Identification of major cardiovascular events in patients with diabetes using primary care data

Pouwels, K. B., Voorham, J., Hak, E. & Denig, P., 2-Apr-2016, In : BMC Health Services Research. 16, 8 p., 110.

Research output: Contribution to journalArticleAcademicpeer-review

Background: Routine primary care data are increasingly being used for evaluation and research purposes but there are concerns about the completeness and accuracy of diagnoses and events captured in such databases. We evaluated how well patients with major cardiovascular disease (CVD) can be identified using primary care morbidity data and drug prescriptions.

Methods: The study was conducted using data from 17,230 diabetes patients of the GIANTT database and Dutch Hospital Data register. To estimate the accuracy of the different measures, we analyzed the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) relative to hospitalizations and/or records with a diagnosis indicating major CVD, including ischaemic heart diseases and cerebrovascular events.

Results: Using primary care morbidity data, 43 % of major CVD hospitalizations could be identified. Adding drug prescriptions to the search increased the sensitivity up to 94 %. A proxy of at least one prescription of either a platelet aggregation inhibitor, vitamin k antagonist or nitrate could identify 85 % of patients with a history of major CVD recorded in primary care, with an NPV of 97 %. Using the same proxy, 57 % of incident major CVD recorded in primary or hospital care could be identified, with an NPV of 99 %.

Conclusions: A substantial proportion of major CVD hospitalizations was not recorded in primary care morbidity data. Drug prescriptions can be used in addition to diagnosis codes to identify more patients with major CVD, and also to identify patients without a history of major CVD.

Original languageEnglish
Article number110
Number of pages8
JournalBMC Health Services Research
Publication statusPublished - 2-Apr-2016


  • Cardiovascular diseases, Electronic health records, Diabetes mellitus, Registries, General practice, Sensitivity and specificity, MYOCARDIAL-INFARCTION, IDENTIFYING PATIENTS, GENERAL-PRACTICE, MEDICAL-RECORDS, HEART-DISEASE, FREE-TEXT, MORTALITY, RISK, MISCLASSIFICATION, QUALITY
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