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How well can electronic health records from primary care identify Alzheimer’s disease cases?

Authors Ponjoan A, Garre-Olmo J, Blanch J, Fages E, Alves-Cabratosa L, Martí-Lluch R, Comas-Cufí M, Parramon D, García-Gil M, Ramos R

Received 26 February 2019

Accepted for publication 24 April 2019

Published 5 July 2019 Volume 2019:11 Pages 509—518

DOI https://doi.org/10.2147/CLEP.S206770

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Melinda Thomas

Peer reviewer comments 2

Editor who approved publication: Professor Irene Petersen


Anna Ponjoan,1–3 Josep Garre-Olmo,3 Jordi Blanch,1 Ester Fages,1,4 Lia Alves-Cabratosa,1 Ruth Martí-Lluch,1–3 Marc Comas-Cufí,1 Dídac Parramon,1,4 María García-Gil,1 Rafel Ramos1,5

1Vascular Health Research Group (ISV-Girona), Jordi Gol Institute for Primary Care Research (IDIAPJGol), Barcelona, Catalonia, Spain; 2Universitat Autònoma de Barcelona, Bellaterra, Catalonia, Spain; 3Girona Biomedical Research Institute (IDIBGI), Girona, Catalonia, Spain; 4Primary Care Services, Catalan Health Institute (ICS), Girona, Catalonia, Spain; 5Department of Medical Sciences, School of Medicine, Campus Salut, University of Girona, Girona, Catalonia, Spain

Background: Electronic health records (EHR) from primary care are emerging in Alzheimer’s disease (AD) research, but their accuracy is a concern. We aimed to validate AD diagnoses from primary care using additional information provided by general practitioners (GPs), and a register of dementias.
Patients and methods: This retrospective observational study obtained data from the System for the Development of Research in Primary Care (SIDIAP). Three algorithms combined International Statistical Classification of Diseases (ICD-10) and Anatomical Therapeutic Chemical codes to identify AD cases in SIDIAP. GPs evaluated dementia diagnoses by means of an online survey. We linked data from the Register of Dementias of Girona and from SIDIAP. We estimated the positive predictive value (PPV) and sensitivity and provided results stratified by age, sex and severity.
Results: Using survey data from the GPs, PPV of AD diagnosis was 89.8% (95% CI: 84.7–94.9). Using the dataset linkage, PPV was 74.8 (95% CI: 73.1–76.4) for algorithm A1 (AD diagnoses), and 72.3 (95% CI: 70.7–73.9) for algorithm A3 (diagnosed or treated patients without previous conditions); sensitivity was 71.4 (95% CI: 69.6–73.0) and 83.3 (95% CI: 81.8–84.6) for algorithms A1 (AD diagnoses) and A3, respectively. Stratified results did not differ by age, but PPV and sensitivity estimates decreased amongst men and severe patients, respectively.
Conclusions: PPV estimates differed depending on the gold standard. The development of algorithms integrating diagnoses and treatment of dementia improved the AD case ascertainment. PPV and sensitivity estimates were high and indicated that AD codes recorded in a large primary care database were sufficiently accurate for research purposes.

Keywords: dementia, family physician, survey, algorithm, data accuracy, real-world data, validation, electronic medical records

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