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Disease Progression Modeling in Chronic Obstructive Pulmonary Disease.
Young, Alexandra L; Bragman, Felix J S; Rangelov, Bojidar; Han, MeiLan K; Galbán, Craig J; Lynch, David A; Hawkes, David J; Alexander, Daniel C; Hurst, John R.
Afiliación
  • Young AL; Centre for Medical Image Computing.
  • Bragman FJS; Department of Computer Science.
  • Rangelov B; Department of Medical Physics and Biomedical Engineering, and.
  • Han MK; Centre for Medical Image Computing.
  • Galbán CJ; UCL Respiratory, University College London, London, United Kingdom.
  • Lynch DA; Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, and.
  • Hawkes DJ; Centre for Medical Image Computing.
  • Alexander DC; UCL Respiratory, University College London, London, United Kingdom.
  • Hurst JR; Artificial Medical Intelligence Group, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Am J Respir Crit Care Med ; 201(3): 294-302, 2020 02 01.
Article en En | MEDLINE | ID: mdl-31657634
ABSTRACT
Rationale The decades-long progression of chronic obstructive pulmonary disease (COPD) renders identifying different trajectories of disease progression challenging.

Objectives:

To identify subtypes of patients with COPD with distinct longitudinal progression patterns using a novel machine-learning tool called "Subtype and Stage Inference" (SuStaIn) and to evaluate the utility of SuStaIn for patient stratification in COPD.

Methods:

We applied SuStaIn to cross-sectional computed tomography imaging markers in 3,698 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-4 patients and 3,479 controls from the COPDGene (COPD Genetic Epidemiology) study to identify subtypes of patients with COPD. We confirmed the identified subtypes and progression patterns using ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) data. We assessed the utility of SuStaIn for patient stratification by comparing SuStaIn subtypes and stages at baseline with longitudinal follow-up data.Measurements and Main

Results:

We identified two trajectories of disease progression in COPD a "Tissue→Airway" subtype (n = 2,354, 70.4%), in which small airway dysfunction and emphysema precede large airway wall abnormalities, and an "Airway→Tissue" subtype (n = 988, 29.6%), in which large airway wall abnormalities precede emphysema and small airway dysfunction. Subtypes were reproducible in ECLIPSE. Baseline stage in both subtypes correlated with future FEV1/FVC decline (r = -0.16 [P < 0.001] in the Tissue→Airway group; r = -0.14 [P = 0.011] in the Airway→Tissue group). SuStaIn placed 30% of smokers with normal lung function at elevated stages, suggesting imaging changes consistent with early COPD. Individuals with early changes were 2.5 times more likely to meet COPD diagnostic criteria at follow-up.

Conclusions:

We demonstrate two distinct patterns of disease progression in COPD using SuStaIn, likely representing different endotypes. One third of healthy smokers have detectable imaging changes, suggesting a new biomarker of "early COPD."
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Progresión de la Enfermedad / Enfermedad Pulmonar Obstructiva Crónica / Modelos Teóricos Tipo de estudio: Prognostic_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Am J Respir Crit Care Med Asunto de la revista: TERAPIA INTENSIVA Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X / Progresión de la Enfermedad / Enfermedad Pulmonar Obstructiva Crónica / Modelos Teóricos Tipo de estudio: Prognostic_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Am J Respir Crit Care Med Asunto de la revista: TERAPIA INTENSIVA Año: 2020 Tipo del documento: Article