ArticleBiomedical informatics insights2017
Using Electronic Medical Record to Identify Patients With Dyslipidemia in Primary Care Settings: International Classification of Disease Code Matters From One Region to a National Database.
Article in Biomedical informatics insights, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Agreement in All-in-One Dataset between Diagnosis and Prescribed Medication for Common Cardiometabolic Diseases in the NDB-K7Ps.Epidemiologia (Basel, Switzerland) · 2023Article
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- Variability independent of mean blood pressure as a real-world measure of cardiovascular risk.EClinicalMedicine · 2022Article
- Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.Journal of clinical monitoring and computing · 2022Article
- Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan's National Health Insurance Claims Data.Clinical epidemiology · 2022Article
- Association of Granuloma Annulare With Type 2 Diabetes, Hyperlipidemia, Autoimmune Disorders, and Hematologic Malignant Neoplasms.JAMA dermatology · 2021Article
- Performance evaluation of case definitions of type 1 diabetes for health insurance claims data in Japan.BMC medical informatics and decision making · 2021Article
- Predictors for diabetic retinopathy progression-findings from nominal group technique and Evidence review.BMJ open ophthalmology · 2020Article
- The Lipid Paradox Among Acute Ischemic Stroke Patients-A Retrospective Study of Outcomes and Complications.Medicina (Kaunas, Lithuania) · 2019Article
- Identified mental disorders in older adults in primary care: A cross-sectional database study.The European journal of general practice · 2018Article
- Genetic associations in community context: a mixed model approach identifies a functional variant in the RBP4 gene associated with HDL-C dyslipidemia.BMC medical genetics · 2018Article
- Six-year time-trend analysis of dyslipidemia among adults in Newfoundland and Labrador: findings from the laboratory information system between 2009 and 2014.Lipids in health and disease · 2018Article
- Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database.Journal of medical systems · 2017Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess the validity of the International Classification of Disease (ICD) codes for identifying patients with dyslipidemia in electronic medical record (EMR) data.
methodsThe EMRs of patients receiving primary care in St. John's, Newfoundland and Labrador (NL), Canada, were retrieved from the Canadian Primary Care Sentinel Surveillance Network database. International Classification of Disease codes were first compared with laboratory lipid data as an independent criterion standard, and next with a "comprehensive criterion standard," defined as any existence of abnormal lipid test, lipid-lowering medication record, or dyslipidemia ICD codes. The ability of ICD coding alone or combined with other components was evaluated against the two criterion standards using receiver operating characteristic (ROC) analysis, sensitivity, specificity, negative predictive value (NPV) and Kappa agreement. (No specificity was reported for the comparison of ICD codes against the comprehensive criterion standard as this naturally leads to 100% specificity.).
resultsThe ICD codes led to a poor outcome when compared with the serum lipid levels (sensitivity, 27%; specificity, 76%; PPV, 71%; NPV, 33%; Kappa, 0.02; area under the receiver operating characteristic curve (AUC), 0.51) or with the comprehensive criterion standard (sensitivity, 32%; NPV, 25%; Kappa, 0.15; AUC, 66%). International Classification of Disease codes combined with lipid-lowering medication data also resulted in low sensitivity (51.2%), NPV (32%), Kappa (0.28), and AUC (75%). The addition of laboratory lipid levels to ICD coding marginally improved the algorithm (sensitivity, 94%; NPV, 79%; Kappa, 0.85; AUC, 97%).
conclusionsThe use of ICD coding, either alone or in combination with laboratory data or lipid-lowering medication records, was not an accurate indicator in identifying dyslipidemia.
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