Skip to main content
SpringerLink
Log in

Advanced Informatics Methods in Acute Brain Injury Research

  • Protocol
  • First Online:
Pre-Clinical and Clinical Methods in Brain Trauma Research

Part of the book series: Neuromethods ((NM,volume 139))

  • 646 Accesses

Abstract

The failure of several clinical trials in traumatic brain injury (TBI) targeting thresholds of physiologic variables challenges the clinical utility of existing approaches and highlights the need for new patient management strategies in acute brain trauma. Medical informatics is a multidisciplinary field that focuses on solving clinical problems using techniques from various quantitative disciplines including engineering, statistics, and computer science. They are proven to be useful in solving problems in the omics domains, but less popular in TBI research. In this chapter, some of the applications of medical informatics in TBI research are discussed. First, we discuss the need for patient-specific threshold of physiologic metrics rather than population based metrics. The role of cerebral autoregulation (CAR) and recent techniques to measure a patient’s CAR status and the utility of CAR in clinical practice are discussed. Second, we focus on two important subfields of informatics – supervised and unsupervised machine learning – and their applications in ABI research. Machine learning (ML) is a powerful tool that is widely used in the discovery of patterns in large datasets and in the development of predictive models. We discuss some of the applications of ML in TBI research; ranging from the prediction of ICP hypertension to discovery of patterns. Also, recent advancements in our ability to store large quantities of data and the availability of cheap processing power has spawned the era of ‘big data’. ‘Big data’ made huge impacts on different fields including genomics and proteomics. We discuss recent advancements in this field and the need for large databases for TBI research. As the condition of each patient is unique, there is a need for a transition to a ‘personalized-medicine’ approach, where the management protocol for each patient is unique and is based on the patient’s status, rather than a guideline-based approach. Techniques from informatics applied to TBI research can aid this transition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cooper DJ, Rosenfeld JV, Murray L et al (2011) Decompressive craniectomy in diffuse traumatic brain injury. N Engl J Med 364:1493–1502. https://doi.org/10.1056/NEJMoa1102077

    Article  CAS  PubMed  Google Scholar 

  2. Czosnyka M, Smielewski P, Timofeev I et al (2007) Intracranial pressure: more than a number. Neurosurg Focus 22:E10

    PubMed  Google Scholar 

  3. Wagshul ME, Eide PK, Madsen JR (2011) The pulsating brain: a review of experimental and clinical studies of intracranial pulsatility. Fluids Barriers CNS 8:5. https://doi.org/10.1186/2045-8118-8-5

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lazaridis C, Smielewski P, Menon DK et al (2016) Patient-specific thresholds and doses of intracranial hypertension in severe traumatic brain injury. Acta Neurochir Suppl 122:117–120. https://doi.org/10.1007/978-3-319-22533-3_23

    Article  PubMed  Google Scholar 

  5. Hu X, Xu P, Scalzo F et al (2009) Morphological clustering and analysis of continuous intracranial pressure. IEEE Trans Biomed Eng 56:696–705

    Article  PubMed  Google Scholar 

  6. Kim D-J, Kim H, Jeong E-J et al (2016) Spectral analysis of intracranial pressure: is it helpful in the assessment of shunt functioning in-vivo? Clin Neurol Neurosurg 142:112–119. https://doi.org/10.1016/j.clineuro.2016.01.023

    Article  PubMed  Google Scholar 

  7. Kvandal P, Sheppard L, Landsverk SA et al (2013) Impaired cerebrovascular reactivity after acute traumatic brain injury can be detected by wavelet phase coherence analysis of the intracranial and arterial blood pressure signals. J Clin Monit Comput 27:375–383. https://doi.org/10.1007/s10877-013-9484-z

    Article  PubMed  PubMed Central  Google Scholar 

  8. Czosnyka M, Smielewski P, Piechnik S et al (2001) Cerebral autoregulation following head injury. J Neurosurg 95:756–763. https://doi.org/10.3171/jns.2001.95.5.0756

    Article  PubMed  CAS  Google Scholar 

  9. Aiolfi A, Benjamin E, Khor D et al (2017) Brain trauma foundation guidelines for intracranial pressure monitoring: compliance and effect on outcome. World J Surg 41:1543–1549

    Article  PubMed  Google Scholar 

  10. Mehta A, Kochanek PM, Tyler-Kabara E et al (2010) Relationship of intracranial pressure and cerebral perfusion pressure with outcome in young children after severe traumatic brain injury. Dev Neurosci 32:413–419. https://doi.org/10.1159/000316804

    Article  PubMed  CAS  Google Scholar 

  11. Lazaridis C, Smielewski P, Steiner LA et al (2013) Optimal cerebral perfusion pressure: are we ready for it? Neurol Res 35:138–148. https://doi.org/10.1179/1743132812Y.0000000150

    Article  PubMed  Google Scholar 

  12. Prabhakar H, Sandhu K, Bhagat H et al (2014) Current concepts of optimal cerebral perfusion pressure in traumatic brain injury. J Anaesthesiol Clin Pharmacol 30:318–327. https://doi.org/10.4103/0970-9185.137260

