Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review. Issue 6 (15th June 2017)

• Record Type: Journal Article
• Authors: Al Sallakh, Mohammad A.; Vasileiou, Eleftheria; Rodgers, Sarah E.; Lyons, Ronan A.; Sheikh, Aziz; Davies, Gwyneth A.
• Is Part Of: European respiratory journal. Volume 49:Issue 6(2017)
• Publication Date: 2017-06-15
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; 616.2
• DOI: 10.1183/13993003.00204-2017
• Languages: English
• ISSNs: 0903-1936

Abstract

There is currently no consensus on approaches to defining asthma or assessing asthma outcomes using electronic health record-derived data. We explored these approaches in the recent literature and examined the clarity of reporting. We systematically searched for asthma-related articles published between January 1, 2014 and December 31, 2015, extracted the algorithms used to identify asthma patients and assess severity, control and exacerbations, and examined how the validity of these outcomes was justified. From 113 eligible articles, we found significant heterogeneity in the algorithms used to define asthma (n=66 different algorithms), severity (n=18), control (n=9) and exacerbations (n=24). For the majority of algorithms (n=106), validity was not justified. In the remaining cases, approaches ranged from using algorithms validated in the same databases to using nonvalidated algorithms that were based on clinical judgement or clinical guidelines. The implementation of these algorithms was suboptimally described overall. Although electronic health record-derived data are now widely used to study asthma, the approaches being used are significantly varied and are often underdescribed, rendering it difficult to assess the validity of studies and compare their findings. Given the substantial growth in this body of literature, it is crucial that scientific consensus is reached on the underlying definitions and algorithms. Inconsistent/underreported asthma case definitions are common in research, limiting reproducibility/comparability http://ow.ly/knmc30a0ltp