Comparison of emergency department trauma triage performance of clinicians and clinical prediction models: a cohort study in India

Ludvig Wärnberg Gerdin, Monty Khajanchi, Vineet Kumar, Nobhojit Roy, Makhan Lal Saha, Kapil Dev Soni, Anurag Mishra, Jyoti Kamble, Nitin Borle, Chandrika Prasad Verma, Martin Gerdin Wärnberg, Ludvig Wärnberg Gerdin, Monty Khajanchi, Vineet Kumar, Nobhojit Roy, Makhan Lal Saha, Kapil Dev Soni, Anurag Mishra, Jyoti Kamble, Nitin Borle, Chandrika Prasad Verma, Martin Gerdin Wärnberg

Abstract

Objective: The aim of this study was to evaluate and compare the abilities of clinicians and clinical prediction models to accurately triage emergency department (ED) trauma patients. We compared the decisions made by clinicians with the Revised Trauma Score (RTS), the Glasgow Coma Scale, Age and Systolic Blood Pressure (GAP) score, the Kampala Trauma Score (KTS) and the Gerdin et al model.

Design: Prospective cohort study.

Setting: Three hospitals in urban India.

Participants: In total, 7697 adult patients who presented to participating hospitals with a history of trauma were approached for enrolment. The final study sample included 5155 patients. The majority (4023, 78.0%) were male.

Main outcome measure: The patient outcome was mortality within 30 days of arrival at the participating hospital. A grid search was used to identify model cut-off values. Clinicians and categorised models were evaluated and compared using the area under the receiver operating characteristics curve (AUROCC) and net reclassification improvement in non-survivors (NRI+) and survivors (NRI-) separately.

Results: The differences in AUROCC between each categorised model and the clinicians were 0.016 (95% CI -0.014 to 0.045) for RTS, 0.019 (95% CI -0.007 to 0.058) for GAP, 0.054 (95% CI 0.033 to 0.077) for KTS and -0.007 (95% CI -0.035 to 0.03) for Gerdin et al. The NRI+ for each model were -0.235 (-0.37 to -0.116), 0.17 (-0.042 to 0.405), 0.55 (0.47 to 0.65) and 0.22 (0.11 to 0.717), respectively. The NRI- were 0.385 (0.348 to 0.4), -0.059 (-0.476 to -0.005), -0.162 (-0.18 to -0.146) and 0.039 (-0.229 to 0.06), respectively.

Conclusion: The findings of this study suggest that there are no substantial differences in discrimination and net reclassification improvement between clinicians and all four clinical prediction models when using 30-day mortality as the outcome of ED trauma triage in adult patients.

Trial registration number: ClinicalTrials.gov Registry (NCT02838459).

Keywords: accident & emergency medicine; epidemiology; trauma management.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
Study flowchart. 1002 patients were excluded from final analysis because they arrived at or after the date when data on the 400th non-surviving patient was collected.
Figure 2
Figure 2
Receiver operating characteristic curves for categorised (A) and continuous models (B) in the comparison sample. GAP, Glasgow Coma Scale, Age and Systolic Blood Pressure; KTS, Kampala Trauma Score; RTS, Revised Trauma Score

