Association between automatic AI-based quantification of airway-occlusive mucus plugs and all-cause mortality in patients with COPD
Thorax, vol. 80, pp. 105–108
Abstract
In this cohort study involving 9399 current and former smokers from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease study, we assessed the relationship between artificial intelligence-quantified mucus plugs on chest CTs and all-cause mortality. Our results revealed a significant positive association, particularly for those with COPD GOLD stages 1-4, with HRs of 1.18 for 1-2 mucus-obstructed bronchial segments and 1.27 for ≥3 obstructed segments. This corroborates previous visual mucus plug counting research and demonstrates the relevance of mucus plugs in COPD pathology and as a marker for risk assessment. Automated mucus plug quantification methods may provide an efficient tool for both clinical evaluations and research.
Authors 8
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Leiden University Medical Center · Erasmus MC
Affiliation as printed
Respiratory Medicine, Erasmus MC, Rotterdam, Netherlands t.vanderveer@lumc.nl
Respiratory Medicine, Leiden University Medical Center, Leiden, Netherlands
Respiratory Medicine, Erasmus MC, Rotterdam, Netherlands
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Affiliation as printed
Biostatistics, Erasmus Medical Center, Rotterdam, Netherlands
Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
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Erasmus MC · Sint Franciscus Gasthuis
Affiliation as printed
Pulmonology, Franciscus Gasthuis & Vlietland, Rotterdam, Netherlands
Respiratory Medicine, Erasmus MC, Rotterdam, Netherlands
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Affiliation as printed
Thirona BV, Nijmegen, Netherlands
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Affiliation as printed
Division of Pulmonary and Critical Care Medicine, Temple University School of Medicine, Philadelphia, Pennsylvania, USA
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Affiliation as printed
Respiratory Medicine, Thirona BV, Nijmegen, Netherlands
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Affiliation as printed
Medicine, National Jewish Health, Denver, Colorado, USA
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Affiliation as printed
Pediatric Pulmonology, Erasmus Medical Center, Rotterdam, Netherlands
Thirona BV, Nijmegen, Netherlands
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