Literature DB >> 28619959

Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review.

Mohammad A Al Sallakh1,2, Eleftheria Vasileiou2,3, Sarah E Rodgers4,5, Ronan A Lyons4,5, Aziz Sheikh2,3,5, Gwyneth A Davies4,2.   

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.
Copyright ©ERS 2017.

Entities:  

Mesh:

Year:  2017        PMID: 28619959     DOI: 10.1183/13993003.00204-2017

Source DB:  PubMed          Journal:  Eur Respir J        ISSN: 0903-1936            Impact factor:   16.671


  19 in total

1.  Performance of a computable phenotype for pediatric asthma using the problem list.

Authors:  Monica Tang; Benjamin A Goldstein; Jingye He; Jillian H Hurst; Jason E Lang
Journal:  Ann Allergy Asthma Immunol       Date:  2020-07-17       Impact factor: 6.347

2.  Developing and evaluating a pediatric asthma severity computable phenotype derived from electronic health records.

Authors:  Komal Peer; William G Adams; Aaron Legler; Megan Sandel; Jonathan I Levy; Renée Boynton-Jarrett; Chanmin Kim; Jessica H Leibler; M Patricia Fabian
Journal:  J Allergy Clin Immunol       Date:  2020-12-15       Impact factor: 14.290

Review 3.  Validation of asthma recording in electronic health records: a systematic review.

Authors:  Francis Nissen; Jennifer K Quint; Samantha Wilkinson; Hana Mullerova; Liam Smeeth; Ian J Douglas
Journal:  Clin Epidemiol       Date:  2017-12-01       Impact factor: 4.790

4.  Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol.

Authors:  Mohammad A Al Sallakh; Sarah E Rodgers; Ronan A Lyons; Aziz Sheikh; Gwyneth A Davies
Journal:  NPJ Prim Care Respir Med       Date:  2018-06-20       Impact factor: 2.871

5.  Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.

Authors:  Amitava Banerjee; Laura Pasea; Steve Harris; Arturo Gonzalez-Izquierdo; Ana Torralbo; Laura Shallcross; Mahdad Noursadeghi; Deenan Pillay; Neil Sebire; Chris Holmes; Christina Pagel; Wai Keong Wong; Claudia Langenberg; Bryan Williams; Spiros Denaxas; Harry Hemingway
Journal:  Lancet       Date:  2020-05-12       Impact factor: 79.321

6.  Defining Major Depressive Disorder Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders.

Authors:  Wendy Marie Ingram; Anna M Baker; Christopher R Bauer; Jason P Brown; Fernando S Goes; Sharon Larson; Peter P Zandi
Journal:  Neurol Psychiatry Brain Res       Date:  2020-02-21

7.  Asthma Exacerbations in Patients with Type 2 Diabetes and Asthma on Glucagon-like Peptide-1 Receptor Agonists.

Authors:  Dinah Foer; Patrick E Beeler; Jing Cui; Elizabeth W Karlson; David W Bates; Katherine N Cahill
Journal:  Am J Respir Crit Care Med       Date:  2021-04-01       Impact factor: 21.405

8.  Metformin Use and Risk of Asthma Exacerbation Among Asthma Patients with Glycemic Dysfunction.

Authors:  Tianshi David Wu; Ashraf Fawzy; Ayobami Akenroye; Corinne Keet; Nadia N Hansel; Meredith C McCormack
Journal:  J Allergy Clin Immunol Pract       Date:  2021-07-19

9.  Childhood asthma prevalence: cross-sectional record linkage study comparing parent-reported wheeze with general practitioner-recorded asthma diagnoses from primary care electronic health records in Wales.

Authors:  Lucy J Griffiths; Ronan A Lyons; Amrita Bandyopadhyay; Karen S Tingay; Suzanne Walton; Mario Cortina-Borja; Ashley Akbari; Helen Bedford; Carol Dezateux
Journal:  BMJ Open Respir Res       Date:  2018-01-08

10.  Creating individual level air pollution exposures in an anonymised data safe haven: a platform for evaluating impact on educational attainment.

Authors:  Amy Mizen; Jane Lyons; Ruth Doherty; Damon Berridge; Paul Wilkinson; Ai Milojevic; David Carruthers; Ashley Akbari; Iain Lake; Gwyneth A Davies; Mohammad Al Sallakh; Anna Mavrogianni; Lorraine Dearden; Rhodri Johnson; Sarah Elizabeth Rodgers
Journal:  Int J Popul Data Sci       Date:  2018-08-21
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