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. 2025 Jan 17;80(2):105-108.
doi: 10.1136/thorax-2024-221928.

Association between automatic AI-based quantification of airway-occlusive mucus plugs and all-cause mortality in patients with COPD

Tjeerd van der Veer  1   2 , Eleni-Rosalina Andrinopoulou  3   4 , Gert-Jan Braunstahl  5   6 , Jean Paul Charbonnier  7 , Victor Kim  8 , Rudolfs Latisenko  9 , David A Lynch  10 , Harm Tiddens  7   11
Affiliations

Association between automatic AI-based quantification of airway-occlusive mucus plugs and all-cause mortality in patients with COPD

Tjeerd van der Veer et al. Thorax. .

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.

Trial registration: ClinicalTrials.gov NCT00608764.

Keywords: COPD epidemiology; Imaging/CT MRI etc; Smoking.

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Conflict of interest statement

Competing interests: TvdV: no conflicts of interest involving the work under consideration for publication, no relevant financial activities outside the submitted work, no other relationships or activities that readers could perceive to have influenced or that give the appearance of potentially influencing the current manuscript. E-RA: no conflicts of interest, no relevant financial activities or other relevant relationships or activities regarding the work under consideration. G-JB: honoraria for lectures and consultancy from GSK, AstraZeneca, Novartis and Sanofi Genzyme, as well as research grants from Sanofi Genzyme, GSK and AstraZeneca, not related to the work under consideration. JPC: employee and shareholder at Thirona. VK: no conflicts of interest, no relevant financial activities or other relevant relationships or activities regarding the work under consideration. RL: senior deep learning engineer at Thirona. DAL: no conflicts of interest, no relevant financial activities or other relevant relationships or activities regarding the work under consideration. HT: received in the last 3 years multiple grants from the following public and institutional grant institutions for lung structure and function research: NHMRC, NIH, CFF, ECFS, IMI and ErasmusMC Sophia Foundation; received unconditional grants for investigator-initiated research from Novartis and Insmed. Acted as consultant for Insmed, Thirona, Neupharma and Boehringer Ingelheim. Chief medical officer for Thirona since April 2022. Vice-chair and faculty for the ADVANCE course sponsored by Vertex.

Figures

Figure 1.
Figure 1.
Mucus Plug Detection and GOLD Class Distribution a. LungQ reconstruction of the bronchial tree with detected mucus plugs indicated in red and the corresponding transversal CT images. b. Distribution of participants’ GOLD class for segment scores 0, 1–2 and ≥3 mucus-obstructed segment categories. c. Scatterplot of total mucus plug counts and GOLD classes. NB. total plug count can be >2 also at 1–2 bronchial segments affected, as multiple plugs can be found in the same segment.
See this image and copyright information in PMC

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