Validated methods to identify patients with asthma–COPD overlap in healthcare databases: a systematic review protocol

Author:

Amegadzie Joseph EmilORCID,Badejo Oluwatosin,Gamble John-MichaelORCID,Wright Mark,Farrell Jamie,Jackson Brooke,Sultana Kirin,Hashmi Maimoona,Gao Zhiwei

Abstract

IntroductionAsthma–chronic obstructive pulmonary disease (COPD) overlap (ACO) is characterised by patients presenting symptoms of both asthma and COPD. Many efforts have been made to validate different methods of identifying asthma–COPD overlap cases based on symptoms, spirometry and medical history in epidemiological studies using healthcare databases. There are various coding algorithm strategies that can be used and selection depends on targeted validation. The primary objectives of this systematic review are to identify validated methods (or algorithms) that identify patients with ACO from healthcare databases and summarise the reported validity measures of these methods.MethodsMEDLINE, EMBASE databases and the Web of Science will be systematically searched by using appropriate search strategies that are able to identify studies containing validated codes and algorithms for the diagnosis of ACO in healthcare databases published, in English, before October 2018. For each selected study, we require the presence of at least one test measure (eg, sensitivity, specificity etc). We will also include studies, in which the validated algorithm is compared with an external reference standard such as questionnaires completed by patients or physicians, medical charts review, manual review or an independent second database. For all selected studies, a uniform table will be created to summarise the following vital information: name of author, publication year, country, data source, population, clinical outcome, algorithms, reference standard method of validation and characteristics of the test measure used to determine validity.PROSPERO registration numberCRD42018087472.

Funder

Canadian Respiratory Research Network

Publisher

BMJ

Subject

General Medicine

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