Abstract
OBJECTIVE: The goal of this study was to develop, implement, and test an automated decision system to provide early detection of clinically important bronchopulmonary events in a population of lung transplant recipients following a home monitoring protocol. SUBJECTS AND METHODS: Spirometry and other clinical data were collected daily at home by lung transplant recipients and transmitted weekly to the study data center. Decision rules were developed using wavelet analysis of declines in spirometry and increases in respiratory symptoms from a learning set of patient home data and validated with an independent patient set. RESULTS: Using forced expiratory volume in 1 s or symptoms, the detection captured the majority of events (sensitivity, 80-90%) at an acceptable level of false alarms. On average, detections occurred 6.6-10.8 days earlier than the known event records. CONCLUSIONS: This approach is useful for early discovery of pulmonary events and has the potential to decrease the time required for humans to review large amount of home monitoring data to discover relatively infrequent but clinically important events.