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Optimisation of lipids for prevention of cardiovascular disease in a primary care
  1. Smita Bakhai,
  2. Aishwarya Bhardwaj,
  3. Parteet Sandhu,
  4. Jessica L. Reynolds
  1. Internal Medicine, University at Buffalo, the State University of New York, Buffalo, New York, USA
  1. Correspondence to Dr Smita Bakhai; sybakhai{at}buffalo.edu

Abstract

The 2013 American College of Cardiology/American Heart Association (ACC/AHA) guidelines focus on atherosclerotic cardiovascular disease (ASCVD) risk reduction, using a Pooled Cohort Equation to calculate a patient’s 10-year risk score, which is used to guide initiation of statin therapy. We identified a gap of evidence-based treatment for hyperlipidaemia in the Internal Medicine Clinic. Therefore, the aim of this study was to increase calculation of ASCVD risk scores in patients between the ages of 40 and 75 years from a baseline rate of less than 1% to 10%, within 12 months, for primary prevention of ASCVD. Root cause analysis was performed to identify materials/methods, provider and patient-related barriers. Plan-Do-Study-Act cycles included: (1) creation of customised workflow in electronic health records for documentation of calculated ASCVD risk score; (2) physician education regarding guidelines and electronic health record workflow; (3) refresher training for residents and a chart alert and (4) patient education and physician reminders. The outcome measures were ASCVD risk score completion rate and percentage of new prescriptions for statin therapy. Process measures included lipid profile order and completion rates. Increase in patient wait time, and blood test and medications costs were the balanced measures. We used weekly statistical process control charts for data analysis. The average ASCVD risk completion rate was 14.2%. The mean ASCVD risk completion rate was 4.0%. In eligible patients, the average lipid profile completion rate was 18%. ASCVD risk score completion rate was 33% 1-year postproject period. A team-based approach led to a sustainable increase in ASCVD risk score completion rate. Lack of automation in ASCVD risk score calculation and physician prompts in electronic health records were identified as major barriers. Furthermore, the team identified multiple barriers to lipid blood tests and treatment of increased ASCVD risk based on ACC/AHA guidelines.

  • primary care
  • healthcare quality improvement
  • quality improvement

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Footnotes

  • Contributors SYB: Study oversight, study concept and design, acquisition of data, analysis and interpretation of data, drafting of manuscript, critical revision of the manuscript for important intellectual content and finalisation of manuscript. AB: Acquisition of data, analysis and interpretation of data. PS: Acquisition of data, analysis and interpretation of data. JLR: Interpretation of data, drafting of manuscript, critical revision of the manuscript for important intellectual content and finalisation of manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent Not required.

  • Ethics approval Based on local policy, this work was deemed as an improvement study and not a research study on human subjects as patients were not affected directly and their treatment was not altered nor were they given an experimental or novel therapy. The local research policy was met and ethical approval was not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement An electronic patient database is available, contact authors for access.

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