A digital therapy for proactively managing exacerbations and delivering therapeutic benefit to patients with moderate to severe asthma

Abstract

Background: Over 30% of patients with asthma have suboptimal control and frequently experience acute/sub-acute exacerbations. Improved patient-centered tools are needed to facilitate early identification and management of these events. To address this gap, we developed an on-demand asthma triage application which uses machine learning algorithms to enable real-time triage advice delivered directly to the patient.

Objectives: Study aims were to test algorithm accuracy in simulated patient cases and assess therapeutic benefit to real-life asthmatics in an observational trial.

Methods: The application was trained on opinions of six pulmonologists triaging over 1900 simulated cases covering the clinically relevant health variable space. The algorithm outputs 1) presence of exacerbation and 2) a triage recommendation from 4 choices (no action, continue usual treatment, call MD, and go to ER). Initial validation of the algorithm’s accuracy was through comparison to consensus (mode) of 8 pulmonologists. The algorithm was subsequently embedded in a mobile phone, and an observational trial on asthma control, anxiety, quality of life, and user experience was conducted.

Results and Conclusion: Using physician consensus as a standard, the algorithm correctly assigned the exacerbation and triage classes with accuracy of 96% and 84% respectively, better than all 8 MDs. The algorithm also demonstrated superior accuracy and sensitivity in asthma scenarios requiring emergency care. Clinical trial data indicates statistically significant improvements in asthma control, quality of life and anxiety. Further clinical testing is needed to confirm these promising initial findings.