Short-term prediction of COPD exacerbations based on wearable vital sign monitoring.

Abstract

Early detection of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) remains a critical challenge in COPD management. This study introduces the Bora Vital Sign Standard Score (BVS3), a novel unsupervised statistical score that predicts AECOPD using vital signs collected at home through remote patient monitoring, and retrospectively evaluates its predictive performance in identifying AECOPD ahead of clinician-defined episodes. The eMEUSE-SANTÉ clinical trial (NCT04963192) involved 220 COPD patients who were remotely monitored for six months using a CE-certified (Class IIa) connected wristband measuring oxygen saturation (SpO₂), breathing rate (BR) and heart rate (HR). A total of 42 physician validated exacerbations of COPD with no missing remote monitoring data were documented in 39 patients at a general hospital. Continuous 24-hour monitoring of vital signs using a connected wristband was well accepted over the long term, with a median adherence of 86% indicating strong patient compliance. The BVS3 risk score achieved excellent predictive performance, with an AUC of 0.88 (95% CI 0.83 - 0.92) for moderate and severe AECOPD combined. The BVS3 score anticipated exacerbations an average of 4.4 ± 3.1 days before clinical confirmation, with an overall accuracy of 84.8% and sensitivity of 74% with 85% specificity. Individual Z-scores for heart rate (z-HR), breathing rate (z-BR) and oxygen saturation (z-SpO2) showed specific predictive capabilities for moderate and severe events, yielding AUCs of 0.83, 0.82 and 0.71 respectively, but with inferior performances compared with the combination of the 3 vital signs Z-scores. These results demonstrate that integrating passive remote monitoring with unsupervised statistical modeling provides a scalable, high-compliance approach to AECOPD detection. The interpretable BVS3 risk score achieves good accuracy and anticipation for AECOPD prediction with minimal patient burden. By enabling earlier intervention, this end-to-end digital solution could significantly improve patient outcomes through proactive disease management.