Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review
Muwada Bashir Awad Bashir,1 Gregorio Paolo Milani,2,3,* Valentina De Cosmi,4,5,* Alessandra Mazzocchi,3,* Guoqiang Zhang,1 Rani Basna,1 Linnea Hedman,6 Anne Lindberg,7 Linda Ekerljung,1 Malin Axelsson,8 Lowie EGW Vanfleteren,9 Eva Rönmark,6 Helena Backman,6 Hannu Kankaanranta...
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Dove Medical Press
2025-02-01
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| Series: | Journal of Asthma and Allergy |
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| author | Bashir MBA Milani GP De Cosmi V Mazzocchi A Zhang G Basna R Hedman L Lindberg A Ekerljung L Axelsson M Vanfleteren LEGW Rönmark E Backman H Kankaanranta H Nwaru BI |
| author_facet | Bashir MBA Milani GP De Cosmi V Mazzocchi A Zhang G Basna R Hedman L Lindberg A Ekerljung L Axelsson M Vanfleteren LEGW Rönmark E Backman H Kankaanranta H Nwaru BI |
| author_sort | Bashir MBA |
| collection | DOAJ |
| description | Muwada Bashir Awad Bashir,1 Gregorio Paolo Milani,2,3,* Valentina De Cosmi,4,5,* Alessandra Mazzocchi,3,* Guoqiang Zhang,1 Rani Basna,1 Linnea Hedman,6 Anne Lindberg,7 Linda Ekerljung,1 Malin Axelsson,8 Lowie EGW Vanfleteren,9 Eva Rönmark,6 Helena Backman,6 Hannu Kankaanranta,1,10,11 Bright I Nwaru1,12 1Krefting Research Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; 2Pediatric Unit, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy; 3Department of Clinical Science and Community Health, University of Milan, Milan, Italy; 4Department of Food Safety, Nutrition and Veterinary Public Health, Instituto Superiore Di Sanità - Italian National Institute of Health, Roma, Italy; 5Department of Clinical Sciences and Community Health, University of Milan, Milano, Italy; 6Department of Public Health and Clinical Medicine, Section of Sustainable Health/ the OLIN Unit, Umeå University, Umeå, Sweden; 7Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden; 8Department of Care Science, Faculty of Health and Society, Malmö University, Malmö, Sweden; 9COPD Center, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden; 10Department of Respiratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland; 11Tampere University Respiratory Research Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; 12Wallenberg Centre for Molecular and Translational Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden*These authors contributed equally to this workCorrespondence: Muwada Bashir Awad Bashir, Krefting Research Centre, Institute of Medicine, University of Gothenburg, P.O. Box 424, Gothenburg, SE-405 30, Sweden, Tel +46707847667, Fax +46 317863101, Email muwada.bashir@gu.se; mawadah99@yahoo.comIntroduction: Computational sciences have significantly contributed to characterizing airway disease phenotypes, complementing medical expertise. However, comparing studies that derive phenotypes is challenging due to varying decisions made during phenotyping. We conducted a systematic review to describe studies that utilized unsupervised computational approaches for phenotyping obstructive airway diseases in children and adults.Methods: We searched for relevant papers published between 2010 and 2020 in PubMed, EMBASE, Scopus, Web of Science, and Google Scholar. Additional sources included conference proceedings, reference lists, and expert recommendations. Two reviewers independently screened studies for eligibility, extracted data, and assessed study quality. Disagreements were resolved by a third reviewer. An in-house quality appraisal tool was used. Evidence was synthesized, focusing on populations, variables, and computational approaches used for deriving phenotypes.Results: Of 120 studies included in the review, 60 focused on asthma, 19 on severe asthma, 28 on COPD, 4 on asthma-COPD overlap (ACO), and 9 on rhinitis. Among asthma studies, 31 focused on adults and 9 on children, with phenotypes related to atopy, age at onset, and disease severity. Severe asthma phenotypes were characterized by symptomatology, atopy, and age at onset. COPD phenotypes involved lung function, emphysematous changes, smoking, comorbidities, and daily life impairment. ACO and rhinitis phenotypes were mostly defined by symptoms, lung function, and sensitization, respectively. Most studies used hierarchical clustering, with some employing latent class modeling, mixture models, and factor analysis. The comprehensiveness of variable reporting was the best quality indicator, while reproducibility measures were often lacking.Conclusion: Variations in phenotyping methods, study settings, participant profiles, and variables contribute to significant differences in characterizing asthma, severe asthma, COPD, ACO, and rhinitis phenotypes across studies. Lack of reproducibility measures limits the evaluation of computational phenotyping in airway diseases, underscoring the need for consistent approaches to defining outcomes and selecting variables to ensure reliable phenotyping.Keywords: asthma, COPD, severe asthma, rhinitis, unsupervised, phenotyping |
| format | Article |
| id | doaj-art-90de090ccc284abfa8ecf7b13003a48f |
| institution | Kabale University |
| issn | 1178-6965 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Journal of Asthma and Allergy |
