Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors.

ABSTRACT:

Purpose

By using data obtained with digital inhalers, machine learning models have the potential to detect early signs of deterioration and predict impending exacerbations of 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 (

SUBMITTER: Snyder LD 

PROVIDER: S-EPMC12083419 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature