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Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
Published on: November 11, 2020
Individualized prediction of lung-function decline in chronic obstructive pulmonary disease
Zafar Zafari1, Don D Sin2, Dirkje S Postma1
1Centre for Clinical Epidemiology and Evaluation (Zafari, Bryan, Sadatsafavi), Vancouver Coastal Health Research Institute, University of British Columbia; Centre for Heart and Lung Innovation (Sin, Man, McManus, Hollander), St. Paul's Hospital; Institute for Heart and Lung Health (Sin, Man, McManus, Hollander, Sadatsafavi), University of British Columbia; Division of Respiratory Medicine (Sin, Lam, Man, Sadatsafavi), Department of Medicine, University of British Columbia, Vancouver, BC; University Medical Center Groningen (Postma, Vonk), Groningen Research Institute for Asthma and COPD (GRIAC), University of Groningen, Groningen; University Medical Center Groningen (Postma), Department of Pulmonary Diseases, University of Groningen, Groningen, the Netherlands; Department of Respiratory Medicine and Allergology (Löfdahl), Lund University, Lund, Sweden; Department of Epidemiology (Vonk), University of Groningen, University Medical Center Groningen, the Netherlands; School of Population and Public Health (Bryan), University of British Columbia, Vancouver, BC; Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences (Khakban), University of British Columbia, Vancouver, BC; David Geffen School of Medicine at UCLA (Tashkin), Los Angeles, Calif.; Johns Hopkins University School of Medicine (Wise), Baltimore, Md.; University of Minnesota School of Public Health (Connett), Minneapolis, Minn.; PROOF Centre for Excellence (McManus, Ng, Hollander), Vancouver, BC; Department of Community Health Sciences (Tammemagi), Brock University, St. Catharines, Ont.
This study developed an individualized prediction model for lung function decline in smokers with COPD. Easily measured clinical variables explain much of the variation in forced expiratory volume at 1 second (FEV1) decline.
Area of Science:
- Pulmonary Medicine
- Biostatistics
- Clinical Trials
Background:
- Chronic obstructive pulmonary disease (COPD) exhibits significant variability in lung function decline among individuals.
- Accurate prediction of forced expiratory volume at 1 second (FEV1) decline is crucial for managing mild-to-moderate COPD, particularly in current smokers.
Purpose of the Study:
- To develop and validate an individualized prediction model for FEV1 decline in current smokers with mild-to-moderate COPD.
- To identify key clinical variables that explain individual differences in the rate of lung function decline.
Main Methods:
- Utilized mixed-effects regression models on data from the Lung Health Study to predict FEV1 over 11 years.
- Incorporated individual variability by allowing regression coefficients to vary across participants.
- Validated the prediction equations using two independent COPD cohorts.
Main Results:
- The model, created using data from 5594 patients, explained 88% of the variation in follow-up FEV1.
- Significant individual variability in FEV1 decline was observed; the 95% interval for annual decline was -124 to -15 mL/yr for smokers.
- The prediction equations demonstrated robust performance in external validation datasets, with a C statistic of 0.90 for predicting severity grades.
Conclusions:
- A significant portion of individual variation in FEV1 decline can be attributed to easily measurable clinical variables.
- The developed individualized prediction model offers a valuable tool for forecasting future lung health in patients with mild-to-moderate COPD.
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