    Article  PubMed  PubMed Central  Google Scholar 

  13. Czosnyka M, Miller C, Participants in the International Multidisciplinary Consensus Conference on Multimodality Monitoring (2014) Monitoring of cerebral autoregulation. Neurocrit Care 21(Suppl 2):S95–S102. https://doi.org/10.1007/s12028-014-0046-0

    Article  PubMed  Google Scholar 

  14. Brady KM, Lee JK, Kibler KK et al (2007) Continuous time-domain analysis of cerebrovascular autoregulation using near-infrared spectroscopy. Stroke 38:2818–2825. https://doi.org/10.1161/STROKEAHA.107.485706

    Article  PubMed  PubMed Central  Google Scholar 

  15. Depreitere B, Güiza F, Van den Berghe G et al (2014) Pressure autoregulation monitoring and cerebral perfusion pressure target recommendation in patients with severe traumatic brain injury based on minute-by-minute monitoring data. J Neurosurg 120:1451–1457. https://doi.org/10.3171/2014.3.JNS131500

    Article  PubMed  Google Scholar 

  16. Czosnyka M, Dias C (2015) Role of pressure reactivity index in neurocritical care. In: Uchino H, Ushijima K, Ikeda Y (eds) Neuroanesthesia and cerebrospinal protection. Springer, Tokyo, pp 223–236. https://doi.org/10.1007/978-4-431-54490-6_21

    Chapter  Google Scholar 

  17. Czosnyka M, Smielewski P, Kirkpatrick P et al (1997) Continuous assessment of the cerebral vasomotor reactivity in head injury. Neurosurgery 41:11–17; discussion 17-19

    Article  PubMed  CAS  Google Scholar 

  18. Dias C, Silva MJ, Pereira E et al (2015) Optimal cerebral perfusion pressure management at bedside: a Single-center Pilot Study. Neurocrit Care 23:92–102. https://doi.org/10.1007/s12028-014-0103-8

    Article  PubMed  Google Scholar 

  19. Hu X, Xu P, Asgari S et al (2010) Forecasting ICP elevation based on prescient changes of intracranial pressure waveform morphology. IEEE Trans Biomed Eng 57:1070–1078. https://doi.org/10.1109/TBME.2009.2037607

    Article  PubMed  PubMed Central  Google Scholar 

  20. Lee JK, Poretti A, Perin J et al (2017) Optimizing cerebral autoregulation may decrease neonatal regional hypoxic-ischemic brain injury. Dev Neurosci 39:248–256

    Article  PubMed  CAS  Google Scholar 

  21. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:Article17. https://doi.org/10.2202/1544-6115.1128

    Article  PubMed  Google Scholar 

  22. Deo RC (2015) Machine learning in medicine. Circulation 132:1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hijazi S, Page A, Kantarci B, Soyata T (2016) Machine learning in cardiac health monitoring and decision support. Computer 49:38–48. https://doi.org/10.1109/MC.2016.339

    Article  Google Scholar 

  24. Boursalie O, Samavi R, Doyle TE (2015) M4CVD: mobile machine learning model for monitoring cardiovascular disease. Proc Comput Sci 63:384–391. https://doi.org/10.1016/j.procs.2015.08.357

    Article  Google Scholar 

  25. Pinsky MR, Clermont G, Hravnak M (2016) Predicting cardiorespiratory instability. Crit Care 20:70. https://doi.org/10.1186/s13054-016-1223-7

    Article  PubMed  PubMed Central  Google Scholar 

  26. Thottakkara P, Ozrazgat-Baslanti T, Hupf BB et al (2016) Application of machine learning techniques to high-dimensional clinical data to forecast postoperative complications. PLoS One 11:e0155705. https://doi.org/10.1371/journal.pone.0155705

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Taylor RA, Pare JR, Venkatesh AK et al (2016) Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med Off J Soc Acad Emerg Med 23:269–278. https://doi.org/10.1111/acem.12876

    Article  Google Scholar 

  28. Scott H, Colborn K (2016) Machine learning for predicting sepsis in-hospital mortality: an important start. Acad Emerg Med Off J Soc Acad Emerg Med 23:1307. https://doi.org/10.1111/acem.13009

    Article  Google Scholar 

  29. Giannini HM, Chivers C, Draugelis M et al (2017) Development and implementation of a machine-learning algorithm for early identification of sepsis in a multi-hospital academic healthcare system. Am J Resp Crit Care Med 195:A7015. D15 Crit. Care we have Cryst. Ball Predict. Clin. Deterioration outcome Crit. Ill patients. Am Thoracic Soc

    Google Scholar 

  30. Horng S, Sontag DA, Halpern Y et al (2017) Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS One 12:e0174708. https://doi.org/10.1371/journal.pone.0174708

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Danner OK, Hendren S, Santiago E et al (2017) Physiologically-based, predictive analytics using the heart-rate-to-systolic-ratio significantly improves the timeliness and accuracy of sepsis prediction compared to SIRS. Am J Surg 213:617–621. https://doi.org/10.1016/j.amjsurg.2017.01.006

