Sage Journals: Discover world-class research

Abstract

Introduction

Trauma accounts for 9% of all deaths worldwide, killing almost five million people annually. As India accounts for more than one million of these deaths, research on local trauma care is of great importance. A key aspect of such research is outcome comparisons between contexts. One tool to adjust these comparisons for trauma severity is the International Classification of Diseases Injury Severity Score. The aim was to assess two versions of this score in India.

Methods

The data used were from the project Towards Improved Trauma Care Outcomes in India. Published survival risk ratios were used to calculate multiplicative-International Classification of Diseases Injury Severity Score and single-worst-injury-International Classification of Diseases Injury Severity Score for the 200 most recent non-surviving patients and the surviving patients during the same period. Score performance was measured in discrimination and calibration.

Results

The 30-day prediction single-worst-injury-International Classification of Diseases Injury Severity Score discriminated best with an area under the receiver operating characteristics curve of 0.668 (95% CI 0.645–0.690) and a calibration slope of 0.830 (95% CI 0.708–0.940).

Conclusions

The single-worst-injury-International Classification of Diseases Injury Severity Score applied on 30-day mortality was the only score to calibrate on a satisfactory level. None of the scores had an acceptable discrimination. In interpreting these findings, we see that none of the tested scores can currently be implemented in the studied hospitals.

Keywords

International Classification of Diseases Injury Severity Score India injury trauma

Get full access to this article

View all access options for this article.

References

World Health Organization. Injuries and violence: The facts 2014. Report, 2014. http://www.who.int/violence_injury_prevention/media/news/2015/Injury_violence_facts_2014/en/.

Naghavi

Wang

Lozano

, et al. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014; 385: 117–171.

Gruen

Gabbe

Stelfox

, et al. Indicators of the quality of trauma care and the performance of trauma systems. Br J Surg 2012; 99: 97–104.

Baker

O’Neill

Haddon

, et al. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma 1974; 14: 187–196.

Osler

Rutledge

Deis

, et al. ICISS: an international classification of disease-9 based injury severity score. J Trauma 1996; 41: 380–388.

Kilgo

Osler

Meredith

. The worst injury predicts mortality outcome the best: rethinking the role of multiple injuries in trauma outcome scoring. J Trauma 2003; 55: 599–607.

Meredith

Evans

Kilgo

, et al. A comparison of the abilities of nine scoring algorithms in predicting mortality. J Trauma 2002; 53: 621–629.

Gururaj

. Road traffic deaths, injuries and disabilities in India: current scenario. Natl Med J India 2008; 21: 14–20.

Pohlman TH, Offner P and Bjerke HS. Trauma scoring systems. Medscape. http://emedicine.medscape.com/article/434076-overview#showall (2014, accessed 4 April 2018).

10.

Gerdin

Roy

Khajanchi

, 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: 1–7.

11.

Gerdin

Roy

Khajanchi

, et al. Validation of a novel prediction model for early mortality in adult trauma patients in three public university hospitals in urban India. BMC Emerg Med 2016; 16: 1–12.

12.

Roy

Gerdin

Ghosh

, et al. 30-day in-hospital trauma mortality in four urban university hospitals using an Indian Trauma Registry. World J Surg 2016; 40: 1299–1307.

13.

Steyerberg

Borsboom

GJJM

van Houwelingen

, et al. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med 2004; 23: 2567–2586.

14.

Vergouwe

Steyerberg

Eijkemans

MJC

, et al. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 2005; 58: 475–483.

15.

R Core Team. R: A language and environment for statistical computing, 2015. https://www.gbif.org/tool/81287/r-a-language-and-environment-for-statistical-computing.

16.

Steyerberg

Vickers

Cook

, et al. Assessing the performance of prediction models. Epidemiology 2010; 21: 128–138.

17.

Hosmer DW Lemeshow S. Area under the ROC curve. In: Noel AC Cressie, Nicholas I Fisher, Iain M Johnstone, JB Kadane, David W Scott, Bernard W Silverman, Adrian FM Smith, Jozef L Teugels (eds) Applied logistic regression. New Jersey, USA: John Wiley & Sons Inc, 2000; 160–164.

18.

Margolis

Bilker

Boston

, et al. Statistical characteristics of area under the receiver operating characteristic curve for a simple prognostic model using traditional and bootstrapped approaches. J Clin Epidemiol 2002; 55: 518–524.

19.

Attergrim J. SRRs for 546 ICD-10 codes based on 11944 trauma patients in four public university hospitals in Urban India, 2016. https://figshare.com/articles/srrs-2016-04-25-080655_csv/3383020/1.

20.

Sterner M, Attergrim J, Claeson A, et al. Dataset: both the multiplicative and single-worst-injury International Classification of Diseases Injury Severity Score (ICISS) underperform in urban Indian hospitals, 2016. https://figshare.com/articles/Dataset_csv/5425690/1.

21.

Rutledge

Osler

Emery

, et al. The end of the Injury Severity Score (ISS) and the Trauma and Injury Severity Score (TRISS): ICISS, an International Classification of Diseases, ninth revision-based prediction tool, outperforms both ISS and TRISS as predictors of trauma patient survival. J Trauma 1998; 44: 41–49.

22.

Hung

Mehmood

, et al. Exploring injury severity measures and in-hospital mortality: a multi-hospital study in Kenya. Injury 2017; 48: 2112–2118.

23.

Gagné

Moore

Sirois

M-J

, et al. Performance of International Classification of Diseases-based injury severity measures used to predict in-hospital mortality and intensive care admission among traumatic brain-injured patients. J Trauma Acute Care Surg 2017; 82: 374–382.

24.

Laytin

Kumar

Juillard

, et al. Choice of injury scoring system in low- and middle-income countries: lessons from Mumbai. Injury 2015; 46: 2491–2497.

25.

Stephenson

Henley

Harrison

, et al. Diagnosis based injury severity scaling: investigation of a method using Australian and New Zealand hospitalisations. Inj Prev 2004; 10: 379–383.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB

Both the multiplicative and single-worst-injury International Classification of Diseases Injury Severity Score underperform in urban Indian hospitals

Abstract

Introduction

Methods

Results

Conclusions

Keywords

Get full access to this article

References

Supplementary Material