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| Name | Class |
|---|---|
| Case Western Reserve University | OTHER |
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The overarching goal of this research is to use machine learning analysis of high-resolution data-collected by wearable technology-to predict complications and poor recovery in patients undergoing treatment for benign or malignant conditions.
This is a multi-center non-randomized prospective cohort study using wearable devices and machine learning to predict complications and poor recovery in patients undergoing treatment for benign or malignant conditions.
Patients 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 2,400 subjects are enrolled. At ~30 days before treatment the subjects will have a wearable device (such as a Fitbit) placed on their wrist and will wear the device for up to 5 years following treatment. 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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Treatment Group | Adults patients who are scheduled to undergo treatment for a benign or malignant condition and meet the inclusion and exclusion criteria. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Device: Wearable Device | Device | A Wearable Device will be placed on the wrist of the patient ~30 days prior to the patient's scheduled treatment and for up to 5 years following treatment. The device will record activity in terms of steps, sleep quality, heart rate, etc. |
| Measure | Description | Time Frame |
|---|---|---|
| Early detection of complications and adverse events using machine learning analysis of patient biometric data. | Proportion of complications detected by the machine learning algorithm. | Five Years |
| Prediction of the quality of recovery after treatment using patient biometric data. | Proportion of patients whose quality of recovery is correctly predicted by the machine learning algorithm. | Four Years |
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Inclusion Criteria:
Exclusion Criteria:
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Patients undergoing treatment for benign or malignant conditions that are amenable to wearing a device of interest and meet the inclusion and exclusion criteria above will be approached for informed consent.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chi-Fu Jeffrey Yang, MD | Contact | 617-726-5200 | cjyang@mgh.harvard.edu | |
| Isha Mehta Warikoo, MD | Contact | 857-250-1355 | imehtawarikoo@mgh.harvard.edu |
| Name | Affiliation | Role |
|---|---|---|
| Chi-Fu Jeffrey Yang | Massachusetts General Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Massachusetts General Hospital | Recruiting | Boston | Massachusetts | 02114 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41213469 | Derived | Wang D, Fang Z, Zhu A, Rettner B, Potter AL, McCarthy M, Zhang L, Kim J, Zhang Y, Powell J, Pope A, Beqari J, Cranor J, Smock G, Warikoo IM, Aaron A, Guo Q, Hanna G, Mitri J, Zarif M, Melki A, Wilkins I, Lin MW, Lee H, Costantino C, Furlow PW, Sachdeva UM, Auchincloss HG, Wright C, Lanuti M, Li X, Jeffrey Yang CF. Changes in patient-reported quality of life after lobectomy versus sublobar resection. J Thorac Cardiovasc Surg. 2026 Apr;171(4):826-835.e5. doi: 10.1016/j.jtcvs.2025.10.040. Epub 2025 Nov 8. |
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| ID | Term |
|---|---|
| D000076251 | Wearable Electronic Devices |
| ID | Term |
|---|---|
| D055615 | Electrical Equipment and Supplies |
| D004864 | Equipment and Supplies |
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