Skip to main content
Top
Published in: Journal of Clinical Monitoring and Computing 6/2015

01-12-2015 | Original Research

Accuracy of continuous noninvasive hemoglobin monitoring for the prediction of blood transfusions in trauma patients

Authors: Samuel M. Galvagno Jr., Peter Hu, Shiming Yang, Cheng Gao, David Hanna, Stacy Shackelford, Colin Mackenzie

Published in: Journal of Clinical Monitoring and Computing | Issue 6/2015

Login to get access

Abstract

Early detection of hemorrhagic shock is required to facilitate prompt coordination of blood component therapy delivery to the bedside and to expedite performance of lifesaving interventions. Standard physical findings and vital signs are difficult to measure during the acute resuscitation stage, and these measures are often inaccurate until patients deteriorate to a state of decompensated shock. The aim of this study is to examine a severely injured trauma patient population to determine whether a noninvasive SpHb monitor can predict the need for urgent blood transfusion (universal donor or additional urgent blood transfusion) during the first 12 h of trauma patient resuscitation. We hypothesize that trends in continuous SpHb, combined with easily derived patient-specific factors, can identify the immediate need for transfusion in trauma patients. Subjects were enrolled if directly admitted to the trauma center, >17 years of age, and with a shock index (heart rate/systolic blood pressure) >0.62. Upon admission, a Masimo Radical-7 co-oximeter sensor (Masimo Corporation, Irvine, CA) was applied, providing measurement of continuous non-invasive hemoglobin (SpHb) levels. Blood was drawn and hemoglobin concentration analyzed and conventional pulse oximetry photopletysmograph signals were continuously recorded. Demographic information and both prehospital and admission vital signs were collected. The primary outcome was transfusion of at least one unit of packed red blood cells within 24 h of admission. Eight regression models (C1–C8) were evaluated for the prediction of blood use by comparing area under receiver operating curve (AUROC) at different time intervals after admission. 711 subjects had continuous vital signs waveforms available, to include heart rate (HR), SpHb and SpO2 trends. When SpHb was monitored for 15 min, SpHb did not increase AUROC for prediction of transfusion. The highest ROC was recorded for model C8 (age, sex, prehospital shock index, admission HR, SpHb and SpO2) for the prediction of blood products within the first 3 h of admission. When data from 15 min of continuous monitoring were analyzed, significant improvement in AUROC occurred as more variables were added to the model; however, the addition of SpHb to any of the models did not improve AUROC significantly for prediction of blood use within the first 3 h of admission in comparison to analysis of conventional oximetry features. The results demonstrate that SpHb monitoring, accompanied by continuous vital signs data and adjusted for age and sex, has good accuracy for the prediction of need for transfusion; however, as an independent variable, SpHb did not enhance predictive models in comparison to use of features extracted from conventional pulse oximetry. Nor was shock index better than conventional oximetry at discriminating hemorrhaging and prediction of casualties receiving blood. In this population of trauma patients, noninvasive SpHb monitoring, including both trends and absolute values, did not enhance the ability to predict the need for blood transfusion.
Literature
1.
go back to reference World Health Organization. Injuries and violence: the facts. Geneva, World Health Organization, 2010. World Health Organization. Injuries and violence: the facts. Geneva, World Health Organization, 2010.
2.
4.
go back to reference Allen J. Photoplethysmography and its application in clinical physiological assessment. Physiol Meas. 2007;28:R1–39.CrossRefPubMed Allen J. Photoplethysmography and its application in clinical physiological assessment. Physiol Meas. 2007;28:R1–39.CrossRefPubMed
6.
go back to reference Guyette F, Gomez H, Suffoletto B, et al. Preshospital dynamic tissue saturation response predicts in-hospital lifesaving interventions in trauma patients. J Trauma Acute Care Surg. 2012;72:930–5.PubMedCentralCrossRefPubMed Guyette F, Gomez H, Suffoletto B, et al. Preshospital dynamic tissue saturation response predicts in-hospital lifesaving interventions in trauma patients. J Trauma Acute Care Surg. 2012;72:930–5.PubMedCentralCrossRefPubMed
7.
go back to reference McGee S, Abernethy WI, Simel D. The rational clinical examination: is this patient hypovolemic. JAMA. 1999;281:1022–9.CrossRefPubMed McGee S, Abernethy WI, Simel D. The rational clinical examination: is this patient hypovolemic. JAMA. 1999;281:1022–9.CrossRefPubMed
8.
go back to reference Gehring H, Hornberger C, Dibbelt L, et al. Accuracy of point-of-care-testing (POCT) for determining hemoglobin concentration. Acta Anaesthesiol Scand. 2002;46:980–6.CrossRefPubMed Gehring H, Hornberger C, Dibbelt L, et al. Accuracy of point-of-care-testing (POCT) for determining hemoglobin concentration. Acta Anaesthesiol Scand. 2002;46:980–6.CrossRefPubMed
