A Prospective Multicenter Comparison of Trauma and Injury Severity Score, American Society of Anesthesiologists Physical Status, and National Surgical Quality Improvement Program Calculator's Ability to Predict Operative Trauma Outcomes.
Journal
Anesthesia and analgesia
ISSN: 1526-7598
Titre abrégé: Anesth Analg
Pays: United States
ID NLM: 1310650
Informations de publication
Date de publication:
08 Dec 2023
08 Dec 2023
Historique:
medline:
13
12
2023
pubmed:
13
12
2023
entrez:
13
12
2023
Statut:
aheadofprint
Résumé
Trauma outcome prediction models have traditionally relied upon patient injury and physiologic data (eg, Trauma and Injury Severity Score [TRISS]) without accounting for comorbidities. We sought to prospectively evaluate the role of the American Society of Anesthesiologists physical status (ASA-PS) score and the National Surgical Quality Improvement Program Surgical Risk-Calculator (NSQIP-SRC), which are measurements of comorbidities, in the prediction of trauma outcomes, hypothesizing that they will improve the predictive ability for mortality, hospital length of stay (LOS), and complications compared to TRISS alone in trauma patients undergoing surgery within 24 hours. A prospective, observational multicenter study (9/2018-2/2020) of trauma patients ≥18 years undergoing operation within 24 hours of admission was performed. Multiple logistic regression was used to create models predicting mortality utilizing the variables within TRISS, ASA-PS, and NSQIP-SRC, respectively. Linear regression was used to create models predicting LOS and negative binomial regression to create models predicting complications. From 4 level I trauma centers, 1213 patients were included. The Brier Score for each model predicting mortality was found to improve accuracy in the following order: 0.0370 for ASA-PS, 0.0355 for NSQIP-SRC, 0.0301 for TRISS, 0.0291 for TRISS+ASA-PS, and 0.0234 for TRISS+NSQIP-SRC. However, when comparing TRISS alone to TRISS+ASA-PS (P = .082) and TRISS+NSQIP-SRC (P = .394), there was no significant improvement in mortality prediction. NSQIP-SRC more accurately predicted both LOS and complications compared to TRISS and ASA-PS. TRISS predicts mortality better than ASA-PS and NSQIP-SRC in trauma patients undergoing surgery within 24 hours. The TRISS mortality predictive ability is not improved when combined with ASA-PS or NSQIP-SRC. However, NSQIP-SRC was the most accurate predictor of LOS and complications.
Sections du résumé
BACKGROUND
BACKGROUND
Trauma outcome prediction models have traditionally relied upon patient injury and physiologic data (eg, Trauma and Injury Severity Score [TRISS]) without accounting for comorbidities. We sought to prospectively evaluate the role of the American Society of Anesthesiologists physical status (ASA-PS) score and the National Surgical Quality Improvement Program Surgical Risk-Calculator (NSQIP-SRC), which are measurements of comorbidities, in the prediction of trauma outcomes, hypothesizing that they will improve the predictive ability for mortality, hospital length of stay (LOS), and complications compared to TRISS alone in trauma patients undergoing surgery within 24 hours.
METHODS
METHODS
A prospective, observational multicenter study (9/2018-2/2020) of trauma patients ≥18 years undergoing operation within 24 hours of admission was performed. Multiple logistic regression was used to create models predicting mortality utilizing the variables within TRISS, ASA-PS, and NSQIP-SRC, respectively. Linear regression was used to create models predicting LOS and negative binomial regression to create models predicting complications.
RESULTS
RESULTS
From 4 level I trauma centers, 1213 patients were included. The Brier Score for each model predicting mortality was found to improve accuracy in the following order: 0.0370 for ASA-PS, 0.0355 for NSQIP-SRC, 0.0301 for TRISS, 0.0291 for TRISS+ASA-PS, and 0.0234 for TRISS+NSQIP-SRC. However, when comparing TRISS alone to TRISS+ASA-PS (P = .082) and TRISS+NSQIP-SRC (P = .394), there was no significant improvement in mortality prediction. NSQIP-SRC more accurately predicted both LOS and complications compared to TRISS and ASA-PS.
