Improved Precision of COPD Exacerbation Detection in Night-Time Cough Monitoring

Area of Science:

• Respiratory Medicine
• Digital Health
• Biomedical Engineering

Background:

• Precision healthcare applications benefit from targeted interventions.
• An existing alert system for COPD exacerbations shows improved efficacy with patient stratification.
• Cough data analysis is key to refining this alert mechanism.

Purpose of the Study:

• To analyze cough data from a stratified cohort for improved precision in COPD exacerbation alerts.
• To explore options and feasibility for enhancing an existing COPD exacerbation alert system.

Main Methods:

• An augmented alert system was developed, complementing the existing one to capture rapid cough trend changes.
• A post hoc analysis was conducted on the augmented system using trend data.
• Meaningfulness of trend data for patients and caregivers was assessed.

Main Results:

• Stratification reduced missed exacerbations.
• The augmented alert system enhanced sensitivity and early detection of AE-COPD.
• The combined approach achieved 86% sensitivity with a 1.5% false alert rate, using interpretable, rule-based signals.

Conclusions:

• The augmented, trend-based cough alert system shows potential for precise AE-COPD detection.
• Further validation is required as the system was designed and tested on the same data.
• Interpretable signals may aid patient self-management and caregiver insights.