ArticleResearch square2025
Deep Learning of Suboptimal Spirometry to Predict Respiratory Outcomes and Mortality.
Davin Hill, Max Torop, Aria Masoomi, Peter J Castaldi, Edwin K Silverman, Sandeep Bodduluri, Surya P Bhatt, Taedong Yun, Cory Y McLean, Farhad Hormozdiari and 3 more
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In one paragraphArticle in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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What it found
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2 · The registryThe trial behind it
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3 · Its place in the literatureWho cites it
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4 · The recordCorrections and comments
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5 · Who and what moneyAuthors and funding
13 authors.
Davin HillDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Max ToropDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Aria MasoomiDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Peter J CastaldiChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Edwin K SilvermanChanning Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Sandeep BodduluriDivision of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
Surya P BhattDivision of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.ORCID 0000-0002-8418-4497 Jennifer DyDepartment of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Funding
Genetic Epidemiology of COPDU01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2007 to 2021
$56.9MGENETIC EPIDEMIOLOGY OF COPD (COPD GENE) TASK A: STUDY VISIT 4, COLLECTION OF COPDGENE STUDY DATA ANDBIOSPECIMENS AND OVERSIGHT OF THE COPDGENE STUDY75N92023D00011 · NHLBI · NATIONAL JEWISH HEALTH · PI NEWMAN, LEE S · 2023 to 2025
$29.6MRespiratory Computational Discovery CoreP01HL114501 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI HERSH, CRAIG P · 2013 to 2025
$24.9MGenetic Epidemiology of COPDU01HL089856 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2007 to 2021
$20.7M(2 of 2) Genetic Epidemiology of COPDR01HL089856 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2012 to 2016
$18.9MSYSTEMS APPROACHES TO THE EPIDEMIOLOGY, GENETICS AND GENOMICS OF LUNG DISEASEST32HL007427 · NHLBI · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI DAWN L DEMEO, Edwin K Silverman · 1985 to 2026
$13.6MGenetic and Genomic Characterization of the Occurrence and Progression of Interstitial Lung AbnormalitiesR01HL135142 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MICHAEL H. CHO, GARY MATTHEW HUNNINGHAKE · 2017 to 2026
$7.7MCOPD Susceptibility, Heterogeneity, and Progression: Proteomics and GeneticsR01HL133135 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI MORITZ, ROBERT L, SILVERMAN, EDWIN K · 2017 to 2025
$7.2MUsing Integrative Genomics To Identify and Characterize Emphysema-Associated eQTLR01HL124233 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CASTALDI, PETER · 2014 to 2024
$7.1M1/2 Video Telehealth Pulmonary Rehabilitation to Reduce Hospital Readmission in Chronic Obstructive Pulmonary Disease (Tele-COPD)UH3HL155806 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Surya P Bhatt · 2022 to 2026
$5.4MM-ISIC: A Multimodal Open-Source International Skin Imaging Collaboration Informatics Platform for Automated Skin Cancer DetectionU24CA264369 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI Kivanc Kose, Veronica Miriam Rotemberg · 2022 to 2026
$3.9MGenetic and Functional Dissection of a Cluster of COPD GWAS Signals on Chromosome 4qR01HL147148 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI CHO, MICHAEL H., SILVERMAN, EDWIN K · 2019 to 2022
$3.5MNCI NIH HHS U24 CA264369NHLBI NIH HHS 75N92023D00011NHLBI NIH HHS K08 HL136928NHLBI NIH HHS P01 HL114501NHLBI NIH HHS R01 HL089856NHLBI NIH HHS R01 HL124233NHLBI NIH HHS R01 HL133135NHLBI NIH HHS R01 HL135142NHLBI NIH HHS R01 HL137927NHLBI NIH HHS R01 HL147148NHLBI NIH HHS R01 HL147326NHLBI NIH HHS R01 HL151421NHLBI NIH HHS R01 HL152728NHLBI NIH HHS R01 HL166992NHLBI NIH HHS R01 HL171213NHLBI NIH HHS T32 HL007427NHLBI NIH HHS U01 HL089856NHLBI NIH HHS U01 HL089897NHLBI NIH HHS UH3 HL155806
6 · The paper itselfAbstract
Importance: Obtaining spirometry requires repeated testing and using the maximal values based on quality control criteria. Whether the suboptimal efforts are useful for the prediction of respiratory outcomes is not clear. Objective: To determine whether a machine learning model could predict respiratory outcomes and mortality based on suboptimal spirometry. Design: Observational cohorts (UK Biobank and COPDGene). Setting: Multi-center; population, and disease-enriched. Participants: UK aged 40-69; US aged 45-80, >10 pack-years smoking, without respiratory diseases other than COPD or asthma. Exposures: Raw spirograms (volume-time). Main outcomes and measures: To create a combined representation of lung function we implemented a contrastive learning approach, Results: We trained Spiro-CLF on 940,705 volume-time curves from 352,684 UKB participants with 2-3 spirometry efforts per individual (66.7% with 3 efforts) and at least one QC-passing spirometry effort. Of all spirometry efforts, 61.6% were suboptimal (37.5% submaximal and 24.1% QC-failing). In the UK Biobank, Spiro-CLF using QC-failing and submaximal efforts predicted FEV Conclusions and Relevance: A machine-learning model can predict respiratory phenotypes using suboptimal spirometry; results from all spirometry efforts may contain valuable data. Additional studies are required to determine performance and utility in specific clinical scenarios.
Identifiers
PMID40630516
PMCPMC12236919
What Socratic holds
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