Article Text

Original research
Development and temporal external validation of a simple risk score tool for prediction of outcomes after severe head injury based on admission characteristics from level-1 trauma centre of India using retrospectively collected data
  1. Vineet Kumar Kamal1,
  2. Ravindra Mohan Pandey2,
  3. Deepak Agrawal3
  1. 1Division of Epidemiology & Biostatistics, National Institute of Epidemiology, Indian Council of Medial Research (ICMR), Chennai, Tamil Nadu, India
  2. 2Department of Biostatistics, All India Institute of Medical Sciences (AIIMS), New Delhi, India
  3. 3Department of Neurosurgery, Jai Prakash Naryan Apex Trauma Centre, All India Institute of Medical Sciences (AIIMS), New Delhi, India
  1. Correspondence to Dr Vineet Kumar Kamal; vineetstats{at}gmail.com

Abstract

Objective To develop and validate a simple risk scores chart to estimate the probability of poor outcomes in patients with severe head injury (HI).

Design Retrospective.

Setting Level-1, government-funded trauma centre, India.

Participants Patients with severe HI admitted to the neurosurgery intensive care unit during 19 May 2010–31 December 2011 (n=946) for the model development and further, data from same centre with same inclusion criteria from 1 January 2012 to 31 July 2012 (n=284) for the external validation of the model.

Outcome(s) In-hospital mortality and unfavourable outcome at 6 months.

Results A total of 39.5% and 70.7% had in-hospital mortality and unfavourable outcome, respectively, in the development data set. The multivariable logistic regression analysis of routinely collected admission characteristics revealed that for in-hospital mortality, age (51–60, >60 years), motor score (1, 2, 4), pupillary reactivity (none), presence of hypotension, basal cistern effaced, traumatic subarachnoid haemorrhage/intraventricular haematoma and for unfavourable outcome, age (41–50, 51–60, >60 years), motor score (1–4), pupillary reactivity (none, one), unequal limb movement, presence of hypotension were the independent predictors as its 95% confidence interval (CI) of odds ratio (OR)_did not contain one. The discriminative ability (area under the receiver operating characteristic curve (95% CI)) of the score chart for in-hospital mortality and 6 months outcome was excellent in the development data set (0.890 (0.867 to 912) and 0.894 (0.869 to 0.918), respectively), internal validation data set using bootstrap resampling method (0.889 (0.867 to 909) and 0.893 (0.867 to 0.915), respectively) and external validation data set (0.871 (0.825 to 916) and 0.887 (0.842 to 0.932), respectively). Calibration showed good agreement between observed outcome rates and predicted risks in development and external validation data set (p>0.05).

Conclusion For clinical decision making, we can use of these score charts in predicting outcomes in new patients with severe HI in India and similar settings.

  • trauma management
  • statistics & research methods
  • public health
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Footnotes

  • Twitter @Vineet Kr Kamal @bunni_vinni

  • Contributors VKK was involved in literature search, conception and design of the study, data collection, analysis and interpretation of data, writing, critical revision and final manuscript preparation. RMP was involved in conception, design of the study, supervision and mentoring, interpretation of data, critical revision. DA was involved in conception, design of the study, supervision, interpretation of data, critical revision.

  • Funding This research work was done from the data collected for Ph.D. work, which objectives were different, of first author (VKK) at Department of Biostatistics, All India Institute of Medical Sciences, New Delhi. There was no any specific funding for this particular work, but he received funding as a senior research fellow (SRF) from Indian Council of Medical Research (ICMR), New Delhi, India for his Ph.D. research.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as online supplemental information. All data requests should be submitted to the corresponding author for consideration. Access to anonymised data may be granted on reasonable request after the permission of coauthor, DA. Exclusive use will be retained until the publication of major outputs.

  • Author note This work won the best poster award at the 11th International Conference on Urban Health (ICUH), Manchester, the United Kingdom hosted by the University of Manchester and was attended by more than 1,000 participants from different parts of the world.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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