References

    1. Roth GA, Abate D, Abate KH, et al. . Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the global burden of disease study 2017. Lancet 2018;392:1736–88. 10.1016/S0140-6736(18)32203-7
    1. Kondo Y, Abe T, Kohshi K, et al. . Revised trauma scoring system to predict in-hospital mortality in the emergency department: Glasgow coma scale, age, and systolic blood pressure score. Crit Care 2011;15:R191 10.1186/cc10348
    1. Fitzgerald M, et al. Trauma resuscitation errors and computer-assisted decision support. Arch Surg 2011;146:218–25. 10.1001/archsurg.2010.333
    1. Rehn M, Perel P, Blackhall K, et al. . Prognostic models for the early care of trauma patients: a systematic review. Scand J Trauma Resusc Emerg Med 2011;19:17 10.1186/1757-7241-19-17
    1. Champion HR, Sacco WJ, Copes WS, et al. . A revision of the trauma score. J Trauma 1989;29:623–9. 10.1097/00005373-198905000-00017
    1. Kobusingye OC, Lett RR. Hospital-Based trauma registries in Uganda. J Trauma 2000;48:498–502. 10.1097/00005373-200003000-00022
    1. Gerdin M, Roy N, Dharap S, et al. . Early hospital mortality among adult trauma patients significantly declined between 1998-2011: three single-centre cohorts from Mumbai, India. PLoS One 2014;9:e90064 10.1371/journal.pone.0090064
    1. Laytin AD, Kumar V, Juillard CJ, et al. . Choice of injury scoring system in low- and middle-income countries: lessons from Mumbai. Injury 2015;46:2491–7. 10.1016/j.injury.2015.06.029
    1. Weeks SR, Stevens KA, Haider AH, et al. . A modified Kampala trauma score (KTS) effectively predicts mortality in trauma patients. Injury 2016;47:125–9. 10.1016/j.injury.2015.07.004
    1. Gardner A, Forson PK, Oduro G, et al. . Diagnostic accuracy of the Kampala trauma score using estimated abbreviated injury scale scores and physician opinion. Injury 2017;48:177–83. 10.1016/j.injury.2016.11.022
    1. Gerdin M, Roy N, Khajanchi M, et al. . Predicting early mortality in adult trauma patients admitted to three public university hospitals in urban India: a prospective multicentre cohort study. PLoS One 2014;9:e105606–7. 10.1371/journal.pone.0105606
    1. Pencina MJ, D'Agostino RB, D'Agostino RB, et al. . Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157–72. 10.1002/sim.2929
    1. R Core Team R: A language and environment for statistical computing [Internet. Vienna, Austria: R Foundation for Statistical Computing, 2017.
    1. Efron B. Bootstrap methods: another look at the jackknife. The Annals of Statistics 1979;7:1–26. 10.1214/aos/1176344552
    1. Steyerberg EW. Clinical Prediction Models - A Practical Approach to Development, Validation, and Updating. New York: Springer-Verlag New York, 2009.
    1. Iversen AKS, Kristensen M, Østervig RM, et al. . A simple clinical assessment is superior to systematic triage in prediction of mortality in the emergency department. Emerg Med J 2019. 10.1136/emermed-2016-206382. [Epub ahead of print: 16 Oct 2018].
    1. Mahajan P, Kuppermann N, Tunik M, et al. . Comparison of clinician suspicion versus a clinical prediction rule in identifying children at risk for intra-abdominal injuries after blunt torso trauma. Acad Emerg Med 2015;22:1034–41. 10.1111/acem.12739
    1. Pommerening MJ, Goodman MD, Holcomb JB, et al. . Clinical gestalt and the prediction of massive transfusion after trauma. Injury 2015;46:807–13. 10.1016/j.injury.2014.12.026
    1. Penaloza A, Verschuren F, Meyer G, et al. . Comparison of the unstructured clinician gestalt, the wells score, and the revised Geneva score to estimate pretest probability for suspected pulmonary embolism. Ann Emerg Med 2013;62:117–24. 10.1016/j.annemergmed.2012.11.002
    1. Christian MD, Sprung CL, King MA, et al. . Triage: care of the critically ill and injured during pandemics and disasters: chest consensus statement. Chest 2014;146:e61S–74. 10.1378/chest.14-0736
    1. Mohan D, Barnato AE, Rosengart MR, et al. . Trauma triage in the emergency departments of nontrauma centers: an analysis of individual physician caseload on triage patterns. J Trauma Acute Care Surg 2013;74:1541–7. 10.1097/TA.0b013e31828c3f75
    1. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 2014;35:1925–31. 10.1093/eurheartj/ehu207
    1. South African Triage Group The South African Triage Scale Training Manual 2012 [Internet. Westerns Cape Government, 2012: 1–34.

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