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Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging: a machine learning approach
  1. Carl Marincowitz1,
  2. Lewis Paton2,
  3. Fiona Lecky1,
  4. Paul Tiffin3
  1. 1Centre for Urgent and Emergency Care Research (CURE), Health Services Research School of Health and Related Research, The University of Sheffield, Sheffield, UK
  2. 2Department of Health Sciences, University of York Alcuin College, York, York, UK
  3. 3Hull York Medical School Department of Health Sciences, University of York, York, UK
  1. Correspondence to Dr Carl Marincowitz, School of Health and Related Research (ScHARR), The University of Sheffield, Sheffield, UK; c.marincowitz{at}sheffield.ac.uk

Abstract

Background Patients with mild traumatic brain injury on CT scan are routinely admitted for inpatient observation. Only a small proportion of patients require clinical intervention. We recently developed a decision rule using traditional statistical techniques that found neurologically intact patients with isolated simple skull fractures or single bleeds <5 mm with no preinjury antiplatelet or anticoagulant use may be safely discharged from the emergency department. The decision rule achieved a sensitivity of 99.5% (95% CI 98.1% to 99.9%) and specificity of 7.4% (95% CI 6.0% to 9.1%) to clinical deterioration. We aimed to transparently report a machine learning approach to assess if predictive accuracy could be improved.

Methods We used data from the same retrospective cohort of 1699 initial Glasgow Coma Scale (GCS) 13–15 patients with injuries identified by CT who presented to three English Major Trauma Centres between 2010 and 2017 as in our original study. We assessed the ability of machine learning to predict the same composite outcome measure of deterioration (indicating need for hospital admission). Predictive models were built using gradient boosted decision trees which consisted of an ensemble of decision trees to optimise model performance.

Results The final algorithm reported a mean positive predictive value of 29%, mean negative predictive value of 94%, mean area under the curve (C-statistic) of 0.75, mean sensitivity of 99% and mean specificity of 7%. As with logistic regression, GCS, severity and number of brain injuries were found to be important predictors of deterioration.

Conclusion We found no clear advantages over the traditional prediction methods, although the models were, effectively, developed using a smaller data set, due to the need to divide it into training, calibration and validation sets. Future research should focus on developing models that provide clear advantages over existing classical techniques in predicting outcomes in this population.

  • trauma
  • research
  • trauma
  • head
  • imaging
  • CT/MRI

Data availability statement

Clinical data were collected and anonymised by members of the direct care team without consent but with NHS research ethics approval. The NHS research ethics approval limits access to individual-level patient data to members of the research team. We are able to provide summary data on reasonable request.

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Data availability statement

Clinical data were collected and anonymised by members of the direct care team without consent but with NHS research ethics approval. The NHS research ethics approval limits access to individual-level patient data to members of the research team. We are able to provide summary data on reasonable request.

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Footnotes

  • Handling editor Katie Walker

  • Contributors The idea for the study was conceived by CM and PT with help from FL and LP. The analysis was completed by PT and LP with clinical specialist advice regarding the interpretation of results from CM and FL. All authors read and approved the final manuscript.

  • Funding FL is supported by the European Union Framework 7 Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury ((EC grant 602150)) and NHS Trusts 'Trauma Audit and Research Network - www.tarn.ac.uk' (Grant Number Not Applicable/NA). CM is a National Institute for Health Research (NIHR) Clinical Lecturer in Emergency Medicine (Grant Number Not Applicable/NA). PT is funded in his research by an NIHR Career Development Fellowship (CDF-2015-08-11). This publication presents independent research funded by the National Institute for Health Research, University of Sheffield and University of York.

  • Disclaimer The views expressed are those of the author(s) and not necessarily those of the University of Sheffield, University of York, the NHS, the NIHR or the Department of Health and Social Care.

  • Competing interests None declared.

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

  • 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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