9.
go back to reference Kim SH, Lilot M, Murphy LSK, et al. Accuracy of continuous noninvasive hemoglobin monitoring: a systematic review and meta-analysis. Anesth Analg. 2014;119:332–46.CrossRefPubMed Kim SH, Lilot M, Murphy LSK, et al. Accuracy of continuous noninvasive hemoglobin monitoring: a systematic review and meta-analysis. Anesth Analg. 2014;119:332–46.CrossRefPubMed
10.
go back to reference Patino M, Schultz L, Hossain M, Moeller J, Mahmoud M, Gunter J, Kurth CD. Trending and accuracy of noninvasive hemoglobin monitoring in pediatric perioperative patients. Anesth Analg. 2014;119(4):920–5. Patino M, Schultz L, Hossain M, Moeller J, Mahmoud M, Gunter J, Kurth CD. Trending and accuracy of noninvasive hemoglobin monitoring in pediatric perioperative patients. Anesth Analg. 2014;119(4):920–5.
11.
go back to reference de Biasi A, Stansbury L, Dutton R, Stein D, Scalea T, Hess JR. Blood product use in trauma resuscitation: plasma deficitversus plasma ratio as predictors of mortality in trauma. Transfusion. 2011;51:1925–32.PubMedCentralCrossRefPubMed de Biasi A, Stansbury L, Dutton R, Stein D, Scalea T, Hess JR. Blood product use in trauma resuscitation: plasma deficitversus plasma ratio as predictors of mortality in trauma. Transfusion. 2011;51:1925–32.PubMedCentralCrossRefPubMed
12.
go back to reference Mackenzie CF, Gao C, Hu PF, Anazodo A, Chen H, Dinardo T, Imle PC, Hartsky L, Stephens C, Menaker J, Fouche Y, Murdock K, Galvagno S, Alcorta R, Shackelford S, ONPOINT Study Group. Comparison of decision-assist and clinical judgment of experts for prediction of life saving interventions. Shock. 2014. doi:10.1097/SHK.0000000000000288. Mackenzie CF, Gao C, Hu PF, Anazodo A, Chen H, Dinardo T, Imle PC, Hartsky L, Stephens C, Menaker J, Fouche Y, Murdock K, Galvagno S, Alcorta R, Shackelford S, ONPOINT Study Group. Comparison of decision-assist and clinical judgment of experts for prediction of life saving interventions. Shock. 2014. doi:10.​1097/​SHK.​0000000000000288​.
13.
go back to reference Como J, Dutton R, Scalea T, Edeleman B, Hess J. Blood transfusion rates in the care of acute trauma. Transfusion. 2004;44:809–13.CrossRefPubMed Como J, Dutton R, Scalea T, Edeleman B, Hess J. Blood transfusion rates in the care of acute trauma. Transfusion. 2004;44:809–13.CrossRefPubMed
14.
go back to reference 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;77(3 Suppl 2):S222–7. 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;77(3 Suppl 2):S222–7.
15.
go back to reference Vandromme M, Griffin R, Kerby J, McGwin G, Rue L, Weinberg J. Identifying risk for massive transfusion in the relatively normotensive patient: utility of the prehospital shock index. J Trauma Acute Care Surg. 2011;70:384–8.CrossRef Vandromme M, Griffin R, Kerby J, McGwin G, Rue L, Weinberg J. Identifying risk for massive transfusion in the relatively normotensive patient: utility of the prehospital shock index. J Trauma Acute Care Surg. 2011;70:384–8.CrossRef
16.
go back to reference DeLong E, DeLong D, Clarke-Pearson D. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.CrossRefPubMed DeLong E, DeLong D, Clarke-Pearson D. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45.CrossRefPubMed
17.
go back to reference Stone M. Cross-validatory choice and assessment of statistical predictions (with discussion). J R Stat Soc B. 1974;36:111–47. Stone M. Cross-validatory choice and assessment of statistical predictions (with discussion). J R Stat Soc B. 1974;36:111–47.
18.
go back to reference Mackenzie C, Wang Y, Hu P, et al. Automated prediction of early blood transfusion and mortality in trauma patients. J Trauma Acute Care Surg. 2014;76:1379–85.CrossRefPubMed Mackenzie C, Wang Y, Hu P, et al. Automated prediction of early blood transfusion and mortality in trauma patients. J Trauma Acute Care Surg. 2014;76:1379–85.CrossRefPubMed
19.
go back to reference Ogura T, Nakamura Y, Nakano M, et al. Predicting the need for massive transfusion in trauma patients: the Traumatic Bleeding Severity Score. J Trauma Acute Care Surg. 2014;76:1243–50.CrossRefPubMed Ogura T, Nakamura Y, Nakano M, et al. Predicting the need for massive transfusion in trauma patients: the Traumatic Bleeding Severity Score. J Trauma Acute Care Surg. 2014;76:1243–50.CrossRefPubMed
Metadata
Title
Accuracy of continuous noninvasive hemoglobin monitoring for the prediction of blood transfusions in trauma patients
Authors
Samuel M. Galvagno Jr.
Peter Hu
Shiming Yang
Cheng Gao
David Hanna
Stacy Shackelford
Colin Mackenzie
Publication date
01-12-2015
Publisher
Springer Netherlands
Published in
Journal of Clinical Monitoring and Computing / Issue 6/2015
Print ISSN: 1387-1307
Electronic ISSN: 1573-2614
DOI
https://doi.org/10.1007/s10877-015-9671-1

Other articles of this Issue 6/2015

Journal of Clinical Monitoring and Computing 6/2015 Go to the issue