The Role of Wearable Devices in Cardiothoracic Surgery: Predicting and Detecting Early Postoperative Complications

Recruiting

• Conditions: Surgery--Complications

• Registration Number: NCT04824066
• Lead Sponsor: Massachusetts General Hospital

Brief Summary

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

Detailed Description

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 Timeline

• Study First Submitted: 2021-03-11
• Study First Submitted QC: 2021-03-26
• Study First Posted: 2021-04-01
• Study Start: 2021-07-10
• Status Verified: 2024-11
• Last Update Submitted: 2024-11-18
• Last Update Posted: 2024-11-19
• Primary Completion: 2026-01-01
• Study Completion: 2027-06-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1200

Inclusion Criteria

1. Age 18 years or older undergoing cardiothoracic surgery that is male or a non-pregnant female and are amenable to using one of the wearable devices of interest (Fitbit, iWatch, Biostrap).
2. Individuals willing to provide informed consent and who have capacity for all study procedures

Exclusion Criteria

1. Individuals with mental incapacity and/or cognitive impairment that would preclude adequate understanding of, or cooperation with the study protocol.
2. Any pregnant participant.
3. Severe irreversible pulmonary hypertension.
4. Congenital heart disease
5. Chronic renal insufficiency or undergoing chronic renal replacement therapy
6. Liver cirrhosis

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Early detection of postoperative complications using machine learning analysis of patient biometric data.	Four Years	Proportion of complications detected by the machine learning algorithm.
Prediction of the quality of postoperative recovery using pre- and intraoperative data.	Four Years	Proportion of patients whose quality of postoperative recovery is correctly predicted by the machine learning algorithm.

Trial Locations

Locations (1)

Massachusetts General Hospital; 🇺🇸 Boston, Massachusetts, United States