Occult BleedingNCT04814810
A 48 Subject Study Using Non-invasive Multi-Technology Measurements for Early Detection of Ongoing Hemorrhage
An observational study of Occult Bleeding and Hemorrhage, run by Dartmouth-Hitchcock Medical Center at 1 site in United States.
Trial summary
Early detection of ongoing hemorrhage (OH) before onset of hemorrhagic shock is a universally acknowledged great unmet need, and particularly important after traumatic injury. Delays in the detection of OH are associated with a "failure to rescue" and a dramatic deterioration in prognosis once the onset of clinically… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Occult Bleeding, Hemorrhage, Hemorrhagic Shock
- How many people
- 7 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Feb 2021to 15 Apr 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients undergoing liver resection
Who may be able to join
- Adults 18 years or older
- Patients undergoing liver resection.
- Ability to give informed consent.
Who cannot join
- Pre-existing systemic illness, likely to alter systemic cardiovascular response to hemorrhage. Including congestive heart failure, and a paced cardiac rhythm.
- Pregnant
- Prisoner status
Trial locations and contacts
1 site in 1 country.
United States1 site
The Ohio State University Comprehensive Cancer Center
Columbus, Ohio, 43210
About this study
Official title: Development of a Multiplex Precision Medicine System for Early Warning of Progression Toward Shock After Trauma: Non-invasive Measurement During Hepatectomy With Low Central Venous Pressure
Early detection of ongoing hemorrhage (OH) before onset of hemorrhagic shock is a universally acknowledged great unmet need, and particularly important after traumatic injury. 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 would save countless lives.
This is a single site study, enrolling 48 patients undergoing liver resection in a "no significant risk" prospective clinical trial to: 1) further identify a minimal subset of noninvasive measurement technologies necessary for the desired diagnostic performance, 2) validate the performance of our Phase I algorithm, and 3) re-train the algorithm to a Phase II human iteration.
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.
Read the detailed description
Hemorrhagic shock remains a leading cause of death on the battlefield as well in civilian communities. Early detection of ongoing hemorrhage before progression to frank shock would allow early intervention. It is widely appreciated that the classic medical vital signs perform poorly until late in the progression to shock after traumatic injury. Currently available techniques, including intermittent vital sign monitoring, laboratory analysis, and single measurement devices have poor performance before clinically obvious physiologic distress.
The overall goal of this project is to develop a multi-technology noninvasive system for early detection of ongoing hemorrhage. The underlying hypothesis is that deep learning developed algorithms obtaining diagnostic signals from multiple sources will outperform single technology solutions.
While the promise of innovative noninvasive testing has received wide attention, development of effective bedside technologies has thus far been limited and their performance disappointing. In 2014, Kim et al stated that "The results from this meta-analysis found that inaccuracy and imprecision of continuous noninvasive arterial pressure monitoring devices are larger than what was defined as acceptable" and noninvasive blood pressure measurement is among the most fully developed of these technologies. The failure of noninvasive technologies in the detection or diagnosis of complex disease states has been essentially complete. The investigators believe that this failure reflects the limitations of uniplex systems (a single sensor in a single-location) and patient-to-patient variation in physiologic response. Uniplex systems sacrifice the entire diagnostic signal in anatomic-temporal patterns, which likely has significant discriminant power.
To date, technological innovation in early detection of ongoing hemorrhage has been of two broad categories: 1) a search to discover a single new measurement of tissue or organ status or 2) application of more sophisticated mathematical techniques based on machine learning and signal processing.
The investigators propose to develop a system that combines state-of-the-art noninvasive sensing technologies and advanced multivariable statistical algorithms. This system will be developed from its inception to be inexpensive and easily applied, even in austere settings.
To avoid the unnecessary use of blood products, hepatectomies are performed with low central venous pressure (CVP). This is accomplished through restrictive use of intravenous fluids and at times medications to lower the central venous pressure. Low central venous pressure during hepatectomy is an excellent model for development of technologies such as ours and has not been previously used for this purpose.
During each procedure, the investigators will obtain a full ensemble of noninvasive optical, electromagnetic and impedance physiological signals during the LCVPLR procedure. The work proposed herein will evaluate these technologies during standard low central venous pressure liver resections (LCVPLR). These data will be utilized for further machine learning-based algorithm development. The proposed study will be low risk since the measured data will not be available to the clinicians.
Specific Aims:
- Evaluate the performance of existing non-invasive sensing technologies and multivariable algorithms in LCVPLR.
- Obtain human model training and validation data sets during LCVPLR for further refinement of the algorithms.
Power and Sample Size: The investigators anticipate acquiring data from every enrolled subject. The data obtained before onset of parenchymal transection will be utilized as the "no hemorrhage" control. Power and sample size calculations indicate that a sample size of 48 subjects should be sufficient to: 1) further identify the minimal subset of noninvasive measurement technologies necessary for the desired diagnostic performance, 2) validate the existing algorithms, and 3) initially train a human clinical iteration of the algorithms, with a sufficient degree of accuracy (p < 0.05 for ROC-AUC).
