Journal article
Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study
The Lancet. Digital health, Vol.5(2), pp.e83-e92
02/2023
DOI: 10.1016/S2589-7500(22)00232-1
PMCID: PMC9896720
PMID: 36707189
Abstract
Quantitative CT is becoming increasingly common for the characterisation of lung disease; however, its added potential as a clinical tool for predicting severe exacerbations remains understudied. We aimed to develop and validate quantitative CT-based models for predicting severe chronic obstructive pulmonary disease (COPD) exacerbations.
We analysed the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS) cohort, a multicentre study done at 12 clinical sites across the USA, of individuals aged 40-80 years from four strata: individuals who never smoked, individuals who smoked but had normal spirometry, individuals who smoked and had mild to moderate COPD, and individuals who smoked and had severe COPD. We used 3-year follow-up data to develop logistic regression classifiers for predicting severe exacerbations. Predictors included age, sex, race, BMI, pulmonary function, exacerbation history, smoking status, respiratory quality of life, and CT-based measures of density gradient texture and airway structure. We externally validated our models in a subset from the Genetic Epidemiology of COPD (COPDGene) cohort. Discriminative model performance was assessed using the area under the receiver operating characteristic curve (AUC), which was also compared with other predictors, including exacerbation history and the BMI, airflow obstruction, dyspnoea, and exercise capacity (BODE) index. We evaluated model calibration using calibration plots and Brier scores.
Participants in SPIROMICS were enrolled between Nov 12, 2010, and July 31, 2015. Participants in COPDGene were enrolled between Jan 10, 2008, and April 15, 2011. We included 1956 participants from the SPIROMICS cohort who had complete 3-year follow-up data: the mean age of the cohort was 63·1 years (SD 9·2) and 1017 (52%) were men and 939 (48%) were women. Among the 1956 participants, 434 (22%) had a history of at least one severe exacerbation. For the CT-based models, the AUC was 0·854 (95% CI 0·852-0·855) for at least one severe exacerbation within 3 years and 0·931 (0·930-0·933) for consistent exacerbations (defined as ≥1 acute episode in each of the 3 years). Models were well calibrated with low Brier scores (0·121 for at least one severe exacerbation; 0·039 for consistent exacerbations). For the prediction of at least one severe event during 3-year follow-up, AUCs were significantly higher with CT biomarkers (0·854 [0·852-0·855]) than exacerbation history (0·823 [0·822-0·825]) and BODE index 0·812 [0·811-0·814]). 6965 participants were included in the external validation cohort, with a mean age of 60·5 years (SD 8·9). In this cohort, AUC for at least one severe exacerbation was 0·768 (0·767-0·769; Brier score 0·088).
CT-based prediction models can be used for identification of patients with COPD who are at high risk of severe exacerbations. The newly identified CT biomarkers could potentially enable investigation into underlying disease mechanisms responsible for exacerbations.
National Institutes of Health and the National Heart, Lung, and Blood Institute.
Details
- Title: Subtitle
- Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study
- Creators
- Muhammad F A Chaudhary - University of IowaEric A HoffmanJunfeng GuoAlejandro P Comellas - University of IowaJohn D Jr NewellPrashant NagpalSpyridon Fortis - University of IowaGary E ChristensenSarah E Gerard - University of IowaYue Pan - University of IowaDi Wang - University of IowaFereidoun Abtin - David Geffen School of Medicine at UCLAIgor Z Barjaktarevic - David Geffen School of Medicine at UCLAR Graham Barr - Columbia UniversitySurya P Bhatt - University of Alabama at BirminghamSandeep Bodduluri - University of Alabama at BirminghamChristopher B Cooper - David Geffen School of Medicine at UCLALisa Gravens-Mueller - Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USAMeiLan K Han - University of Michigan–Ann ArborFernando J Martinez - Weill Cornell MedicineElla A Kazerooni - University of Michigan–Ann ArborMartha G Menchaca - University of Illinois at ChicagoVictor E Ortega - Department of Internal Medicine, Division of Respiratory Medicine, Mayo Clinic, Scottsdale, AZ, USARobert Paine Iii - University of UtahJoyce D Schroeder - University of UtahPrescott G Woodruff - University of California, San FranciscoJoseph M Reinhardt
- Resource Type
- Journal article
- Publication Details
- The Lancet. Digital health, Vol.5(2), pp.e83-e92
- DOI
- 10.1016/S2589-7500(22)00232-1
- PMID
- 36707189
- PMCID
- PMC9896720
- ISSN
- 2589-7500
- eISSN
- 2589-7500
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health
- Language
- English
- Date published
- 02/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Pulmonary, Critical Care, and Occupational Medicine; ICTS; Radiation Oncology; The Iowa Institute for Biomedical Imaging; Advanced Pulmonary Physiomic Imaging Laboratory; Holden Comprehensive Cancer Center; Internal Medicine
- Record Identifier
- 9984362332602771
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