A 48 Subject Study Using Non-invasive Multi-Technology Measurements for Early Detection of Ongoing Hemorrhage

NCT ID: NCT04814810

Last Updated: 2022-01-05

Basic Information

• Recruitment Status: TERMINATED
• Total Enrollment: 7 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-02-01
• Study Completion Date: 2021-04-15

Brief 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 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:

1. Evaluate the performance of existing non-invasive sensing technologies and multivariable algorithms in LCVPLR.

2. 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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

1. Adults 18 years or older

2. Patients undergoing liver resection.

3. Ability to give informed consent.

Exclusion Criteria

1. Pre-existing systemic illness, likely to alter systemic cardiovascular response to hemorrhage. Including congestive heart failure, and a paced cardiac rhythm.

2. Pregnant

3. Prisoner status

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

United States Department of Defense

FED

Sponsor Role: collaborator

Ohio State University

OTHER

Sponsor Role: collaborator

Dartmouth College

OTHER

Sponsor Role: collaborator

Dartmouth-Hitchcock Medical Center

OTHER

Sponsor Role: lead

Responsible Party

Norman A. Paradis

Professor of Surgery; Emergency Medicine Physician

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Norman A Paradis, MD

Role: STUDY_DIRECTOR

Dartmouth-Hitchcock Medical Center

Mary Dillhoff, MD

Role: PRINCIPAL_INVESTIGATOR

Ohio State University

Ryan Halter, PhD

Role: PRINCIPAL_INVESTIGATOR

Dartmouth College

Vikrant Vaze, PhD

Role: PRINCIPAL_INVESTIGATOR

Dartmouth College

Jonathan Elliott, PhD

Role: PRINCIPAL_INVESTIGATOR

Dartmouth-Hitchcock Medical Center

Locations

The Ohio State University Comprehensive Cancer Center

Columbus, Ohio, United States

Countries

United States

References

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.

Reference Type: BACKGROUND

PMID: 24770559 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 17161083 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 8428472 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 21795890 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 22764618 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 24637618 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 18006869 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 26871715 (View on PubMed)

Other Identifiers

STUDY02000394

Identifier Type: -

Identifier Source: org_study_id