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Grant funding for the study ended.
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| Name | Class |
|---|---|
| United States Department of Defense | FED |
| Dartmouth College | OTHER |
| Baystate Medical Center | OTHER |
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This study is Phase 3 of a three-phase DOD CDMRP funded project for the development of a multi-technology poly-anatomic noninvasive system for early detection of occult hemorrhage.
Early detection of ongoing hemorrhage (OH) before onset of shock is a universally acknowledged great unmet need, and particularly important after trauma. Delays in the detection of OH are associated with a "failure to rescue" and a dramatic deterioration in prognosis once the onset of clinically frank shock has occurred. An early alert to the presence of OH with an acceptable rate of false-positives and false-negatives would save countless lives. Additionally, such technology would save significant time, money and effort by allowing medical resources to be applied more accurately - the essence of precision medicine. An automated system would monitor currently stable patients continuously, leaving clinicians free to care for patients in need of attention.
The investigators will enroll 480 trauma patients in a "no significant risk" prospective clinical trial to 1) evaluate the performance of a Mark I prototype, 2) validate the performance of the Phase II algorithm, and 3) re-train the algorithm to Phase III iteration.
This is not a therapeutic study. The main outcome variables are non-invasive measurements that will be used for machine learning, not real-time patient management. The data generated will be used later for discovery and validation in traditional and innovative machine learning.
As a minimal risk study, there will be no change from standard of care for patients undergoing surgery. The surgical procedures and pharmacotherapies will proceed as per standard clinical management.
Enrolled patients will undergo standard preoperative, anesthetic, and postoperative physiological monitoring that includes:
Electrocardiogram: Heart rate and electrical activity of the heart will be recorded via a 3-lead ECG.
Arterial Oxygen Saturation (SpO2): Pulse oximetry will be recorded using a commercially available finger pulse oximeter.
Finger Photoplethysmography: waveforms will be obtained non-invasively from either the SpO2 photoplethysmography or alternatively a Flashback Technologies Compensatory Reserve Index (CRI™) device.
In addition to these standard physiologic measurements, as a part of the research, the investigators will also acquire the following non-invasive optical and impedance measurements. All of these devices have been previously validated as non-invasive and safe in humans. The impedance electrodes and optical emitter/detectors will be incorporated into easily applied latex-free based "belts" that will be applied to anatomic locations along the thorax, abdomen and thigh. Application of these belts will be as soon as practical after patient arrival.
The clinicians providing care to the patient will be blind with respect to the measurements from these devices.
Continuous-wave Near-Infrared Spectroscopy (CW-NIRS) Device, custom built:
The device is built on an Ocean Optics FLAME USB Spectrometers, with two OSRAM 4736 NIR LEDs in plastic holders meant to be taped or strapped to the patient on the chest/torso and thigh locations. An Oceans Optics tungsten-halogen light source is used for an additional probe location on the forehead. Probe holders are designed to collect light from surface of skin by means of a mirror and collimator. The device will collect data on light attenuation of muscle in models of shock/trauma. The light output for all locations is below the ANSI limits for maximum permissible skin exposure.
SwissTom Electrical Impedance Tomography (EIT) System:
Name of device: Pioneer Set (Electrical Impedance Tomography System) Device Manufacturer: SwissTom AG (Parent company SenTec AG)
This device monitors dynamically changing perfusion physiology. To record multiplexed impedance signature a belt of 16-32 electrodes is placed around the thorax above the nipple line and thigh and impedances are recorded between multiple sets of electrodes.
SwissTom develops a clinical EIT system that is approved for pulmonary imaging. This Pioneer system is their research platform that has similar functionality and safety performance to their clinical system but provides research teams with access to raw data and additional functionality for specifying imaging parameters (e.g. control of frame rates, signal frequency acquisition, electrode drive patterns).
ScioSpec MultiChannel Electrical Impedance Spectroscopy (EIS) System:
Name of device: ISX-Mini (MultiChannel Electrical Impedance Spectroscopy System) Device Manufacturer: ScioSpec
To record multiplexed impedance signatures- an array of 4 electrodes is placed around the cranium and thorax of the patient and impedance spectroscopy signatures are recorded at each anatomic location. This device monitors dynamically changing intracranial, intrathoracic and intra-abdominal physiology.
