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

Mohammad A Al Sallakh, Sarah E Rodgers, Ronan A Lyons, Aziz Sheikh, Gwyneth A Davies

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)22
Journalnpj Primary Care Respiratory Medicine
Volume28
Issue number1
DOIs
Publication statusPublished - 20 Jun 2018

Keywords

  • Journal Article

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