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

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.

Study Overview

• Status: Terminated
• Conditions: Hemorrhage; Hemorrhagic Shock; Occult Bleeding

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.

• Study Type: Observational
• Enrollment (Actual): 7

Contacts and Locations

Study Locations

• United States
  • Ohio
    • Columbus, Ohio, United States, 43210
      • The Ohio State University Comprehensive Cancer Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Patients undergoing liver resection

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Non-invasive measurements that will be used for machine learning	Continuous-wave Near-Infrared Spectroscopy (CW-NIRS)	2-3 hours
Non-invasive measurements that will be used for machine learning	Electrical Impedance Tomography	2-3 hours
Non-invasive measurements that will be used for machine learning	Electrical Impedance Spectroscopy	2-3 hours
Non-invasive measurements that will be used for machine learning	Intrathoracic Hemodynamic Bioreactance Signatures	2-3 hours

Collaborators and Investigators

• Sponsor: Dartmouth-Hitchcock Medical Center
• Collaborators: United States Department of Defense; Ohio State University; Dartmouth College
• Investigators: Study Director: Norman A Paradis, MD, Dartmouth-Hitchcock Medical Center; Principal Investigator: Mary Dillhoff, MD, Ohio State University; Principal Investigator: Ryan Halter, PhD, Dartmouth College; Principal Investigator: Vikrant Vaze, PhD, Dartmouth College; Principal Investigator: Jonathan Elliott, PhD, Dartmouth-Hitchcock Medical Center

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2021
• Primary Completion (Actual): April 15, 2021
• Study Completion (Actual): April 15, 2021

Study Registration Dates

• First Submitted: March 17, 2021
• First Submitted That Met QC Criteria: March 23, 2021
• First Posted (Actual): March 24, 2021

Study Record Updates

• Last Update Posted (Actual): January 5, 2022
• Last Update Submitted That Met QC Criteria: December 15, 2021
• Last Verified: December 1, 2021

More Information

Terms related to this study

• Keywords: Hemorrhage; Triage; Precision Medicine; Electrical Impedance Tomography; Bioimpedance; Near-Infrared Spectroscopy; Occult Bleeding; Hemorrhagic Shock; Trauma Injury; Trauma, Multiple; Trauma Blunt; Electrical Impedance Spectroscopy
• Additional Relevant MeSH Terms: Pathologic Processes; Hemorrhage; Shock; Shock, Hemorrhagic
• Other Study ID Numbers: STUDY02000394

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No