CONCLUSIONS
CONCLUSIONS
TRISS predicts mortality better than ASA-PS and NSQIP-SRC in trauma patients undergoing surgery within 24 hours. The TRISS mortality predictive ability is not improved when combined with ASA-PS or NSQIP-SRC. However, NSQIP-SRC was the most accurate predictor of LOS and complications.
Identifiants
pubmed: 38091502
doi: 10.1213/ANE.0000000000006802
pii: 00000539-990000000-00675
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2023 International Anesthesia Research Society.
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest.
Références
Centers for Disease Control and Prevention. WISQARS (Web-based Injury Statistics Query and Reporting System). Published August 1, 2020. Accessed August 21, 2020. https://www.cdc.gov/injury/wisqars/index.html
Coimbra R, Kozar RA, Smith JW, et al. The coalition for national trauma research supports the call for a national trauma research action plan. J Trauma Acute Care Surg. 2017;82:637–645.
Baker SP, O’Neill B, Haddon W Jr, Long WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma. 1974;14:187–196.
Glance LG, Osler TM, Mukamel DB, Meredith W, Dick AW. Expert consensus vs empirical estimation of injury severity: effect on quality measurement in trauma. Arch Surg. 2009;144:326–32; .
Linn S. The injury severity score--importance and uses. Ann Epidemiol. 1995;5:440–446.
Boyd CR, Tolson MA, Copes WS. Evaluating trauma care: the TRISS method trauma score and the injury severity score. J Trauma. 1987;27:370–378.
Champion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the trauma score. J Trauma. 1989;29:623–629.
Singh J, Gupta G, Garg R, Gupta A. Evaluation of trauma and prediction of outcome using TRISS method. J Emerg Trauma Shock. 2011;4:446–449.
Brockamp T, Maegele M, Gaarder C, et al. Comparison of the predictive performance of the BIG, TRISS, and PS09 score in an adult trauma population derived from multiple international trauma registries. Crit Care. 2013;17:R134.
Chico-Fernández M, Llompart-Pou JA, Sánchez-Casado M, et al.; in representation of the Trauma and Neurointensive Care Working Group of the SEMICYUC. Mortality prediction using TRISS methodology in the Spanish ICU Trauma Registry (RETRAUCI). Med Intensiva. 2016;40:395–402.
Terzian WTH, Hoey BA, Hoff WS, et al. Getting “more mileage” out of the Trauma and Injury Severity Score: extending the paradigm to morbidity and length of stay predictions. J Am Coll Surg. 2017;225:S54–S55.
de Munter L, Polinder S, Lansink KW, Cnossen MC, Steyerberg EW, de Jongh MA. Mortality prediction models in the general trauma population: a systematic review. Injury. 2017;48:221–229.
Schluter PJ, Cameron CM, Davey TM, et al. Using trauma injury severity score (TRISS) variables to predict length of hospital stay following trauma in New Zealand. N Z Med J. 2009;122:65–78.
Rogers FB, Osler T, Krasne M, et al. Has TRISS become an anachronism? A comparison of mortality between the National Trauma Data Bank and Major Trauma Outcome Study databases. J Trauma Acute Care Surg. 2012;73:326–31; .
Valderrama-Molina CO, Giraldo N, Constain A, et al. Validation of trauma scales: ISS, NISS, RTS and TRISS for predicting mortality in a Colombian population. Eur J Orthop Surg Traumatol. 2017;27:213–220.
Skaga NO, Eken T, Søvik S, Jones JM, Steen PA. Pre-injury ASA physical status classification is an independent predictor of mortality after trauma. J Trauma. 2007;63:972–978.
