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Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study

BMJ 2013; 346 doi: https://doi.org/10.1136/bmj.f2350 (Published 21 May 2013) Cite this as: BMJ 2013;346:f2350

Re: Completeness and diagnostic validity of recording acute myocardial infarction events in primary care, hospital care, disease registry, and national mortality records: cohort study

We thank Stevens and McManus for pointing out that using unlinked primary care databases may underestimate the absolute risk of myocardial infarction as they fail to capture all events. Non-differential under-recording may not cause bias in studies solely focussing on relative effects, but for predicting absolute risks or rates, using a single data source whether from primary or secondary care settings, may lead to biased estimates. Ideally all events should be recorded definitively in a single electronic health record, but as this does not currently happen in the NHS or other health systems, we recommend the use of linked data sources, such as those available through the Clinical Practice Research Datalink (CPRD), to overcome under-recording in individual sources.

In the CALIBER programme [1] we are developing prognostic models for patients with coronary disease in a linked dataset, using multiple data sources for outcome ascertainment. We are investigating the use of free text entered by doctors as an additional source of diagnostic information [2], and we are part of the new network of four UK e-Health Informatics Research Centres [3] which will make use of linked datasets available through the CPRD, facilitate further linkages and enable greater use of electronic health records for research.

1. Denaxas S, George J, Herrett E, Shah A, Kalra D, Hingorani AD, et al. Data resource profile: Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Records (CALIBER). Int J Epidemiol. 2012;41:1625-38. doi: 10.1093/ije/dys188

2. Shah AD, Martinez C, Hemingway H. The freetext matching algorithm: a computer program to extract diagnoses and causes of death from unstructured text in electronic health records. BMC Med Inform Decis Mak. 2012;12:88. doi: 10.1186/1472-6947-12-88

3. http://www.mrc.ac.uk/Ourresearch/ResearchInitiatives/E-HealthInformaticsResearch/index.htm

Competing interests: No competing interests

05 June 2013
Anoop D Shah
Clinical Research Fellow
Emily Herrett, Rachael Boggon, Spiros Denaxas, Liam Smeeth, Tjeerd van Staa, Adam Timmis, Harry Hemingway
Clinical Epidemiology Group, Department of Epidemiology and Public Health, University College London
1-19 Torrington Place, UCL - Gower Street Campus, London, WC1E 6BT, UK