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.
- Conditions
- Occult Bleeding, Hemorrhage, Hemorrhagic Shock
- Keywords
- Occult Bleeding, Hemorrhage, Hemorrhagic Shock, Trauma Injury, Trauma, Multiple, Trauma Blunt, Precision Medicine, Triage, Bioimpedance, Electrical Impedance Tomography, Electrical Impedance Spectroscopy, Near-Infrared Spectroscopy
- Medical subject headings
- Hemorrhage, Shock, Hemorrhagic, Accidental Injuries, Multiple Trauma, Wounds, Nonpenetrating
What is being measured
The primary outcome is the main question the study is designed to answer.
Non-invasive measurements that will be used for machine learning
Continuous-wave Near-Infrared Spectroscopy (CW-NIRS)
Measured: 2-3 hours
Non-invasive measurements that will be used for machine learning
Electrical Impedance Tomography
Measured: 2-3 hours
Non-invasive measurements that will be used for machine learning
Electrical Impedance Spectroscopy
Measured: 2-3 hours
Non-invasive measurements that will be used for machine learning
Intrathoracic Hemodynamic Bioreactance Signatures
Measured: 2-3 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2021
- Study start1 Feb 2021
- Primary completion15 Apr 2021
- Study completion15 Apr 2021
Why it stopped: Research model determined to be ineffective for capturing machine-learning appropriate data
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Occult BleedingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 7 participants (actual)
Sponsor and investigators
- Lead sponsor
- Dartmouth-Hitchcock Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- United States Department of Defense, Ohio State University, Dartmouth College
- Responsible party
- Principal investigator, Norman A. Paradis, Dartmouth-Hitchcock Medical Center
- Investigators
- Norman A Paradis, MD, Study director, Dartmouth-Hitchcock Medical CenterMary Dillhoff, MD, Principal investigator, Ohio State UniversityRyan Halter, PhD, Principal investigator, Dartmouth CollegeVikrant Vaze, PhD, Principal investigator, Dartmouth CollegeJonathan Elliott, PhD, Principal investigator, Dartmouth-Hitchcock Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04814810
- Sponsor's protocol ID
- STUDY02000394
- Status last verified
- Dec 2021
- First submitted
- 17 Mar 2021
- Last updated
- 5 Jan 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 24770559Parks 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.
PubMed 17161083Wo 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.
PubMed 8428472Convertino 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.
PubMed 21795890Convertino 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.
PubMed 22764618Kim 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.
PubMed 24637618Soller 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.
PubMed 18006869Belle 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.
PubMed 26871715
Study at a glance
From the registry record, last updated by the sponsor on 5 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Multiplex Precision Medicine System for Early Warning of Progression Toward Shock After Trauma: Non-invasive Measurement During Hepatectomy With Low Central Venous Pressure
- ClinicalTrials.gov ID
- NCT04814810
- Acronym / sponsor ID
- STUDY02000394
- Conditions
- Occult Bleeding, Hemorrhage, Hemorrhagic Shock
- Keywords
- Occult Bleeding, Hemorrhage, Hemorrhagic Shock, Trauma Injury, Trauma, Multiple, Trauma Blunt, Precision Medicine, Triage
- Study type
- Observational
- Recruitment status
- TerminatedReason stopped: Research model determined to be ineffective for capturing machine-learning appropriate data
- Enrolment
- 7 participants (actual)
- Lead sponsor
- Dartmouth-Hitchcock Medical CenterUniversity, hospital or other
- Collaborators
- United States Department of Defense, Ohio State University, Dartmouth College
- Study start
- 1 Feb 2021 (actual)
- Primary completion
- 15 Apr 2021 (actual)
- Study completion
- 15 Apr 2021 (actual)
- First posted
- 24 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Early detection of ongoing hemorrhage (OH) before onset of hemorrhagic shock is a universally acknowledged great unmet need, and particularly important after traumatic injury. 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 would save countless lives.
This is a single site study, enrolling 48 patients undergoing liver resection in a "no significant risk" prospective clinical trial to: 1) further identify a minimal subset of noninvasive measurement technologies necessary for the desired diagnostic performance, 2) validate the performance of our Phase I algorithm, and 3) re-train the algorithm to a Phase II human iteration.
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.
Detailed description
Hemorrhagic shock remains a leading cause of death on the battlefield as well in civilian communities. Early detection of ongoing hemorrhage before progression to frank shock would allow early intervention. It is widely appreciated that the classic medical vital signs perform poorly until late in the progression to shock after traumatic injury. Currently available techniques, including intermittent vital sign monitoring, laboratory analysis, and single measurement devices have poor performance before clinically obvious physiologic distress.
The overall goal of this project is to develop a multi-technology noninvasive system for early detection of ongoing hemorrhage. The underlying hypothesis is that deep learning developed algorithms obtaining diagnostic signals from multiple sources will outperform single technology solutions.
