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

Официальное название

Development and Validation of a Wearable Device-Based Early Warning Model for Postoperative Complications in Thoracic Surgery: A Retrospective and Prospective Cohort Study

Обзор

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.

Подробное описание

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.

Вмешательства

• Устройство Wearable device-based perioperative monitoring
  Participants will wear a study wearable device that continuously collects physiological and behavioral data, including heart rate, oxygen saturation, skin temperature, activity, sleep, and wearing status, from hospital admission to approximately 30 days after discharge. The device is used for monitoring and data collection and does not change routine clinical care.

Первичные конечные точки

• Area under the receiver operating characteristic curve of the wearable device-based early warning model for 30-day postoperative complications [Срок оценки: From the day of surgery to 30 days after surgery]

Вторичные конечные точки (3)

• Warning lead time before clinically confirmed postoperative complications [Срок оценки: From the day of surgery to 30 days after surgery]
• Sensitivity of the wearable device-based early warning model for 30-day postoperative complications [Срок оценки: From the day of surgery to 30 days after surgery]
• Specificity of the wearable device-based early warning model for 30-day postoperative complications [Срок оценки: From the day of surgery to 30 days after surgery]

Критерии участия

Критерии включения

• Age 18 years or older.
• Hospitalized in the Department of Thoracic Surgery of Tongji Hospital and scheduled to undergo thoracic surgery.
• Able to wear the study-designated wearable device after hospital admission and expected to continue wearing the device and/or uploading data within 30 days after discharge.

Критерии исключения

• Patients or family members are unwilling to wear the wearable device or unable to meet the required wearing time.
• Severe or unstable psychiatric disease, such as severe depression or schizophrenia.
• Pregnancy or lactation.
• Allergy to the watch strap material or local skin conditions that prevent wearing the device.
• Unable to complete follow-up within 30 days after discharge.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 1 центр

• Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Ухань

Идентификаторы

NCT: NCT07646730 · TJ-IRB202512090 · TJ-IRB202512090

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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