1
|
Murphy EK, Bertsch SR, Klein SB, Rashedi N, Sun Y, Joyner MJ, Curry TB, Johnson CP, Regimbal RJ, Wiggins CC, Senefeld JW, Shepherd JRA, Elliott JT, Halter RJ, Vaze VS, Paradis NA. Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model. Sci Rep 2024; 14:8719. [PMID: 38622207 PMCID: PMC11018605 DOI: 10.1038/s41598-024-59139-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/08/2024] [Indexed: 04/17/2024] Open
Abstract
Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95-1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0-15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38-0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.
Collapse
Affiliation(s)
- Ethan K Murphy
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.
| | - Spencer R Bertsch
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Samuel B Klein
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
- Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Navid Rashedi
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Yifei Sun
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Michael J Joyner
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Timothy B Curry
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Christopher P Johnson
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Riley J Regimbal
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Chad C Wiggins
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Jonathon W Senefeld
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - John R A Shepherd
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Jonathan Thomas Elliott
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
- Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Ryan J Halter
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
| | - Vikrant S Vaze
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Norman A Paradis
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
- Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| |
Collapse
|
2
|
Ciaraglia A, Convertino VA, Wang H, Cigarroa F, Thomas E, Fritze D, Nicholson S, Eastridge B. Intraoperative Use of Compensatory Reserve Measurement in Orthotopic Liver Transplant: Improved Sensitivity for the Prediction of Hypovolemic Events. Mil Med 2023; 188:322-327. [PMID: 37948269 DOI: 10.1093/milmed/usad130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/13/2023] [Accepted: 04/19/2023] [Indexed: 11/12/2023] Open
Abstract
INTRODUCTION The compensatory reserve measurement (CRM) is a continuous non-invasive monitoring technology that measures the summation of all physiological mechanisms involved in the compensatory response to central hypovolemia. The CRM is displayed on a 0% to 100% scale. The objective of this study is to characterize the use of CRM in the operative setting and determine its ability to predict hypovolemic events compared to standard vital signs. Orthotopic liver transplant was used as the reference procedure because of the predictable occurrence of significant hemodynamic shifts. METHODS A prospective observational cohort study was conducted on 22 consecutive patients undergoing orthotopic liver transplant. The subjects were monitored in accordance with the standard of care. The CRM data were collected concurrently with intraoperative staff blinded to the outputs. The data were stored on secure devices on encrypted files. Based on prior literature, subgroup analysis was performed for high-tolerance (good compensators) and low-tolerance (poor compensators) groups, which was based on a shock index threshold of 0.9. Threshold events were defined as follows: CRM below 60% (CRM60), systolic blood pressure (SBP) below 90 mmHg (SBP90), and heart rate (HR) above 100 beats per minute (HR100). RESULTS Complete data were captured in 22 subjects as a result of device malfunction or procedure cancellation. Sensitivity analysis was performed for the detection of hypovolemia at the time of the event. CRM60 was the most sensitive (62.6%) when compared to other threshold measures such as SBP90 (30.6%), HR100 (23.1%), elevated lactate (54.6%), and a drop in hemoglobin (41.7%). The number of patients meeting the CRM60 threshold at the time of the first transfusion (TFX) was higher when compared to SBP90 and HR100 in the overall group (P = .001 and P < .001, respectively) and both the high-tolerance (P = .002 and P = .001, respectively) and low-tolerance groups (P = .016 and P = .001, respectively). Similar results supporting the higher sensitivity of CRM were observed when comparing the number of patients below the threshold at the time of the first vasopressor administration. Start time was standardized so that the time-to-threshold signals for hemodynamic and laboratory parameters could be compared. The median time-to-CRM signal detection before the TFX event was -15.0 minutes (i.e., 15 minutes before TFX). There was no difference when compared to the SBP threshold (median time -5.0 minutes, P = .64) but was significantly sooner when compared to HR (P = .006), lactate (P = .002), and hemoglobin (P < .001). CONCLUSIONS At the time of the first TFX, the CRM had a higher rate of detection of a hypovolemic event compared to SBP and HR, indicating a higher sensitivity for the detection of the first hypovolemic event. When combined with all hypovolemic events, sensitivity analysis showed that CRM60 provides the earlier predictive capability. Given that SBP is the clinical standard of care for the initiation of TFX, the finding that median time to event detection was statistically similar between CRM60 and SBP90 was not unexpected. When compared to other measures of hypovolemia, the CRM consistently showed earlier detection of hypovolemic events. Although this study had a small sample size, it produced significant results and can serve as a proof of concept for future large-scale studies.
