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

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 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 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 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.

[1]  K. Egol,et al.  ASA Physical Status Classification Improves Predictive Ability of a Validated Trauma Risk Score , 2021, Geriatric orthopaedic surgery & rehabilitation.

[2]  M. Engoren,et al.  Predicting length of stay for trauma and emergency general surgery patients. , 2020, American journal of surgery.

[3]  Alexander L. Eastman,et al.  The role of the American Society of anesthesiologists physical status classification in predicting trauma mortality and outcomes. , 2019, American journal of surgery.

[4]  T. Eken,et al.  Validation of the Norwegian survival prediction model in trauma (NORMIT) in Swedish trauma populations , 2019, The British journal of surgery.

[5]  J. Tan,et al.  External validation of a modified trauma and injury severity score model in major trauma injury. , 2019, Injury.

[6]  E. Steyerberg,et al.  Improvement of the performance of survival prediction in the ageing blunt trauma population: A cohort study , 2018, PloS one.

[7]  J. Nahmias,et al.  The Assignment of American Society of Anesthesiologists Physical Status Classification for Adult Polytrauma Patients: Results From a Survey and Future Considerations , 2017, Anesthesia and analgesia.

[8]  T. Eken,et al.  Validating performance of TRISS, TARN and NORMIT survival prediction models in a Norwegian trauma population , 2017, Acta anaesthesiologica Scandinavica.

[9]  W. Hoff,et al.  Getting “More Mileage” Out of the Trauma and Injury Severity Score: Extending the Paradigm to Morbidity and Length of Stay Predictions , 2017 .

[10]  K. Davis,et al.  The Coalition for National Trauma Research supports the call for a national trauma research action plan , 2017, The journal of trauma and acute care surgery.

[11]  E. Steyerberg,et al.  Mortality prediction models in the general trauma population: A systematic review. , 2017, Injury.

[12]  C. Restrepo,et al.  Validation of trauma scales: ISS, NISS, RTS and TRISS for predicting mortality in a Colombian population , 2017, European Journal of Orthopaedic Surgery & Traumatology.

[13]  J. Egea-Guerrero,et al.  Mortality prediction using TRISS methodology in the Spanish ICU Trauma Registry (RETRAUCI). , 2016, Medicina intensiva.

[14]  E. Haut,et al.  Association between Insurance Status and Hospital Length of Stay following Trauma , 2016, The American surgeon.

[15]  L. Handolin,et al.  External validation of the Norwegian survival prediction model in trauma after major trauma in Southern Finland , 2016, Acta anaesthesiologica Scandinavica.

[16]  H. Stelfox,et al.  Impact of socio-economic status on hospital length of stay following injury: a multicenter cohort study , 2015, BMC Health Services Research.

[17]  Rolf Lefering,et al.  Update of the trauma risk adjustment model of the TraumaRegister DGU™: the Revised Injury Severity Classification, version II , 2014, Critical Care.

[18]  T. Eken,et al.  Norwegian survival prediction model in trauma: modelling effects of anatomic injury, acute physiology, age, and co-morbidity , 2014, Acta anaesthesiologica Scandinavica.

[19]  C. Ko,et al.  Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. , 2013, Journal of the American College of Surgeons.

[20]  M. Cohen,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 , 2013, Critical Care.

[21]  T. Osler,et al.  Has TRISS become an anachronism? A comparison of mortality between the National Trauma Data Bank and Major Trauma Outcome Study databases , 2012, The journal of trauma and acute care surgery.

[22]  J. Perelman,et al.  Impact of socioeconomic factors on in-patient length of stay and their consequences in per case hospital payment systems , 2011, Journal of health services research & policy.

[23]  Jaspal R Singh,et al.  Evaluation of trauma and prediction of outcome using TRISS method , 2011, Journal of emergencies, trauma, and shock.

[24]  R. McClure,et al.  Using Trauma Injury Severity Score (TRISS) variables to predict length of hospital stay following trauma in New Zealand. , 2009, The New Zealand medical journal.

[25]  T. Osler,et al.  Expert consensus vs empirical estimation of injury severity: effect on quality measurement in trauma. , 2009, Archives of surgery.

[26]  P. Harris,et al.  Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support , 2009, J. Biomed. Informatics.

[27]  C. Florkowski.  Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. , 2008, The Clinical biochemist. Reviews.

[28]  E. Elm,et al.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. , 2008, Journal of clinical epidemiology.

[29]  T. Eken,et al.  Pre-injury ASA physical status classification is an independent predictor of mortality after trauma. , 2007, The Journal of trauma.

[30]  C. Lechman,et al.  Predicting Length of Stay in an Acute Care Hospital , 2001, Social work in health care.

[31]  F. Windmeijer,et al.  An R-squared measure of goodness of fit for some common nonlinear regression models , 1997 .

[32]  S. Linn.  The injury severity score--importance and uses. , 1995, Annals of epidemiology.

[33]  T A Gennarelli,et al.  A revision of the Trauma Score. , 1989, The Journal of trauma.

[34]  W. Copes,et al.  Evaluating trauma care : the TRISS method. Trauma Score and the Injury Severity Score , 1987 .

[35]  W. Haddon,et al.  The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. , 1974, The Journal of trauma.

[36]  P. Laake,et al.  Classification of comorbidity in trauma: the reliability of pre-injury ASA physical status classification. , 2013, Injury.

[37]  Gary King,et al.  Logistic Regression in Rare Events Data , 2001, Political Analysis.

[38]  David Barron,et al.  The Analysis of Count Data: Over-dispersion and Autocorrelation , 1992 .