rates of myocardial infarction (14.77% versus 8.33%; P<0.0001) and heart failure (11.82% versus 10.50%; P<0.0001) after adjusting for traditional risk factors. FH also significantly correlated with a worse composite major adverse cardiovascular event (odds ratio, 4.02; 95% CI, 3.88-4.16; P<0.0001), mortality (odds ratio, 1.20; CI, 1.15-1.26; P<0.0001), and higher total cost per-year (incidence rate ratio, 1.30; 95% CI, 1.28-1.33; P<0.0001). The study demonstrated how an EHR tool identified disproportionately high prevalence of FH in the medical cohort and found that it was associated with worse outcomes and higher costs of medical care. This EHR tool can thus empower health systems to identify and target patients with FH for better outcomes.

The FH Foundation, seeking a method for a large scale screening, saw the benefits from using EHR tools from integrated healthcare system studies. This led to the FIND FH initiative using an application of machine learning to EHR encounter data, aimed to accelerate early diagnosis and timely intervention for undiagnosed patients with FH.(5) This study was the first to use EHR-based tools from four large healthcare systems across the country. The developed tool flagged 1.3 million people from a database of several other integrated healthcare systems for follow up of probable FH. 

The EHR algorithm included procedure and diagnostic codes (requires at least one cardiovascular disease risk factor: hypertension, hypercholesterolemia, or hyperlipidemia), prescriptions, and laboratory findings (Figure 1). The model was then applied to a national healthcare encounter database (170 million individuals) and an integrated healthcare delivery system dataset (174,000 individuals). Using a model with a measured precision (positive predictive value) of 0·85, recall (sensitivity) of 0·45, area under the precision–recall curve of 0·55, and area under the receiver operating characteristic curve of 0·89, 1,331,759 of 170,416,201 patients in the national database and 866 of 173,733 individuals in the health-care delivery system dataset as likely to have FH. 

FH experts reviewed a sample of flagged individuals (45 from the national database and 103 from the health-care delivery system dataset) and applied clinical FH diagnostic criteria. Of those reviewed, 87% (95% Cl 73–100) in the national database and 77% (68–86) in the health-care delivery system dataset were categorized as having a high enough clinical suspicion of familial hypercholesterolemia to warrant guideline-based clinical evaluation and treatment. The FH Foundation also developed a HIPAA-compliant outreach process to notify participating providers of their patients with probable FH in their practice. Because of the validation of the automated process, this tool can be applied to scale across diverse healthcare systems.

With the high burden of cardiovascular morbidity and mortality associated with FH, EHR-based tools along with a clinical decision support tool are key for early identification and treatment initiation. The EHR can offer an opportunity to find FH more efficiently, and enable cascade screening. We can strive to identify the 90% of FH patients who remain undiagnosed and help them achieve normal CV outcomes. 

Disclosure statement: Ms. deRichemond has no financial disclosures to report. Dr. Khalil has no financial disclosures to report. References are listed on page 31.

References:

1. Gidding SS Champagne MAde Ferranti SD, et al.The agenda for familial hypercholesterolemia: a scientific statement from the American Heart Association. Circulation. 2015;132: 2167- 2192
2. Duell, P. Barton et al. Longitudinal low density lipoprotein cholesterol goal achievement and cardiovascular outcomes among adult patients with familial hypercholesterolemia: the CASCADE FH registry. Atherosclerosis. 2019;289:85-93.
3. Abul-Husn NS, Manickam K, Jones LK, Wright EA, Hartzel DN, Gonzaga-Jauregui C, O’Dushlaine C, Leader JB, Lester Kirchner H, Lindbuchler DM, Barr ML, Giovanni MA, Ritchie MD, Overton JD, Reid JG, Metpally RP, Wardeh AH, Borecki IB, Yancopoulos GD, Baras A, Shuldiner AR, Gottesman O, Ledbetter DH, Carey DJ, Dewey FE, Murray MF. Genetic identification of familial hypercholesterolemia within a single U.S. healthcare system. Science. 2016;354:aaf7000. -
4. Patel P, Hu Y, Kolinovsky A, et al. Hidden Burden of Electronic Health Record-Identified Familial Hypercholesterolemia: Clinical Outcomes and Cost of Medical Care. J Am Heart Assoc. 2019;8(13):e011822. doi:10.1161/JAHA.118.011822
5. Kelly D Myers et al. Precision screening for familial hypercholesterolemia: a machine learning study applied to electronic health encounter data, The Lancet Digital Health (2019). DOI: 10.1016/S2589-7500(19)30150-5