Chronic obstructive pulmonary disease - Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors

Aug 3, 2026

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Digital inhalers can capture daily inhaler-use signals that reflect early deterioration and support personalized forecasting of impending chronic obstructive pulmonary disease (COPD) exacerbations. This study evaluated whether machine learning models trained on data from an integrated digital dry powder inhaler could predict impending events for individual patients. In a 12-week open-label study, patients recorded reliever dosing with sensor-derived timestamped and inhalation-quality measures. A gradient-boosted model predicted exacerbations within 5 days with AUROC 0.77, using baseline inhalation parameters and their pre-exacerbation changes.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Purpose

Patients and methods

Results

Conclusion

Introduction

Background on COPD and exacerbations

Role of inhalation technique measures

Digital inhaler data and early deterioration

Chronic obstructive pulmonary disease Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors Laurie D Snyder ,1 Michael DePietro,2 Michael Reich,3 Megan L Neely,4,5 Njira Lugogo,6 Roy Pleasants,7 Thomas Li,2 Lena Granovsky,3 Randall Brown,2 Guilherme Safioti2 To cite: Snyder LD, DePietro M, Reich M, et al. Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors. BMJ Open Respir Res 2025;12:e002577 . doi:10.1136/ bmjresp-2024-002577 ► Additional supplemental material is published online only. To view, please visit the journal online ([https://doi](https://doi). org/10.1136/bmjresp-2024- 002577) . Received 16 May 2024 Accepted 1 April 2025 © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group. 1Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA 2Teva Branded Pharmaceutical Products R&D Inc, Parsippany, New Jersey, USA 3Teva Pharmaceutical Industries Ltd, Tel Aviv, Israel 4Duke Clinical Research Institute, Durham, North Carolina, USA 5Duke University Medical Center, Durham, North Carolina, USA 6University of Michigan, Ann Arbor, Michigan, USA 7UNC, Chapel Hill, North Carolina, USA Correspondence to Dr Laurie D Snyder; [laurie.snyder@duke.edu](laurie.snyder@duke.edu) ABSTRACT Purpose By using data obtained with digital inhalers, machine learning models have the potential to detect early signs of deterioration and predict impending exacerbationsof chronic obstructive pulmonary disease (COPD) for individual patients. This analysis aimed to determine if a machine learning algorithm capable of predicting impending exacerbations could be developed using data from an integrated digital inhaler. Patients and methods A 12-week, open-label clinical study enrolled patients (≥40 years old) with COPD to use ProAir Digihaler, a digital dry powder inhaler with integrated sensors, to deliver their reliever medication (albuterol, 90 µg/dose; 1–2 inhalations every 4 hours, as needed) . The Digihaler recorded inhaler use through timestamps, peak inspiratory flow (PIF), inhalation volume, inhalation duration, and time to PIF throughout the study. By applying machine learning methodology to data downloaded from the inhalers after study completion, along with clinical and demographic information, a model predictive of impending exacerbations was generated. Results The predictive analysis included 336 patients, 98 of whom experienced a total of 111 exacerbations. PIFand inhalation volume were observed to decline in the days preceding an exacerbation. Using gradient-boosting trees with data from the Digihaler and baseline patient characteristics, the machine learning model was able to predict an exacerbation over the following 5 days with a receiver operating characteristic area under curve of 0.77 (95% CI: 0 .71–0.83) . Features of the model with the highest weight were baseline inhalation parameters and changes in inhalation parameters before an exacerbation compared with baseline. Conclusion We demonstrated the development of a proof-of-concept machine learning model predictive of impending COPD exacerbations using data from the integrated digital reliever inhaler. This approach may potentially support patient monitoring, help improve disease management, and enable pre-emptive interventions to minimise exacerbations. Clinical trial registration number NCT03256695 . INTRODUCTION Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide.1 COPD exacerbations, especially when WHAT IS ALREADY KNOWN ON THIS TOPIC | ⇒ Data that can be captured and recorded by a digital inhaler may be able to be used to predict impending exacerbations of chronic respiratory disease, as has previously been demonstrated in asthma.
WHAT THIS STUDY ADDS | | --- | | ⇒ Patien

FAQs

What was the study trying to predict in COPD patients?

The study aimed to develop a machine learning algorithm that predicts impending COPD exacerbations for individual patients before they occur.

What data were used to train the predictive model?

The model used sensor-derived inhaler-use data from an integrated digital inhaler, including timestamps and inhalation parameters, together with clinical and demographic baseline information.

How accurate was the prediction within the specified time window?

Using gradient-boosting trees, the model predicted an exacerbation over the following 5 days with an AUROC of 0.77 (95% CI 0.71–0.83).

Which model features contributed most to predictions?

The highest-weight features were baseline inhalation parameters and changes in inhalation parameters in the days before an exacerbation compared with baseline.

Aug 3, 2026

Upload by Quinn