Out-of-hospital Circulatory Measures to Identify Patients With Serious Injury: A Systematic Review.
Journal
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
ISSN: 1553-2712
Titre abrégé: Acad Emerg Med
Pays: United States
ID NLM: 9418450
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
05
02
2020
accepted:
12
06
2020
pubmed:
20
6
2020
medline:
29
12
2020
entrez:
20
6
2020
Statut:
ppublish
Résumé
The objective was to systematically identify and summarize out-of-hospital measures of circulatory compromise as diagnostic predictors of serious injury, focusing on measures usable by emergency medical services to inform field triage decisions. We searched Ovid MEDLINE, CINAHL, and the Cochrane databases from 1996 through August 2017 for published literature on individual circulatory measures in trauma. We reviewed reference lists of included articles for additional relevant citations. Measures of diagnostic accuracy included sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Indicators of serious injury included resource need, serious anatomic injury, and mortality. We pooled estimates when data permitted. We identified 114 articles, reporting results of 111 studies. Measures included systolic blood pressure (sBP), heart rate (HR), shock index (SI), lactate, base deficit, and HR variability. Pooled out-of-hospital sensitivity estimates were sBP < 90 mm Hg = 19% (95% confidence interval [CI] = 12% to 29%), HR ≥ 110 beats/min = 28% (95% CI = 20% to 37%), SI > 0.9 = 37% (95% CI = 22% to 56%), and lactate > 2.0 mmol/L = 74% (95% CI = 48% to 90%). Pooled specificity estimates were sBP < 90 mm Hg = 95% (95% CI = 91% to 97%), HR ≥ 110 beats/min = 85% (95% CI = 74% to 91%), SI > 0.9 = 85% (95% CI = 72% to 92%), and lactate > 2.0 mmol/L = 62% (95% CI = 51% to 72%). Pooled AUROCs included sBP = 0.67 (95% CI = 0.58 to 0.75), HR = 0.67 (95% CI = 0.56 to 0.79), SI = 0.72 (95% CI = 0.66 to 0.77), and lactate = 0.77 (95% CI = 0.67 to 0.82). Strength of evidence was low to moderate. Out-of-hospital circulatory measures are associated with poor to fair discrimination for identifying trauma patients with serious injuries. Many seriously injured patients have normal circulatory measures (low sensitivity), but when present, the measures are highly specific for identifying patients with serious injuries.
Types de publication
Journal Article
Review
Comment
Langues
eng
Sous-ensembles de citation
IM
Pagination
1323-1339Commentaires et corrections
Type : CommentOn
Informations de copyright
© 2020 by the Society for Academic Emergency Medicine.
Références
Wang HE, Mann NC, Jacobson KE, et al. National characteristics of emergency medical services responses in the United States. Prehosp Emerg Care 2013;17:8-14.
MacKenzie EJ, Rivara FP, Jurkovich GJ, et al. A national evaluation of the effect of trauma-center care on mortality. N Engl J Med 2006;354:366-78.
Haas B, Gomez D, Zagorski B, Stukel TA, Rubenfeld GD, Nathens AB. Survival of the fittest: the hidden cost of undertriage of major trauma. J Am Coll Surg 2010;211:804-11.
Sasser SM, Hunt RC, Faul M, et al. Guidelines for field triage of injured patients recommendations of the National Expert Panel on Field Triage, 2011. MMWR Recomm Rep 2012;61:1-21.
Mackersie RC. History of trauma field triage development and the American College of Surgeons criteria. Prehosp Emerg Care 2006;10:287-94.
Newgard CD, Fu R, Zive D, et al. Prospective validation of the national field triage guidelines for identifying seriously injured persons. J Am Coll Surg 2016;222:146-58.e2.
Chou R, Totten AM, Carney N, et al. Predictive utility of the total Glasgow Coma Scale versus the motor component of the Glasgow Coma Scale for identification of patients with serious traumatic injuries. Ann Emerg Med 2017;70:143-57.e6.
Daya MR, Cheney TP. Chou R, et al. Out-of hospital measures to identify patients with serious injury: a systematic review. Acad Emerg Med 2020;27:1312-1322.
Totten AM, Cheney TP, O'Neil ME, et al. Comparative Effectiveness Review No. 205. (Prepared by the Pacific Northwest Evidence-based Practice Center under Contract No. 290-2015-00009-I.) AHRQ Publication No. 18-EHC008-EF. Rockville, MD: Agency for Healthcare Research and Quality, 2018.
Samson D, Schoelles KM. Chapter 2: medical tests guidance (2) developing the topic and structuring systematic reviews of medical tests: utility of PICOTS, analytic frameworks, decision trees, and other frameworks. J Gen Intern Med 2012;27(Suppl 1):S11-S19.
Methods Guide for Effectiveness and Comparative Effectiveness Reviews. AHRQ Publication No. 10(14)-EHC063-EF. Rockville, MD: Agency for Healthcare Research and Quality, 2014.
Bazzoli GJ, Madura KJ, Cooper GF, MacKenzie EJ, Maier RV. Progress in the development of trauma systems in the United States. Results of a national survey. JAMA 1995;273:395-401.
Hayden JA, van der Windt DA, Cartwright JL, Cote P, Bombardier C. Assessing bias in studies of prognostic factors. Ann Intern Med 2013;158:280-6.
Singh S, Chang SM, Matchar DB, Bass EB. Chapter 7: grading a body of evidence on diagnostic tests. J Gen Intern Med 2012;27(Suppl 1):S47-55.
Hartmann KE, Matchar DB, Chang S. Chapter 6: assessing applicability of medical test studies in systematic reviews. J Gen Intern Med 2012;27(Suppl 1):S39-46.