| spelling | doaj-art-90de090ccc284abfa8ecf7b13003a48f2025-02-06T16:40:25ZengDove Medical PressJournal of Asthma and Allergy1178-69652025-02-01Volume 1811316099934Computational Phenotyping of Obstructive Airway Diseases: A Systematic ReviewBashir MBAMilani GPDe Cosmi VMazzocchi AZhang GBasna RHedman LLindberg AEkerljung LAxelsson MVanfleteren LEGWRönmark EBackman HKankaanranta HNwaru BIMuwada Bashir Awad Bashir,1 Gregorio Paolo Milani,2,3,* Valentina De Cosmi,4,5,* Alessandra Mazzocchi,3,* Guoqiang Zhang,1 Rani Basna,1 Linnea Hedman,6 Anne Lindberg,7 Linda Ekerljung,1 Malin Axelsson,8 Lowie EGW Vanfleteren,9 Eva Rönmark,6 Helena Backman,6 Hannu Kankaanranta,1,10,11 Bright I Nwaru1,12 1Krefting Research Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; 2Pediatric Unit, Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy; 3Department of Clinical Science and Community Health, University of Milan, Milan, Italy; 4Department of Food Safety, Nutrition and Veterinary Public Health, Instituto Superiore Di Sanità - Italian National Institute of Health, Roma, Italy; 5Department of Clinical Sciences and Community Health, University of Milan, Milano, Italy; 6Department of Public Health and Clinical Medicine, Section of Sustainable Health/ the OLIN Unit, Umeå University, Umeå, Sweden; 7Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden; 8Department of Care Science, Faculty of Health and Society, Malmö University, Malmö, Sweden; 9COPD Center, Sahlgrenska University Hospital, University of Gothenburg, Gothenburg, Sweden; 10Department of Respiratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland; 11Tampere University Respiratory Research Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland; 12Wallenberg Centre for Molecular and Translational Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden*These authors contributed equally to this workCorrespondence: Muwada Bashir Awad Bashir, Krefting Research Centre, Institute of Medicine, University of Gothenburg, P.O. Box 424, Gothenburg, SE-405 30, Sweden, Tel +46707847667, Fax +46 317863101, Email muwada.bashir@gu.se; mawadah99@yahoo.comIntroduction: Computational sciences have significantly contributed to characterizing airway disease phenotypes, complementing medical expertise. However, comparing studies that derive phenotypes is challenging due to varying decisions made during phenotyping. We conducted a systematic review to describe studies that utilized unsupervised computational approaches for phenotyping obstructive airway diseases in children and adults.Methods: We searched for relevant papers published between 2010 and 2020 in PubMed, EMBASE, Scopus, Web of Science, and Google Scholar. Additional sources included conference proceedings, reference lists, and expert recommendations. Two reviewers independently screened studies for eligibility, extracted data, and assessed study quality. Disagreements were resolved by a third reviewer. An in-house quality appraisal tool was used. Evidence was synthesized, focusing on populations, variables, and computational approaches used for deriving phenotypes.Results: Of 120 studies included in the review, 60 focused on asthma, 19 on severe asthma, 28 on COPD, 4 on asthma-COPD overlap (ACO), and 9 on rhinitis. Among asthma studies, 31 focused on adults and 9 on children, with phenotypes related to atopy, age at onset, and disease severity. Severe asthma phenotypes were characterized by symptomatology, atopy, and age at onset. COPD phenotypes involved lung function, emphysematous changes, smoking, comorbidities, and daily life impairment. ACO and rhinitis phenotypes were mostly defined by symptoms, lung function, and sensitization, respectively. Most studies used hierarchical clustering, with some employing latent class modeling, mixture models, and factor analysis. The comprehensiveness of variable reporting was the best quality indicator, while reproducibility measures were often lacking.Conclusion: Variations in phenotyping methods, study settings, participant profiles, and variables contribute to significant differences in characterizing asthma, severe asthma, COPD, ACO, and rhinitis phenotypes across studies. Lack of reproducibility measures limits the evaluation of computational phenotyping in airway diseases, underscoring the need for consistent approaches to defining outcomes and selecting variables to ensure reliable phenotyping.Keywords: asthma, COPD, severe asthma, rhinitis, unsupervised, phenotypinghttps://www.dovepress.com/computational-phenotyping-of-obstructive-airway-diseases-a-systematic--peer-reviewed-fulltext-article-JAAasthmacopdsevere asthmarhinitisunsupervisedphenotyping |
| spellingShingle | Bashir MBA Milani GP De Cosmi V Mazzocchi A Zhang G Basna R Hedman L Lindberg A Ekerljung L Axelsson M Vanfleteren LEGW Rönmark E Backman H Kankaanranta H Nwaru BI Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review Journal of Asthma and Allergy asthma copd severe asthma rhinitis unsupervised phenotyping |
| title | Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review |
| title_full | Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review |
| title_fullStr | Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review |
| title_full_unstemmed | Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review |
| title_short | Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review |
| title_sort | computational phenotyping of obstructive airway diseases a systematic review |
| topic | asthma copd severe asthma rhinitis unsupervised phenotyping |
| url | https://www.dovepress.com/computational-phenotyping-of-obstructive-airway-diseases-a-systematic--peer-reviewed-fulltext-article-JAA |
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