General Approach to Minimize Risk:
This protocol will be minimal risk to patients as there is little risk associated with the placement of these devices and they can be removed at any time. They don't interfere with standard equipment used in clinical management of trauma patients.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Trauma Patients | Experimental | Patients suffering blunt and/or penetrating trauma but without physiologic criteria suggestive of ongoing hemorrhage:
|
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Detection of Occult Hemorrhage in Trauma Patients | Device | Non-invasive monitoring of trauma patients |
|
| Measure | Description | Time Frame |
|---|---|---|
| Algorithm Performance: Time of Alarm Before Onset of Deterioration | patient data will be collected over 3-6 hours | |
| Algorithm Performance: Sensitivity as Measured by for Alarm/no Alarm Outcome | patient data will be collected over 3-6 hours | |
| Algorithm Performance: Specificity as Measured by for Alarm/no Alarm Outcome | patient data will be collected over 3-6 hours | |
| Algorithm Performance: Positive Predictive Value for Alarm/no Alarm Outcome | patient data will be collected over 3-6 hours | |
| Algorithm Performance: Negative Predictive Value for Alarm/no Alarm Outcome | patient data will be collected over 3-6 hours |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Noman Paradis, MD | Dartmouth-Hitchcock Medical Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Baystate Medical Center | Springfield | Massachusetts | 01199 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 24770559 | Background | Shackelford SA, Colton K, Stansbury LG, Galvagno SM Jr, Anazodo AN, DuBose JJ, Hess JR, Mackenzie CF. Early identification of uncontrolled hemorrhage after trauma: current status and future direction. J Trauma Acute Care Surg. 2014 Sep;77(3 Suppl 2):S222-7. doi: 10.1097/TA.0000000000000198. No abstract available. | |
| 17161083 | Background |
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At this time, there are no plans to share IPD.
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| ID | Title | Description |
|---|---|---|
| FG000 | Trauma Patients | Patients suffering blunt and/or penetrating trauma but without physiologic criteria suggestive of ongoing hemorrhage:
Detection of Occult Hemorrhage in Trauma Patients: Non-invasive monitoring of trauma patients |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | Trauma Patients | Patients suffering blunt and/or penetrating trauma but without physiologic criteria suggestive of ongoing hemorrhage:
Detection of Occult Hemorrhage in Trauma Patients: Non-invasive monitoring of trauma patients |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Algorithm Performance: Time of Alarm Before Onset of Deterioration | Only 1 participant was enrolled was stable for 6 hours (no signs of hemodynamic deterioration). Therefore there was no data to analyze algorithm performance. Algorithm developed was to be trained to alarm under two circumstances indicating significant risk of ongoing hemorrhage progressing to shock: 1) a change from baseline, 2) development of prespecified patterns. This participant did not experience a triggering event, and therefore there was no data to be used to evaluate the algorithm. | Posted | Number | minutes | patient data will be collected over 3-6 hours |
|
1 day
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Trauma Patients | Patients suffering blunt and/or penetrating trauma but without physiologic criteria suggestive of ongoing hemorrhage:
Detection of Occult Hemorrhage in Trauma Patients: Non-invasive monitoring of trauma patients |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Norman A. Paradis | Dartmouth Health | (603) 650-7254 | Norman.A.Paradis@hitchcock.org |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Jul 18, 2022 | Jun 6, 2023 | Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Oct 13, 2022 | Jun 6, 2023 | ICF_001.pdf |
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| ID | Term |
|---|---|
| D006470 | Hemorrhage |
| D012771 | Shock, Hemorrhagic |
| D000081084 | Accidental Injuries |
| ID | Term |
|---|---|
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D012769 | Shock |
| D014947 | Wounds and Injuries |
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| Parks JK, Elliott AC, Gentilello LM, Shafi S. Systemic hypotension is a late marker of shock after trauma: a validation study of Advanced Trauma Life Support principles in a large national sample. Am J Surg. 2006 Dec;192(6):727-31. doi: 10.1016/j.amjsurg.2006.08.034. |
