Cargando…

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

Asthma and chronic obstructive pulmonary disease (COPD) are two common different clinical diagnoses with overlapping clinical features. Both conditions have been increasingly studied using electronic health records (EHR). Asthma-COPD overlap syndrome (ACOS) is an emerging concept where clinical feat...

Descripción completa

Detalles Bibliográficos
Autores principales: Al Sallakh, Mohammad A, Rodgers, Sarah E, Lyons, Ronan A, Sheikh, Aziz, Davies, Gwyneth A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010464/
https://www.ncbi.nlm.nih.gov/pubmed/29925836
http://dx.doi.org/10.1038/s41533-018-0088-4
_version_ 1783333582862811136
author Al Sallakh, Mohammad A
Rodgers, Sarah E
Lyons, Ronan A
Sheikh, Aziz
Davies, Gwyneth A
author_facet Al Sallakh, Mohammad A
Rodgers, Sarah E
Lyons, Ronan A
Sheikh, Aziz
Davies, Gwyneth A
author_sort Al Sallakh, Mohammad A
collection PubMed
description Asthma and chronic obstructive pulmonary disease (COPD) are two common different clinical diagnoses with overlapping clinical features. Both conditions have been increasingly studied using electronic health records (EHR). Asthma-COPD overlap syndrome (ACOS) is an emerging concept where clinical features from both conditions co-exist, and for which, however, there is no consensus definition. Nonetheless, we expect EHR data of people with ACOS to be systematically different from those with “asthma only” or “COPD only”. We aim to develop a latent class model to understand the overlap between asthma and COPD in EHR data. From the Secure Anonymised Information Linkage (SAIL) databank, we will use routinely collected primary care data recorded in or before 2014 in Wales for people who aged 40 years or more on 1st Jan 2014. Based on this latent class model, we will train a classification algorithm and compare its performance with commonly used objective and self-reported case definitions for asthma and COPD. The resulting classification algorithm is intended to be used to identify people with ACOS, ‘asthma only’, and ‘COPD only’ in primary care datasets.
format Online
Article
Text
id pubmed-6010464
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-60104642018-06-27 Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol Al Sallakh, Mohammad A Rodgers, Sarah E Lyons, Ronan A Sheikh, Aziz Davies, Gwyneth A NPJ Prim Care Respir Med Protocol Asthma and chronic obstructive pulmonary disease (COPD) are two common different clinical diagnoses with overlapping clinical features. Both conditions have been increasingly studied using electronic health records (EHR). Asthma-COPD overlap syndrome (ACOS) is an emerging concept where clinical features from both conditions co-exist, and for which, however, there is no consensus definition. Nonetheless, we expect EHR data of people with ACOS to be systematically different from those with “asthma only” or “COPD only”. We aim to develop a latent class model to understand the overlap between asthma and COPD in EHR data. From the Secure Anonymised Information Linkage (SAIL) databank, we will use routinely collected primary care data recorded in or before 2014 in Wales for people who aged 40 years or more on 1st Jan 2014. Based on this latent class model, we will train a classification algorithm and compare its performance with commonly used objective and self-reported case definitions for asthma and COPD. The resulting classification algorithm is intended to be used to identify people with ACOS, ‘asthma only’, and ‘COPD only’ in primary care datasets. Nature Publishing Group UK 2018-06-20 /pmc/articles/PMC6010464/ /pubmed/29925836 http://dx.doi.org/10.1038/s41533-018-0088-4 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Protocol
Al Sallakh, Mohammad A
Rodgers, Sarah E
Lyons, Ronan A
Sheikh, Aziz
Davies, Gwyneth A
Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol
title Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol
title_full Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol
title_fullStr Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol
title_full_unstemmed Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol
title_short Identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol
title_sort identifying patients with asthma-chronic obstructive pulmonary disease overlap syndrome using latent class analysis of electronic health record data: a study protocol
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010464/
https://www.ncbi.nlm.nih.gov/pubmed/29925836
http://dx.doi.org/10.1038/s41533-018-0088-4
work_keys_str_mv AT alsallakhmohammada identifyingpatientswithasthmachronicobstructivepulmonarydiseaseoverlapsyndromeusinglatentclassanalysisofelectronichealthrecorddataastudyprotocol
AT rodgerssarahe identifyingpatientswithasthmachronicobstructivepulmonarydiseaseoverlapsyndromeusinglatentclassanalysisofelectronichealthrecorddataastudyprotocol
AT lyonsronana identifyingpatientswithasthmachronicobstructivepulmonarydiseaseoverlapsyndromeusinglatentclassanalysisofelectronichealthrecorddataastudyprotocol
AT sheikhaziz identifyingpatientswithasthmachronicobstructivepulmonarydiseaseoverlapsyndromeusinglatentclassanalysisofelectronichealthrecorddataastudyprotocol
AT daviesgwynetha identifyingpatientswithasthmachronicobstructivepulmonarydiseaseoverlapsyndromeusinglatentclassanalysisofelectronichealthrecorddataastudyprotocol