Hosmer DW, Lemeshow S, Sturdivant RX. Assessing the Fit of the Model. Applied Logistic Regression. 3rd ed. Hoboken, NJ: John Wiley & Sons, Inc., 2013. pp. 153-226.
Chu H, Cole SR. Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol 2006;59:1331-2; author reply 2-3.
Trikalinos TA, Balion CM, Coleman CI, et al. Chapter 8: meta-analysis of test performance when there is a "gold standard". J Gen Intern Med 2012;27(Suppl 1):S56-66.
Zwinderman AH, Bossuyt PM. We should not pool diagnostic likelihood ratios in systematic reviews. Stat Med 2008;27:687-97.
Hardy RJ, Thompson SG. A likelihood approach to meta-analysis with random effects. Stat Med 1996;15:619-29.
Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.
Fu R, Vandermeer BW, Shamliyan TA, et al. Handling Continuous Outcomes in Quantitative Synthesis. Methods Guide for Comparative Effectiveness Reviews (Prepared by the Oregon Evidence-based Practice Center under Contract No. 290-2007-10057-I.) AHRQ Publication No. 13-EHC103-EF. Rockville, MD: Agency for Healthcare Research and Quality, 2013.
Emmett M, Szerlip H. Causes of Lactic Acidosis. UpToDate. Alphen aan den Rijn, the Netherlands: Wolters Kluwer, 2017.
Fluid RA. Electrolyte and Acid-Base Disorders: Clinical Evaluation and Management Chapter 28: High Anion Gap Metabolic Acidosis. New York: Spriger, 2014. pp. 319-46.
Andersen LW, Mackenhauer J, Roberts JC, Berg KM, Cocchi MN, Donnino MW. Etiology and therapeutic approach to elevated lactate levels. Mayo Clin Proc 2013;88:1127-40.
Kraut JA, Madias NE. Lactic acidosis. N Engl J Med 2014;371:2309-19.
Baxter J, Cranfield KR, Clark G, Harris T, Bloom B, Gray AJ. Do lactate levels in the emergency department predict outcome in adult trauma patients? A systematic review. J Trauma Acute Care Surg 2016;81:555-66.
Best HK. Evidence topic report. BET 2. Serum lactate as a marker for mortality in patients presenting to the emergency department with trauma. Emerg Med J 2009;26:118-9.
Guyette FX, Meier EN, Newgard C, et al. A comparison of prehospital lactate and systolic blood pressure for predicting the need for resuscitative care in trauma transported by ground. J Trauma Acute Care Surg 2015;78:600-6.
Davis JW, Davis IC, Bennink LD, Bilello JF, Kaups KL, Parks SN. Are automated blood pressure measurements accurate in trauma patients? J Trauma 2003;55:860-3.
Vettorello M, Santambrogio SM, Calini AR, et al. Predicting haemorrhage in pre-hospital traumatic patients: evaluation of the novel heart-to-arm time index. Acta Anaesthesiol Scand 2013;57:929-35.
Edla S, Reisner AT, Liu J, Convertino VA, Carter R 3rd, Reifman J. Is heart rate variability better than routine vital signs for prehospital identification of major hemorrhage? Am J Emerg Med 2015;33:254-61.
King DR, Ogilvie MP, Pereira BM, et al. Heart rate variability as a triage tool in patients with trauma during prehospital helicopter transport. J Trauma 2009;67:436-40.
Chen L, Reisner AT, Gribok A, McKenna TM, Reifman J. Can we improve the clinical utility of respiratory rate as a monitored vital sign? Shock 2009;31:574-80.
Chen L, Reisner AT, McKenna TM, Gribok A, Reifman J. Diagnosis of hemorrhage in a prehospital trauma population using linear and nonlinear multiparameter analysis of vital signs. 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Conf Proc IEEE Eng Med Biol Soc ed. Lyon, France: IEEE, 2007. pp. 3748-51.
Chen L, McKenna TM, Reisner AT, Gribok A, Reifman J. Decision tool for the early diagnosis of trauma patient hypovolemia. J Biomed Inform 2008;41:469-78.
Pottecher J, Ageron FX, Fauche C, et al. Prehospital shock index and pulse pressure/heart rate ratio to predict massive transfusion after severe trauma: retrospective analysis of a large regional trauma database. J Trauma Acute Care Surg 2016;81:713-22.
McManus J, Yershov AL, Ludwig D, et al. Radial pulse character relationships to systolic blood pressure and trauma outcomes. Prehosp Emerg Care 2005;9:423-8.
Holcomb JB, Niles SE, Miller CC, Hinds D, Duke JH, Moore FA. Prehospital physiologic data and lifesaving interventions in trauma patients. Mil Med 2005;170:7-13.
Chen L, Reisner AT, Gribok A, Reifman J. Is respiration-induced variation in the photoplethysmogram associated with major hypovolemia in patients with acute traumatic injuries? Shock 2010;34:455-60.
Grimme K, Pape HC, Probst C, et al. Calculation of different triage scores based on the German Trauma Registry: value of the shock index. Eur J Traum 2005;31:480-7.
Van Haren RM, Thorson CM, Valle EJ, et al. Novel prehospital monitor with injury acuity alarm to identify trauma patients who require lifesaving intervention. J Trauma Acute Care Surg 2014;76:743-9.
Woodford MR, Mackenzie CF, DuBose J, et al. Continuously recorded oxygen saturation and heart rate during prehospital transport outperform initial measurement in prediction of mortality after trauma. J Trauma Acute Care Surg 2012;72:1006-11.
Haider AA, Azim A, Rhee P, et al. Substituting systolic blood pressure with shock index in the National Trauma Triage Protocol. J Trauma Acute Care Surg 2016;81:1136-41.