While the promise of innovative noninvasive testing has received wide attention, development of effective bedside technologies has thus far been limited and their performance disappointing. In 2014, Kim et al stated that "The results from this meta-analysis found that inaccuracy and imprecision of continuous noninvasive arterial pressure monitoring devices are larger than what was defined as acceptable" and noninvasive blood pressure measurement is among the most fully developed of these technologies. The failure of noninvasive technologies in the detection or diagnosis of complex disease states has been essentially complete. The investigators believe that this failure reflects the limitations of uniplex systems (a single sensor in a single-location) and patient-to-patient variation in physiologic response. Uniplex systems sacrifice the entire diagnostic signal in anatomic-temporal patterns, which likely has significant discriminant power.
To date, technological innovation in early detection of ongoing hemorrhage has been of two broad categories: 1) a search to discover a single new measurement of tissue or organ status or 2) application of more sophisticated mathematical techniques based on machine learning and signal processing.
The investigators propose to develop a system that combines state-of-the-art noninvasive sensing technologies and advanced multivariable statistical algorithms. This system will be developed from its inception to be inexpensive and easily applied, even in austere settings.
To avoid the unnecessary use of blood products, hepatectomies are performed with low central venous pressure (CVP). This is accomplished through restrictive use of intravenous fluids and at times medications to lower the central venous pressure. Low central venous pressure during hepatectomy is an excellent model for development of technologies such as ours and has not been previously used for this purpose.
During each procedure, the investigators will obtain a full ensemble of noninvasive optical, electromagnetic and impedance physiological signals during the LCVPLR procedure. The work proposed herein will evaluate these technologies during standard low central venous pressure liver resections (LCVPLR). These data will be utilized for further machine learning-based algorithm development. The proposed study will be low risk since the measured data will not be available to the clinicians.
Specific Aims:
- Evaluate the performance of existing non-invasive sensing technologies and multivariable algorithms in LCVPLR.
- Obtain human model training and validation data sets during LCVPLR for further refinement of the algorithms.
Power and Sample Size: The investigators anticipate acquiring data from every enrolled subject. The data obtained before onset of parenchymal transection will be utilized as the "no hemorrhage" control. Power and sample size calculations indicate that a sample size of 48 subjects should be sufficient to: 1) further identify the minimal subset of noninvasive measurement technologies necessary for the desired diagnostic performance, 2) validate the existing algorithms, and 3) initially train a human clinical iteration of the algorithms, with a sufficient degree of accuracy (p < 0.05 for ROC-AUC).
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing liver resection
Key inclusion criteria (3 listed)
- Adults 18 years or older
- Patients undergoing liver resection.
- Ability to give informed consent.
Key exclusion criteria (3 listed)
- Pre-existing systemic illness, likely to alter systemic cardiovascular response to hemorrhage. Including congestive heart failure, and a paced cardiac rhythm.
- Pregnant
- Prisoner status
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Non-invasive measurements that will be used for machine learning
Time frame: 2-3 hours
Continuous-wave Near-Infrared Spectroscopy (CW-NIRS)
Non-invasive measurements that will be used for machine learning
Time frame: 2-3 hours
Electrical Impedance Tomography
Non-invasive measurements that will be used for machine learning
Time frame: 2-3 hours
Electrical Impedance Spectroscopy
Non-invasive measurements that will be used for machine learning
Time frame: 2-3 hours
Intrathoracic Hemodynamic Bioreactance Signatures
Results and safety
The study was stopped early (reason given: Research model determined to be ineffective for capturing machine-learning appropriate data). Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Jan 2022.
- Background: PubMed 24770559Shackelford 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.
- Background: PubMed 17161083Parks 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.
- Background: PubMed 8428472Wo 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.
- Background: PubMed 21795890Convertino 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.
- Background: PubMed 22764618Convertino 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.
- Background: PubMed 24637618Kim 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.
- Background: PubMed 18006869Soller 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.
- Background: PubMed 26871715Belle 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.
- Individual participant dataSharing planned: Undecided
Related trials for Occult Bleeding
Enrolling by invitationObservational study
PillSense Use in Anemia and Hemoccult
Occult Gastrointestinal Bleeding, Upper Gastrointestinal Bleeding (UGIB)
Not yet recruitingObservational study
How Often Does the Fecal Test for Occult Blood Turn Positive After Using Blood Thinners?
Gastrointestinal Bleeding, Antiplatelet Agents
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Norman A Paradis, MD study director
Dartmouth-Hitchcock Medical Center
Mary Dillhoff, MD principal investigator
Ohio State University
Ryan Halter, PhD principal investigator
Dartmouth College
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Occult Bleeding and Hemorrhage, run by Dartmouth-Hitchcock Medical Center at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: stopped early, as of the sponsor's last update on 5 Jan 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was stopped early (reason given: Research model determined to be ineffective for capturing machine-learning appropriate data). Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Non-invasive measurements that will be used for machine learning (time frame: 2-3 hours). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04814810, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Jan 2022.