Collapse
Affiliation(s)
- Angelo Ciaraglia
- Department of Surgery, Division of Trauma and Critical Care, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Victor A Convertino
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, San Antonio, JBSA Fort Sam Houston, TX 78229, USA
| | - Hanzhang Wang
- Department of Urology, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Francisco Cigarroa
- Department of Surgery, Division of Transplant and Hepatobiliary Surgery, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Elizabeth Thomas
- Department of Surgery, Division of Transplant and Hepatobiliary Surgery, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Danielle Fritze
- Department of Surgery, Division of Transplant and Hepatobiliary Surgery, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Susannah Nicholson
- Department of Surgery, Division of Trauma and Critical Care, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Brian Eastridge
- Department of Surgery, Division of Trauma and Critical Care, The University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| |
Collapse
|
3
|
Koons NJ, Moses CD, Thompson P, Strandenes G, Convertino VA. Identifying critical DO 2 with compensatory reserve during simulated hemorrhage in humans. Transfusion 2022; 62 Suppl 1:S122-S129. [PMID: 35733031 DOI: 10.1111/trf.16958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/09/2022] [Accepted: 03/18/2022] [Indexed: 12/01/2022]
Abstract
BACKGROUND Based on previous experiments in nonhuman primates, we hypothesized that DO2 crit in humans is 5-6 ml O2 ·kg-1 min-1 . STUDY DESIGN AND METHODS We measured the compensatory reserve (CRM) and calculated oxygen delivery (DO2 ) in 166 healthy, normotensive, nonsmoking subjects (97 males, 69 females) during progressive central hypovolemia induced by lower body negative pressure as a model of ongoing hemorrhage. Subjects were classified as having either high tolerance (HT; N = 111) or low tolerance (LT; N = 55) to central hypovolemia. RESULTS HT and LT groups were matched for age, weight, BMI, and vital signs, DO2 and CRM at baseline. The CRM-DO2 relationship was best fitted to a logarithmic model in HT subjects (amalgamated R2 = 0.971) and a second-order polynomial model in the LT group (amalgamated R2 = 0.991). Average DO2 crit for the entire subject cohort was estimated at 5.3 ml O2 ·kg-1 min-1 , but was ~14% lower in HT compared with LT subjects. The reduction in DO2 from 40% CRM to 20% CRM was 2-fold greater in the LT compared with the HT group. CONCLUSIONS Average DO2 crit in humans is 5.3 ml O2 ·kg-1 min-1 , but is ~14% lower in HT compared with LT subjects. The CRM-DO2 relationship is curvilinear in humans, and different when comparing HT and LT individuals. The threshold for an emergent monitoring signal should be recalibrated from 30% to 40% CRM given that the decline in DO2 from 40% CRM to 20% CRM for LT subjects is located on the steepest part of the CRM-DO2 relationship.
Collapse
Affiliation(s)
- Natalie J Koons
- Battlefield Health & Trauma Center for Human Integrative Physiology, U. S. Army Institute of Surgical Research, San Antonio, Texas, USA
| | - Catherine D Moses
- Battlefield Health & Trauma Center for Human Integrative Physiology, U. S. Army Institute of Surgical Research, San Antonio, Texas, USA
| | | | - Geir Strandenes
- Norwegian Armed Forces, Haukeland University Hospital, Bergen, Norway
| | - Victor A Convertino
- Battlefield Health & Trauma Center for Human Integrative Physiology, U. S. Army Institute of Surgical Research, San Antonio, Texas, USA
| |
Collapse
|
4
|
Murphy EK, Klein SB, Hamlin A, Anderson JE, Minichiello JM, Lindqwister AL, Moodie KL, Wanken ZJ, Read JT, Borza VA, Elliott JT, Halter RJ, Vaze VS, Paradis NA. Detection of subclinical hemorrhage using electrical impedance: a porcine study. Physiol Meas 2022; 43. [PMID: 35508144 DOI: 10.1088/1361-6579/ac6cc6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 05/04/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Analyze the performance of electrical impedance tomography (EIT) in an innovative porcine model of subclinical hemorrhage and investigate associations between EIT and hemodynamic trends. APPROACH Twenty-five swine were bled at slow rates to create an extended period of subclinical hemorrhage during which the animal's heart rate (HR) and blood pressure (BP) remained stable from before hemodynamic deterioration, where stable was defined as < 15% decrease in BP and < 20% increase in HR - i.e. hemorrhages were hidden from standard vital signs of HR and BP. Continuous vital signs, photo-plethysmography, and continuous non-invasive EIT data were recorded and analyzed with the objective of developing an improved means of detecting subclinical hemorrhage - ideally as early as possible. MAIN RESULTS Best area-under-the-curve (AUC) values from comparing bleed to no-bleed epochs were 0.96 at a 80 ml bleed (~15.4 minutes) using an EIT-data-based metric and 0.79 at a 120 ml bleed (~23.1 minutes) from invasively measured BP - i.e. the EIT-data-based metric achieved higher AUCs at earlier points compared to standard clinical metrics without requiring image reconstructions. SIGNIFICANCE In this clinically relevant porcine model of subclinical hemorrhage, EIT appears to be superior to standard clinical metrics in early detection of hemorrhage.