Ringdal KG, Skaga NO, Steen PA, et al. Classification of comorbidity in trauma: the reliability of pre-injury ASA physical status classification. Injury. 2013;44:29–35.
Jones JM, Skaga NO, Søvik S, Lossius HM, Eken T. Norwegian survival prediction model in trauma: modelling effects of anatomic injury, acute physiology, age, and co-morbidity. Acta Anaesthesiol Scand. 2014;58:303–315.
Kuza CM, Matsushima K, Mack WJ, et al. The role of the American Society of Anesthesiologists Physical Status classification in predicting trauma mortality and outcomes. Am J Surg. 2019;218:1143–1151.
Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg. 2013;217:833–42.e1.
American College of Surgeons. ACS NSQIP surgical risk calculator. Accessed August 21, 2020. https://riskcalculator.facs.org/RiskCalculator/index.jsp
von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–349.
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42:377–381.
King G, Zeng L. Logistic regression in rare events data. Polit Anal. 2001;9:137–163.
Florkowski CM. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. Clin Biochem Rev. 2008;29(suppl 1):S83–S87.
Barron D. The analysis of count data: overdispersion and autocorrelation. Sociol Methodol. 1992;22:179–220.
Cameron AC, Windmeijer F. An R-squared measure of goodness of fit for some common nonlinear regression models. J Econom. 1997;77:329–342.
Chen WS, Tan JH, Mohamad Y, Imran R. External validation of a modified Trauma and Injury Severity Score model in major trauma injury. Injury. 2019;50:1118–1124.
Raj R, Brinck T, Skrifvars MB, Handolin L. External validation of the Norwegian survival prediction model in trauma after major trauma in Southern Finland. Acta Anaesthesiol Scand. 2016;60:48–58.
Skaga NO, Eken T, Søvik S. Validating performance of TRISS, TARN and NORMIT survival prediction models in a Norwegian trauma population. Acta Anaesthesiol Scand. 2018;62:253–266.
Ghorbani P, Troëng T, Brattström O, et al. Validation of the Norwegian survival prediction model in trauma (NORMIT) in Swedish trauma populations. Br J Surg. 2020;107:381–390.
Lefering R, Huber-Wagner S, Nienaber U, Maegele M, Bouillon B. Update of the trauma risk adjustment model of the TraumaRegister DGU™: the revised injury severity classification, version II. Crit Care. 2014;18:476.
de Munter L, Ter Bogt NCW, Polinder S, Sewalt CA, Steyerberg EW, de Jongh MAC. Improvement of the performance of survival prediction in the ageing blunt trauma population: a cohort study. PLoS One. 2018;13:e0209099.
Konda SR, Parola R, Perskin C, Egol KA. ASA physical status classification improves predictive ability of a validated trauma risk score. Geriatr Orthop Surg Rehabil. 2021;12:2151459321989534.
Keefler J, Duder S, Lechman C. Predicting length of stay in an acute care hospital: the role of psychosocial problems. Soc Work Health Care. 2001;33:1–16.
Englum BR, Hui X, Zogg CK, et al. Association between insurance status and hospital length of stay following trauma. Am Surg. 2016;82:281–288.
Moore L, Cisse B, Batomen Kuimi BL, et al. Impact of socio-economic status on hospital length of stay following injury: a multicenter cohort study. BMC Health Serv Res. 2015;15:285.
Perelman J, Closon MC. Impact of socioeconomic factors on in-patient length of stay and their consequences in per case hospital payment systems. J Health Serv Res Policy. 2011;16:197–202.
Stocker B, Weiss HK, Weingarten N, Engelhardt K, Engoren M, Posluszny J. Predicting length of stay for trauma and emergency general surgery patients. Am J Surg. 2020;220:757–764.
Kuza CM, Hatzakis G, Nahmias JT. The assignment of American Society of Anesthesiologists Physical Status classification for adult polytrauma patients: results from a survey and future considerations. Anesth Analg. 2017;125:1960–1966.