Validation of asthma recording in electronic health records: a systematic review

• Main Authors: Nissen F, Quint JK, Wilkinson S, Mullerova H, Smeeth L, Douglas IJ
• Format: Article
• Language: English
• Published: Dove Medical Press 2017-12-01
• Series: Clinical Epidemiology
• Subjects: Asthma; Validation; Electronic Health Records; Sensitivity; Specificity; Positive Predictive Value
• Online Access: https://www.dovepress.com/validation-of-asthma-recording-in-electronic-health-records-a-systemat-peer-reviewed-article-CLEP

Francis Nissen,1 Jennifer K Quint,2 Samantha Wilkinson,1 Hana Mullerova,3 Liam Smeeth,1 Ian J Douglas1 1Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK; 2National Heart and Lung Institute, Imperial College, London, UK; 3RWD & Epidemiology, GSK R&D, Uxbridge, UK Objective: To describe the methods used to validate asthma diagnoses in electronic health records and summarize the results of the validation studies. Background: Electronic health records are increasingly being used for research on asthma to inform health services and health policy. Validation of the recording of asthma diagnoses in electronic health records is essential to use these databases for credible epidemiological asthma research.Methods: We searched EMBASE and MEDLINE databases for studies that validated asthma diagnoses detected in electronic health records up to October 2016. Two reviewers independently assessed the full text against the predetermined inclusion criteria. Key data including author, year, data source, case definitions, reference standard, and validation statistics (including sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) were summarized in two tables.Results: Thirteen studies met the inclusion criteria. Most studies demonstrated a high validity using at least one case definition (PPV >80%). Ten studies used a manual validation as the reference standard; each had at least one case definition with a PPV of at least 63%, up to 100%. We also found two studies using a second independent database to validate asthma diagnoses. The PPVs of the best performing case definitions ranged from 46% to 58%. We found one study which used a questionnaire as the reference standard to validate a database case definition; the PPV of the case definition algorithm in this study was 89%. Conclusion: Attaining high PPVs (>80%) is possible using each of the discussed validation methods. Identifying asthma cases in electronic health records is possible with high sensitivity, specificity or PPV, by combining multiple data sources, or by focusing on specific test measures. Studies testing a range of case definitions show wide variation in the validity of each definition, suggesting this may be important for obtaining asthma definitions with optimal validity. Keywords: sensitivity, specificity, PPV, NPV, database, validity, epidemiology