Collapse
Affiliation(s)
- Ethan K Murphy
- Thayer School of Engineering, Dartmouth, 14 Engineering Dr, Hanover, New Hampshire, 03755, UNITED STATES
| | - Samuel B Klein
- Geisel School of Medicine, Dartmouth College Geisel School of Medicine, 1 Rope Ferry Rd, Hanover, New Hampshire, 03755-1404, UNITED STATES
| | - Alexandra Hamlin
- Thayer School of Engineering, Dartmouth, 14 Engineering Dr, Hanover, New Hampshire, 03755, UNITED STATES
| | - Justin E Anderson
- Geisel School of Medicine, Dartmouth College Geisel School of Medicine, 1 Rope Ferry Rd, Hanover, New Hampshire, 03755-1404, UNITED STATES
| | - Joseph M Minichiello
- Geisel School of Medicine, Dartmouth College Geisel School of Medicine, 1 Rope Ferry Rd, Hanover, New Hampshire, 03755-1404, UNITED STATES
| | - Alexander L Lindqwister
- Geisel School of Medicine, Dartmouth College Geisel School of Medicine, 1 Rope Ferry Rd, Hanover, New Hampshire, 03755-1404, UNITED STATES
| | - Karen L Moodie
- Geisel School of Medicine, Dartmouth College, 1 Rope Ferry Rd, Hanover, New Hampshire, 03755, UNITED STATES
| | - Zachary J Wanken
- Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, New Hampshire, 03756-1000, UNITED STATES
| | - Jackson T Read
- Geisel School of Medicine, Dartmouth College Geisel School of Medicine, 1 Rope Ferry Rd, Hanover, New Hampshire, 03755-1404, UNITED STATES
| | - Victor A Borza
- Thayer School of Engineering, Dartmouth College, 14 Engineering Dr, Hanover, New Hampshire, 03755-3529, UNITED STATES
| | - Jonathan T Elliott
- Thayer School of Engineering, Dartmouth College, 14 Engineering Dr, Hanover, New Hampshire, 03755-3529, UNITED STATES
| | - Ryan J Halter
- Thayer School of Engineering, Dartmouth College, 8000 Cummings Hall, Hanover, NH 03755-8000, USA, Hanover, 03755-8000, UNITED STATES
| | - Vikrant S Vaze
- Thayer School of Engineering, Dartmouth, 14 Engineering Dr, Hanover, New Hampshire, 03755, UNITED STATES
| | - Norman A Paradis
- Geisel School of Medicine, Dartmouth College Geisel School of Medicine, 1 Rope Ferry Rd, Hanover, New Hampshire, 03755-1404, UNITED STATES
| |
Collapse
|
5
|
Nordine M, Treskatsch S, Habazettl H, Gunga HC, Brauns K, Dosel P, Petricek J, Opatz O. Orthostatic Resiliency During Successive Hypoxic, Hypoxic Orthostatic Challenge: Successful vs. Unsuccessful Cardiovascular and Oxygenation Strategies. Front Physiol 2021; 12:712422. [PMID: 34776997 PMCID: PMC8578448 DOI: 10.3389/fphys.2021.712422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/06/2021] [Indexed: 11/25/2022] Open
Abstract
Introduction: Rapid environmental changes, such as successive hypoxic-hypoxic orthostatic challenges (SHHOC) occur in the aerospace environment, and the ability to remain orthostatically resilient (OR) relies upon orchestration of physiological counter-responses. Counter-responses adjusting for hypoxia may conflict with orthostatic responses, and a misorchestration can lead to orthostatic intolerance (OI). The goal of this study was to pinpoint specific cardiovascular and oxygenation factors associated with OR during a simulated SHHOC. Methods: Thirty one men underwent a simulated SHHOC consisting of baseline (P0), normobaric hypoxia (Fi02 = 12%, P1), and max 60 s of hypoxic lower body negative pressure (LBNP, P2). Alongside anthropometric variables, non-invasive cardiovascular, central and peripheral tissue oxygenation parameters, were recorded. OI was defined as hemodynamic collapse during SHHOC. Comparison of anthropometric, cardiovascular, and oxygenation parameters between OR and OI was performed via Student’s t-test. Within groups, a repeated measures ANOVA test with Holm-Sidak post hoc test was performed. Performance diagnostics were performed to assess factors associated with OR/OI (sensitivity, specificity, positive predictive value PPV, and odd’s ratio OR). Results: Only 9/31 were OR, and 22/31 were OI. OR had significantly greater body mass index (BMI), weight, peripheral Sp02, longer R-R Interval (RRI) and lower heart rate (HR) at P0. During P1 OR exhibited significantly higher cardiac index (CI), stroke volume index (SVI), and lower systemic vascular resistance index (SVRI) than OI. Both groups exhibited a significant decrease in cerebral oxygenation (TOIc) with an increase in cerebral deoxygenated hemoglobin (dHbc), while the OI group showed a significant decrease in cerebral oxygenated hemoglobin (02Hbc) and peripheral oxygenation (TOIp) with an increase in peripheral deoxygenated hemoglobin (dHbp). During P2, OR maintained significantly greater CI, systolic, mean, and diastolic pressure (SAP, MAP, DAP), with a shortened RRI compared to the OI group, while central and peripheral oxygenation were not different. Body weight and BMI both showed high sensitivity (0.95), low specificity (0.33), a PPV of 0.78, with an OR of 0.92, and 0.61. P0 RRI showed a sensitivity of 0.95, specificity of 0.22, PPV 0.75, and OR of 0.99. Delta SVI had the highest performance diagnostics during P1 (sensitivity 0.91, specificity 0.44, PPV 0.79, and OR 0.8). Delta SAP had the highest overall performance diagnostics for P2 (sensitivity 0.95, specificity 0.67, PPV 0.87, and OR 0.9). Discussion: Maintaining OR during SHHOC is reliant upon greater BMI, body weight, longer RRI, and lower HR at baseline, while increasing CI and SVI, minimizing peripheral 02 utilization and decreasing SVRI during hypoxia. During hypoxic LBNP, the ability to remain OR is dependent upon maintaining SAP, via CI increases rather than SVRI. Cerebral oxygenation parameters, beyond 02Hbc during P1 did not differ between groups, suggesting that the during acute hypoxia, an increase in cerebral 02 consumption, coupled with increased peripheral 02 utilization does seem to play a role in OI risk during SHHOC. However, cardiovascular factors such as SVI are of more value in assessing OR/OI risk. The results can be used to implement effective aerospace crew physiological monitoring strategies.
Collapse
Affiliation(s)
- Michael Nordine
- Department of Anaesthesiology and Intensive Care Medicine, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sascha Treskatsch
- Department of Anaesthesiology and Intensive Care Medicine, Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Helmut Habazettl
- Center for Space Medicine and Extreme Environments Berlin, Berlin Institute of Health, Institute of Physiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Hanns-Christian Gunga
- Center for Space Medicine and Extreme Environments Berlin, Berlin Institute of Health, Institute of Physiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Katharins Brauns
- Center for Space Medicine and Extreme Environments Berlin, Berlin Institute of Health, Institute of Physiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Petr Dosel
- Military University Hospital, Institute of Aviation Medicine, Prague, Czechia
| | - Jan Petricek
- Military University Hospital, Institute of Aviation Medicine, Prague, Czechia
| | - Oliver Opatz
- Center for Space Medicine and Extreme Environments Berlin, Berlin Institute of Health, Institute of Physiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| |
Collapse
|
6
|
Convertino VA, Johnson MC, Alarhayem A, Nicholson SE, Chung KK, DeRosa M, Eastridge BJ. Compensatory reserve detects subclinical shock with more expeditious prediction for need of life-saving interventions compared to systolic blood pressure and blood lactate. Transfusion 2021; 61 Suppl 1:S167-S173. [PMID: 34269439 DOI: 10.1111/trf.16494] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/11/2021] [Accepted: 02/11/2021] [Indexed: 12/27/2022]
Abstract
INTRODUCTION We conducted a prospective observational study on 205 trauma patients at a level I trauma facility to test the hypothesis that a compensatory reserve measurement (CRM) would identify higher risk for progression to shock and/or need a life-saving interventions (LSIs) earlier than systolic blood pressure (SBP) and blood lactate (LAC). METHODS A composite outcome metric included blood transfusion, procedural LSI, and mortality. Discrete measures assessed as abnormal (ab) were SBP <90 mmHg, CRM <60%, and LAC >2.0. A graded categorization of shock was defined as: no shock (normal [n] SBP [n-SBP], n-CRM, n-LAC); sub-clinical shock (ab-CRM, n-SBP, n-LAC); occult shock (n-SBP, ab-CRM, ab-LAC); or overt shock (ab-SBP, ab-CRM, ab-LAC). RESULTS Three patients displayed overt shock, 53 displayed sub-clinical shock, and 149 displayed no shock. After incorporating lactate into the analysis, 86 patients demonstrated no shock, 25 were classified as sub-clinical shock, 91 were classified as occult shock, and 3 were characterized as overt shock. Each shock subcategory revealed a graded increase requiring LSI and transfusion. Initial CRM was associated with progression to shock (odds ratio = 0.97; p < .001) at an earlier time than SBP or LAC. CONCLUSIONS Initial CRM uncovers a clinically relevant subset of patients who are not detected by SBP and LAC. Our results suggest CRM could be used to more expeditiously identify injured patients likely to deteriorate to shock, with requirements for blood transfusion or procedural LSI.
