Wearable Device-Based Early Warning of Postoperative Complications in Thoracic Surgery

Perioperative Monitoring, Postoperative Complications, Thoracic Surgery

Wearable Device, Early Warning Model, Postoperative Complications, Thoracic Surgery

After thoracic surgery, some patients may develop complications such as lung infection, abnormal heart rhythm, fluid around the lung, prolonged air leak, wound infection, emergency department visits, or hospital readmission. These problems may not be found early if monitoring is only done during routine vital sign checks or follow-up visits. This study will evaluate whether data collected by a wearable device can help identify early warning signs of postoperative complications in patients undergoing thoracic surgery. The wearable device will collect information such as heart rate, oxygen level, skin temperature, physical activity, sleep, and wearing status. The study includes two parts. First, the researchers will review previously collected wearable device and medical record data to develop an early warning model. Second, new patients undergoing thoracic surgery will wear the device from hospital admission until about 30 days after discharge. The model will then be tested to see how well it predicts complications that require medical intervention within 30 days after surgery. The main goal is to evaluate how accurately the wearable device-based model can identify patients who develop postoperative complications and how early the model can provide a warning before the complication is clinically confirmed.

Postoperative complications are an important concern after thoracic surgery. Conventional perioperative monitoring mainly relies on intermittent vital sign measurements, nursing assessments, physician rounds, and routine post-discharge follow-up. This approach may miss short-lasting, nighttime, or activity-related physiological changes that occur before a complication becomes clinically apparent. Wearable devices can continuously collect physiological and behavioral information, including heart rate, oxygen saturation, skin temperature, activity, sleep, and wearing status. Combining these data with electronic medical record information may help identify early changes associated with postoperative complications. This study uses a retrospective and prospective cohort design. In the retrospective stage, previously collected wearable device data and electronic medical record data from thoracic surgery patients will be analyzed. The researchers will evaluate the agreement between wearable device measurements and routine clinical vital signs, describe physiological signal patterns before postoperative complications, and develop a preliminary early warning model. In the prospective validation stage, newly enrolled patients scheduled for thoracic surgery will wear the study wearable device from hospital admission to approximately 30 days after discharge. The early warning model developed from the retrospective data will be applied prospectively using predefined parameters. Model-generated warnings will be recorded. The occurrence of postoperative complications will be verified through inpatient medical records and follow-up after discharge. The primary reference outcome is postoperative complications requiring medical intervention within 30 days after surgery. These complications may include pulmonary infection, pleural effusion requiring treatment, prolonged air leak, wound infection, postoperative arrhythmia, emergency department visit, or readmission. Model performance will be evaluated using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and warning lead time. Wearable device adherence, data completeness, and the added predictive value of wearable device-derived features beyond routine clinical variables will also be assessed.

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