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Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database

  • Erfan Aref-Eshghi
  • Justin Oake
  • Marshall Godwin
  • Kris Aubrey-Bassler
  • Pauline Duke
  • Masoud Mahdavian
  • Shabnam Asghari
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

The objective of this study was to define the optimal algorithm to identify patients with dyslipidemia using electronic medical records (EMRs). EMRs of patients attending primary care clinics in St. John’s, Newfoundland and Labrador (NL), Canada during 2009–2010, were studied to determine the best algorithm for identification of dyslipidemia. Six algorithms containing three components, dyslipidemia ICD coding, lipid lowering medication use, and abnormal laboratory lipid levels, were tested against a gold standard, defined as the existence of any of the three criteria. Linear discriminate analysis, and bootstrapping were performed following sensitivity/specificity testing and receiver’s operating curve analysis. Two validating datasets, NL records of 2011–2014, and Canada-wide records of 2010–2012, were used to replicate the results. Relative to the gold standard, combining laboratory data together with lipid lowering medication consumption yielded the highest sensitivity (99.6%), NPV (98.1%), Kappa agreement (0.98), and area under the curve (AUC, 0.998). The linear discriminant analysis for this combination resulted in an error rate of 0.15 and an Eigenvalue of 1.99, and the bootstrapping led to AUC: 0.998, 95% confidence interval: 0.997–0.999, Kappa: 0.99. This algorithm in the first validating dataset yielded a sensitivity of 97%, Negative Predictive Value (NPV) = 83%, Kappa = 0.88, and AUC = 0.98. These figures for the second validating data set were 98%, 93%, 0.95, and 0.99, respectively. Combining laboratory data with lipid lowering medication consumption within the EMR is the best algorithm for detecting dyslipidemia. These results can generate standardized information systems for dyslipidemia and other chronic disease investigations using EMRs.

Keywords

Electronic medical records Algorithm Dyslipidemia Data mining 

Notes

Acknowledgements

We thank Ms. Kathleen Murphy for language editing of the manuscript.

Authors’ contributions

Study Design: SA, EAE, JO; Data collection: SA, EAE; Manuscript writing: SA, JO, EAE; Critical comments on the manuscript: EAE, PD, KAB, MG, PD, MM.

Compliance with Ethical Standards

Competing interests

The authors have no potential conflicts to declare.

Funding source

This study was funded by the Newfoundland and Labrador Centre for Applied Health Research (NLCAHR). All of the study steps including data analysis, interpreting the results, and manuscript writing were performed with the support of the mentioned funding source.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Erfan Aref-Eshghi
    • 1
    • 2
  • Justin Oake
    • 1
    • 2
  • Marshall Godwin
    • 2
  • Kris Aubrey-Bassler
    • 1
  • Pauline Duke
    • 2
  • Masoud Mahdavian
    • 3
  • Shabnam Asghari
    • 1
    • 2
  1. 1.Faculty of Medicine, Center for Rural Health Studies, Agnes Cowan Hostel, Health Sciences CentreMemorial University of NewfoundlandSt. John’sCanada
  2. 2.Primary Healthcare Research Unit, Department of Family Medicine, Faculty of MedicineMemorial University of NewfoundlandSt. John’sCanada
  3. 3.Department of Medicine, Faculty of MedicineMemorial University of NewfoundlandSt. John’sCanada

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