Collapse
Affiliation(s)
- Victor A Convertino
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, Texas, USA.,Department of Medicine and Surgery, Uniformed Services University, Bethesda, Maryland, USA
| | - Michael C Johnson
- Division of Trauma and Emergency Surgery, UT Health San Antonio, San Antonio, Texas, USA
| | - Abdul Alarhayem
- Division of Trauma and Emergency Surgery, UT Health San Antonio, San Antonio, Texas, USA
| | - Susannah E Nicholson
- Division of Trauma and Emergency Surgery, UT Health San Antonio, San Antonio, Texas, USA
| | - Kevin K Chung
- Department of Medicine and Surgery, Uniformed Services University, Bethesda, Maryland, USA
| | - Mark DeRosa
- Division of Trauma and Emergency Surgery, UT Health San Antonio, San Antonio, Texas, USA
| | - Brian J Eastridge
- Division of Trauma and Emergency Surgery, UT Health San Antonio, San Antonio, Texas, USA
| |
Collapse
|
7
|
Convertino VA, Koons NJ, Suresh MR. Physiology of Human Hemorrhage and Compensation. Compr Physiol 2021; 11:1531-1574. [PMID: 33577122 DOI: 10.1002/cphy.c200016] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Hemorrhage is a leading cause of death following traumatic injuries in the United States. Much of the previous work in assessing the physiology and pathophysiology underlying blood loss has focused on descriptive measures of hemodynamic responses such as blood pressure, cardiac output, stroke volume, heart rate, and vascular resistance as indicators of changes in organ perfusion. More recent work has shifted the focus toward understanding mechanisms of compensation for reduced systemic delivery and cellular utilization of oxygen as a more comprehensive approach to understanding the complex physiologic changes that occur following and during blood loss. In this article, we begin with applying dimensional analysis for comparison of animal models, and progress to descriptions of various physiological consequences of hemorrhage. We then introduce the complementary side of compensation by detailing the complexity and integration of various compensatory mechanisms that are activated from the initiation of hemorrhage and serve to maintain adequate vital organ perfusion and hemodynamic stability in the scenario of reduced systemic delivery of oxygen until the onset of hemodynamic decompensation. New data are introduced that challenge legacy concepts related to mechanisms that underlie baroreflex functions and provide novel insights into the measurement of the integrated response of compensation to central hypovolemia known as the compensatory reserve. The impact of demographic and environmental factors on tolerance to hemorrhage is also reviewed. Finally, we describe how understanding the physiology of compensation can be translated to applications for early assessment of the clinical status and accurate triage of hypovolemic and hypotensive patients. © 2021 American Physiological Society. Compr Physiol 11:1531-1574, 2021.
Collapse
Affiliation(s)
- Victor A Convertino
- Battlefield Healthy & Trauma Center for Human Integrative Physiology, United States Army Institute of Surgical Research, JBSA San Antonio, Texas, USA
| | - Natalie J Koons
- Battlefield Healthy & Trauma Center for Human Integrative Physiology, United States Army Institute of Surgical Research, JBSA San Antonio, Texas, USA
| | - Mithun R Suresh
- Battlefield Healthy & Trauma Center for Human Integrative Physiology, United States Army Institute of Surgical Research, JBSA San Antonio, Texas, USA
| |
Collapse
|