We will include population-based studies that have used computational approaches to derive phenotypes of chronic airway diseases, whether conducted in the general population or in a clinical setting. We will exclude studies that have characterized phenotypes of chronic airway diseases based on hypothesis-based approaches.
We will include studies focusing on computational phenotyping of the following chronic obstructive airway diseases:
We will include observational general population-based and clinical epidemiological studies, including cohort, case-control, and cross-sectional. We do not anticipate computational phenotyping studies of airway diseases based on randomized clinical trials or other experimental study designs. Case studies and case series as well as ecological studies will be excluded.
We will include studies conducted both in children and adults.
Years of consideration:
Studies conducted in the last ten years: (2010-2020) only will be considered for our review. The selected time window is the reported era of evolution of the use of computational approaches in phenotyping of chronic obstructive airway diseases (23).
There will be no language-based exclusions of studies, and we will endeavor to translate studies published in languages other than English.
To identify relevant studies for the review, we will search PubMed, EMBASE, Web of Science, Scopus and Google scholar. For unpublished materials, such as conferences proceedings, we will search databases of proceeding of conferences and databases of the grey literature, such as Open Grey. We will also contact experts in the field to request for any paper we may miss from our database searches. Finally, we will screen the reference lists of included studies to identify any additional paper.
We have developed a preliminary search strategy to identify relevant studies for the review. The search strategy (Supplementary File 1) was developed in PubMed and will be adapted in searching the other databases.
The search results from the different databases will be exported to Endnote for further screening. Two reviewers will independently screen the studies on the basis of the review inclusion and exclusion criteria; any discrepancies will be resolved by discussion or a third reviewer will arbitrate if a consensus is not reached. The first stage of the literature will involve removal of duplicates from the database searches; then we will perform title and abstract screening. The final stage will involve full-text screening of the studies potentially meeting the eligibility criteria not clearly identified from the titles and abstracts. We will document the screening process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA) flowchart (24) .
Two reviewers will independently extract relevant data from included studies onto a data extraction form to be developed for the review; any discrepancies will be resolved by discussion or a third reviewer will arbitrate if a consensus is not reached. We will develop a data extraction form specifically designed for this review that will be used to capture relevant data from included studies. The form will initially be first piloted on two to three included studies; any amendment will be undertaken prior to using the form on all included studies.
A minimum of the following data items will be collected from included studies onto the data extraction form: general information (authors name; publication year and study time; aim of the study and data source); information describing populations characteristics (population size, recruitment characteristics, sample size, children/adults, inclusion and exclusions criteria); type of airway disease; information about the variables selected for phenotyping (number and description of variables, rational of selection, variable measurement and definition); type and features of computational approach used; and information of the derived phenotypes (number of phenotypes, characteristics of each phenotype, and clinical interpretation).
We will appraise the general quality of included studies using the Effective Public Health Practice Project (EPHPP), where focus of this tool will be in relation to each study’s potential for selection bias; appropriateness of study design; data collection methods; withdrawals and dropouts and analysis (25). Since, to our knowledge, there are no standard tools for assessing the quality of studies on computational disease phenotyping, we will develop a preliminary checklist that will enable us to extract items related to the computational approaches used across studies and to help us compare approaches across studies.
Registration and reporting:
The full protocol for this systematic review is registered in the International Prospective Register of Systematic Reviews with the number CRD42020164898 according to the requirements of the PRISMA-P guideline(27, 28).