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

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 (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. PIF and 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.