Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Dec 8, 2024
Date Accepted: Apr 7, 2025
Deep learning-based COPD exacerbation prediction using flow-volume and volume-time curve imaging: retrospective cohort study
ABSTRACT
Background:
Chronic obstructive pulmonary disease (COPD) exacerbations are key determinants of disease progression, yet existing predictive models relying mainly on spirometric measurements, such as forced expiratory volume in one second, offer limited predictive accuracy for future exacerbations.
Objective:
To determine whether a predictive model that integrates clinical data and spirometry images with the use of artificial intelligence improves accuracy in predicting moderate-to-severe and severe acute exacerbation of COPD (AE-COPD) events compared to a clinical-only model.
Methods:
A retrospective cohort study was conducted using COPD registry data from two teaching hospitals, covering January 2004 to December 2020. The study included a total of 10,492 COPD cases, divided into a development cohort (6,870 cases) and an external validation cohort (3,622 cases). The AI-enhanced model (AI-PFT-Clin) utilized a combination of clinical variables (e.g., history of AE-COPD, dyspnea, inhaled treatments) and spirometry image data (flow-volume loop and volume-time curves). In contrast, the Clin model used only clinical variables. The primary outcomes were moderate-to-severe and severe AE-COPD events within a year of spirometry.
Results:
In the external validation cohort, the AI-PFT-Clin model outperformed the Clin model, showing the area under the receiver operating characteristic curve (AUROC) of 0.755 vs. 0.730 (P<0.05) for moderate-to-severe AE-COPD and 0.713 vs. 0.675 (P<0.05) for severe AE-COPD. The AI-PFT-Clin model demonstrated reliable predictive capability across subgroups, including younger patients and those without prior exacerbations. Higher AI-PFT-Clin scores correlated with elevated AE-COPD risk (adjusted HR for Q4 vs. Q1: 4.21, P<.001), with sustained predictive stability over a 10-year follow-up period.
Conclusions:
The AI-PFT-Clin model, by integrating clinical data with spirometry images, offers enhanced predictive accuracy for AE-COPD events compared to a clinical-only approach. This model may inform early intervention strategies, particularly in populations traditionally seen as lower risk, supporting improved management of COPD through tailored patient care.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.