| 8428472 | Background | Wo CC, Shoemaker WC, Appel PL, Bishop MH, Kram HB, Hardin E. Unreliability of blood pressure and heart rate to evaluate cardiac output in emergency resuscitation and critical illness. Crit Care Med. 1993 Feb;21(2):218-23. doi: 10.1097/00003246-199302000-00012. |
| 21795890 | Background | Convertino VA, Moulton SL, Grudic GZ, Rickards CA, Hinojosa-Laborde C, Gerhardt RT, Blackbourne LH, Ryan KL. Use of advanced machine-learning techniques for noninvasive monitoring of hemorrhage. J Trauma. 2011 Jul;71(1 Suppl):S25-32. doi: 10.1097/TA.0b013e3182211601. |
| 22764618 | Background | Convertino VA. Blood pressure measurement for accurate assessment of patient status in emergency medical settings. Aviat Space Environ Med. 2012 Jun;83(6):614-9. doi: 10.3357/asem.3204.2012. |
| 24637618 | Background | Kim SH, Lilot M, Sidhu KS, Rinehart J, Yu Z, Canales C, Cannesson M. Accuracy and precision of continuous noninvasive arterial pressure monitoring compared with invasive arterial pressure: a systematic review and meta-analysis. Anesthesiology. 2014 May;120(5):1080-97. doi: 10.1097/ALN.0000000000000226. |
| 18006869 | Background | Soller BR, Yang Y, Soyemi OO, Ryan KL, Rickards CA, Walz JM, Heard SO, Convertino VA. Noninvasively determined muscle oxygen saturation is an early indicator of central hypovolemia in humans. J Appl Physiol (1985). 2008 Feb;104(2):475-81. doi: 10.1152/japplphysiol.00600.2007. Epub 2007 Nov 15. |
| 26871715 | Background | Belle A, Ansari S, Spadafore M, Convertino VA, Ward KR, Derksen H, Najarian K. A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation. PLoS One. 2016 Feb 12;11(2):e0148544. doi: 10.1371/journal.pone.0148544. eCollection 2016. |
| Participants |
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| Sex: Female, Male | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
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| Primary | Algorithm Performance: Sensitivity as Measured by for Alarm/no Alarm Outcome | Only 1 participant was enrolled was stable for 6 hours (no signs of hemodynamic deterioration). Therefore there was no data to analyze algorithm performance. Algorithm developed was to be trained to alarm under two circumstances indicating significant risk of ongoing hemorrhage progressing to shock: 1) a change from baseline, 2) development of prespecified patterns. This participant did not experience a triggering event, and therefore there was no data to be used to evaluate the algorithm. | Posted | Number | percent | patient data will be collected over 3-6 hours |
|
|
|
| Primary | Algorithm Performance: Specificity as Measured by for Alarm/no Alarm Outcome | Only 1 participant was enrolled was stable for 6 hours (no signs of hemodynamic deterioration). Therefore there was no data to analyze algorithm performance. Algorithm developed was to be trained to alarm under two circumstances indicating significant risk of ongoing hemorrhage progressing to shock: 1) a change from baseline, 2) development of prespecified patterns. This participant did not experience a triggering event, and therefore there was no data to be used to evaluate the algorithm. | Posted | Number | percent | patient data will be collected over 3-6 hours |
|
|
|
| Primary | Algorithm Performance: Positive Predictive Value for Alarm/no Alarm Outcome | Only 1 participant was enrolled was stable for 6 hours (no signs of hemodynamic deterioration). Therefore there was no data to analyze algorithm performance. Algorithm developed was to be trained to alarm under two circumstances indicating significant risk of ongoing hemorrhage progressing to shock: 1) a change from baseline, 2) development of prespecified patterns. This participant did not experience a triggering event, and therefore there was no data to be used to evaluate the algorithm. | Posted | Number | percent | patient data will be collected over 3-6 hours |
|
|
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| Primary | Algorithm Performance: Negative Predictive Value for Alarm/no Alarm Outcome | Only 1 participant was enrolled was stable for 6 hours (no signs of hemodynamic deterioration). Therefore there was no data to analyze algorithm performance. Algorithm developed was to be trained to alarm under two circumstances indicating significant risk of ongoing hemorrhage progressing to shock: 1) a change from baseline, 2) development of prespecified patterns. This participant did not experience a triggering event, and therefore there was no data to be used to evaluate the algorithm. | Posted | Number | percent | patient data will be collected over 3-6 hours |
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