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This trial aims to demonstrate that the Neuralert Monitoring System will detect strokes before they would be identified by current standard of care. Each patient will be monitored for up to five days, depending on device connectivity or battery duration. Each monitoring session will consist of wearing a Neuralert device on each arm. For this pilot trial, we are interested in learning about Wi-Fi connectivity, successful data transmission, integration into clinical workflow, usability and tolerability.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Cardiac Surgery Patients OR Neurology Patients | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Neuralert Monitor | Device | Device to identify upper extremity asymmetry in patients who are high risk for stroke. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Time to Nursing Response | The time from the nurse receiving an alert to the time they respond after assessing the patient. | Up to 5 days post patient surgery |
| Time to Generate Alert | The time to successful generation of an alert to staff when asymmetry or low battery is detected. | Up to 5 days post patient surgery |
| Measure | Description | Time Frame |
|---|---|---|
| Number of Strokes Identified | The number of strokes identified by the Neuralert device and the number of strokes identified by usual care. If a stroke is identified at the next clinical evaluation after an alarm occurs, it will be considered to have been detected by the Neuralert device. | Up to 5 days post patient surgery |
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Male or female age ≥ 22 years
Admitted or planned admission to one of the participating hospitals and followed by the Cardiothoracic (CT) Surgery or Vascular Surgery services
Considered at high risk for stroke while in the hospital based on:
Ø Interventions or procedures that are high-risk for stroke performed during the hospitalization, which may include:
Ø Other cardiac procedures along with a high-risk medical history:
Admitted with a stroke or TIA attributed to large artery occlusion or severe stenosis that has not been revascularized.
Exclusion Criteria: (all must be no)
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Brett Cucchiara, MD | Contact | (215) 662-6738 | cucchiar@pennmedicine.upenn.edu |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital of the University of Pennsylvania | Recruiting | Philadelphia | Pennsylvania | 19104 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36718858 | Result | Messe SR, Kasner SE, Cucchiara BL, McGarvey ML, Cummings S, Acker MA, Desai N, Atluri P, Wang GJ, Jackson BM, Weimer J. Derivation and Validation of an Algorithm to Detect Stroke Using Arm Accelerometry Data. J Am Heart Assoc. 2023 Feb 7;12(3):e028819. doi: 10.1161/JAHA.122.028819. Epub 2023 Jan 31. |
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| ID | Term |
|---|---|
| D020521 | Stroke |
| ID | Term |
|---|---|
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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| Device Tolerability |
The tolerability and impact on clinical workflow and patient feedback on tolerability after study completion. |
| Up to 5 days post patient surgery |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |