The overarching goal of this research is to use machine learning analysis of high-resolution data-collected by wearable technology-of cardiothoracic surgical patients to assess recovery and detect complications at their earliest stage
This is a single-center non-randomized prospective cohort study using wearable devices in cardiothoracic surgery patients to detect post-operative complications. Patients undergoing cardiothoracic surgery who meet the inclusion and exclusion criteria will be enrolled consecutively with verbal informed consent from the time this protocol is approved by the IRB until 1,200 subjects are enrolled. At \~30 days preoperatively the subjects will have a wearable device (such as a Fitbit) placed on their wrist and will wear the device until \~180 days post-operatively. This device will wirelessly transmit data regarding activity and sleep quality to a smartphone application for the duration of wear and data will be analyzed by our collaborators at Case Western Reserve University.
Study Type
OBSERVATIONAL
Enrollment
1,200
A Wearable Device will be placed on the wrist of the patient \~30 days prior to the patient's scheduled surgery, removed during the operation, and replaced for \~180 days post-operatively. The device will record activity in terms of steps, sleep quality, heart rate, etc.
Massachusetts General Hospital
Boston, Massachusetts, United States
RECRUITINGEarly detection of postoperative complications using machine learning analysis of patient biometric data.
Proportion of complications detected by the machine learning algorithm.
Time frame: Four Years
Prediction of the quality of postoperative recovery using pre- and intraoperative data.
Proportion of patients whose quality of postoperative recovery is correctly predicted by the machine learning algorithm.
Time frame: Four Years
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