Surgery--Complications Clinical Trial
Official title:
The Role of Wearable Devices in Cardiothoracic Surgery: Predicting and Detecting Early Postoperative Complications
NCT number | NCT04824066 |
Other study ID # | 2020P002984 |
Secondary ID | |
Status | Recruiting |
Phase | |
First received | |
Last updated | |
Start date | July 10, 2021 |
Est. completion date | June 30, 2027 |
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
Status | Recruiting |
Enrollment | 1200 |
Est. completion date | June 30, 2027 |
Est. primary completion date | January 1, 2026 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | 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 |
Country | Name | City | State |
---|---|---|---|
United States | Massachusetts General Hospital | Boston | Massachusetts |
Lead Sponsor | Collaborator |
---|---|
Massachusetts General Hospital | Case Western Reserve University |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Early detection of postoperative complications using machine learning analysis of patient biometric data. | Proportion of complications detected by the machine learning algorithm. | Four Years | |
Primary | 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. | Four Years |
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