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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

  • Systems-Level Quality Improvement
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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.

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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.

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Authors and Affiliations

  1. Faculty of Medicine, Center for Rural Health Studies, Agnes Cowan Hostel, Health Sciences Centre, Memorial University of Newfoundland, Room 425, 300 Prince Philip Drive, St. John’s, NL, A1B 3V6, Canada

    Erfan Aref-Eshghi, Justin Oake, Kris Aubrey-Bassler & Shabnam Asghari

  2. Primary Healthcare Research Unit, Department of Family Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, NL, Canada

    Erfan Aref-Eshghi, Justin Oake, Marshall Godwin, Pauline Duke & Shabnam Asghari

  3. Department of Medicine, Faculty of Medicine, Memorial University of Newfoundland, St. John’s, NL, Canada

    Masoud Mahdavian

Authors
  1. Erfan Aref-Eshghi
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  2. Justin Oake
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  3. Marshall Godwin
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  4. Kris Aubrey-Bassler
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  5. Pauline Duke
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  6. Masoud Mahdavian
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  7. Shabnam Asghari
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Corresponding author

Correspondence to Shabnam Asghari.

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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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This article is part of the Topical Collection on Systems-Level Quality Improvement.

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Aref-Eshghi, E., Oake, J., Godwin, M. et al. Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database. J Med Syst 41, 45 (2017). https://doi.org/10.1007/s10916-017-0694-7

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  • DOI: https://doi.org/10.1007/s10916-017-0694-7

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