Optimization of lipid-lowering therapy for high cardiovascular risk patients through electronic medical record reporting and pharmacist evaluation

Kaylee Chen, Scott Canfield, Joshua Blackwell, Samuel Houmes, Steven Jones, John Lindsley

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Identification of high cardiovascular risk patients on suboptimal lipid-lowering therapy (LLT) may be possible through electronic medical record (EMR) reporting, presenting an opportunity for pharmacist involvement in optimizing drug regimens. OBJECTIVES: To (a) identify high cardiovascular risk patients with opportunities for LLT optimization through EMR reporting and (b) evaluate effectiveness of pharmacist review and treatment algorithm on recommending treatment modifications compared with algorithm application alone. METHODS: We generated an EMR report to identify adult patients aged 21-75 years with clinical atherosclerotic cardiovascular disease and low-density lipoprotein cholesterol (LDL-C) level ≥70 mg/dL during a 6-month period and collected pertinent data elements. We selected a subgroup of patients for remote pharmacist review and determined recommendations based on our predefined LLT optimization algorithm and pharmacist clinical judgment. One pharmacist was responsible for making all recommendations and communicated potential treatment modification to primary care providers via email and/or EMR messaging. We tracked provider acceptable/rejection rate to all recommendations made. We also compared recommendations based on using the algorithm alone to combining pharmacist chart review and algorithm and examined reasons for any discrepancies. RESULTS: 941 patients met inclusion criteria, with 399 patients (42.4%) not currently on any LLT. At baseline, 249 patients (25.3%) were on a high-intensity statin, and 19 (1.9%) were on a proprotein convertase subtilisin/ kexin type 9 inhibitor. A subgroup of 34 patients were reviewed, of which 30 (88.2%) were on suboptimal therapy despite not achieving LDL-C goals. The pharmacist recommended to intensify statin therapy for 16 patients (47.1%), initiate nonstatin therapy for 9 patients (26.5%), and initiate statin therapy in 5 patients (14.7%). Pharmacist recommendation acceptance rate was 53.3%, with no response received in 26.6% of cases. The algorithm evaluation alone yielded the same recommendation as the combined pharmacist review with algorithm in 30 (88.2%) of the cases and differed in 4 cases. CONCLUSIONS: The underutilization of LLT among high cardiovascular risk patients remains a growing issue despite effective treatment options with cardiovascular benefits. Pharmacists may be able to identify these patients by using reportable EMR data elements and applying a treatment optimization algorithm to make therapy recommendations and improve outcomes.

Original languageEnglish (US)
Pages (from-to)1010-1016
Number of pages7
JournalJournal of Managed Care and Specialty Pharmacy
Volume26
Issue number8
DOIs
StatePublished - Aug 1 2020

ASJC Scopus subject areas

  • Pharmacy
  • Pharmaceutical Science
  • Health Policy

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