    Article  PubMed  Google Scholar 

  32. Shashikumar SP, Stanley MD, Sadiq I et al (2017) Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics. J Electrocardiol 50:739–749. https://doi.org/10.1016/j.jelectrocard.2017.08.013

    Article  PubMed  PubMed Central  Google Scholar 

  33. Taneja I, Reddy B, Damhorst G et al (2017) Combining biomarkers with EMR data to identify patients in different phases of sepsis. Sci Rep 7:10800. https://doi.org/10.1038/s41598-017-09766-1

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Schmidt JM (2016) Heart rate variability for the early detection of delayed cerebral ischemia. J Clin Neurophysiol Off Publ Am Electroencephalogr Soc 33:268–274. https://doi.org/10.1097/WNP.0000000000000286

    Article  Google Scholar 

  35. Kumar G, Elzaafrani K, Nakhmani A (2017) Machine learning approach to automate detection of cerebral vasospasm using transcranial Doppler monitoring (S23.004). Neurology 88:S23.004

    Article  Google Scholar 

  36. Roederer A, Holmes JH, Smith MJ et al (2014) Prediction of significant vasospasm in aneurysmal subarachnoid hemorrhage using automated data. Neurocrit Care 21:444–450. https://doi.org/10.1007/s12028-014-9976-9

    Article  PubMed  Google Scholar 

  37. Molaei S, Korley FK, Soroushmehr SMR, et al (2016) A machine learning based approach for identifying traumatic brain injury patients for whom a head CT scan can be avoided. In: 2016 38th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC. pp 2258–2261

    Google Scholar 

  38. Chong S-L, Liu N, Barbier S, Ong MEH (2015) Predictive modeling in pediatric traumatic brain injury using machine learning. BMC Med Res Methodol 15:22. https://doi.org/10.1186/s12874-015-0015-0

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mitra J, Shen K, Ghose S et al (2016) Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. NeuroImage 129:247–259. https://doi.org/10.1016/j.neuroimage.2016.01.056

    Article  PubMed  Google Scholar 

  40. Vergara VM, Mayer AR, Damaraju E et al (2016) Detection of mild traumatic brain injury by machine learning classification using resting state functional network connectivity and fractional anisotropy. J Neurotrauma 34:1045–1053. https://doi.org/10.1089/neu.2016.4526

    Article  PubMed  Google Scholar 

  41. Celtikci E (2017) A systematic review on machine learning in neurosurgery: the future of decision making in patient care. Turk Neurosurg 28:167–173. https://doi.org/10.5137/1019-5149.JTN.20059-17.1

    Article  Google Scholar 

  42. Güiza F, Depreitere B, Piper I et al (2013) Novel methods to predict increased intracranial pressure during intensive care and long-term neurologic outcome after traumatic brain injury: development and validation in a multicenter dataset. Crit Care Med 41:554–564. https://doi.org/10.1097/CCM.0b013e3182742d0a

    Article  PubMed  Google Scholar 

  43. Myers RB, Lazaridis C, Jermaine CM et al (2016) Predicting intracranial pressure and brain tissue oxygen crises in patients with severe traumatic brain injury. Crit Care Med 44:1754–1761. https://doi.org/10.1097/CCM.0000000000001838

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  44. Menachemi N, Collum TH (2011) Benefits and drawbacks of electronic health record systems. Risk Manag Healthc Policy 4:47–55. https://doi.org/10.2147/RMHP.S12985

    Article  PubMed  PubMed Central  Google Scholar 

  45. Fartoumi S, Emeriaud G, Roumeliotis N et al (2016) Computerized decision support system for traumatic brain injury management. J Pediatr Intensive Care 5:101–107

    Article  PubMed  Google Scholar 

  46. Singh MP, Hoque MA, Tarkoma S (2016) A survey of systems for massive stream analytics. ArXiv Prepr. ArXiv160509021

    Google Scholar 

  47. Herr TM, Bielinski SJ, Bottinger E et al (2015) A conceptual model for translating omic data into clinical action. J Pathol Inform 6:46. https://doi.org/10.4103/2153-3539.163985

    Article  PubMed  PubMed Central  Google Scholar 

  48. Shukla SK, Murali NS, Brilliant MH (2015) Personalized medicine going precise: from genomics to microbiomics. Trends Mol Med 21:461–462. https://doi.org/10.1016/j.molmed.2015.06.002

    Article  PubMed  PubMed Central  Google Scholar 

  49. Bowman S (2013) Impact of electronic health record systems on information integrity: quality and safety implications. Perspect Health Inf Manag 10:1c

    PubMed  PubMed Central  Google Scholar 

  50. Maas AIR, Harrison-Felix CL, Menon D et al (2011) Standardizing data collection in traumatic brain injury. J Neurotrauma 28:177–187. https://doi.org/10.1089/neu.2010.1617

    Article  PubMed  PubMed Central  Google Scholar 

  51. Sivaganesan A, Manley GT, Huang MC (2014) Informatics for Neurocritical care: challenges and opportunities. Neurocrit Care 20:132–141. https://doi.org/10.1007/s12028-013-9872-8

    Article  PubMed  Google Scholar 

  52. Smielewski P, Czosnyka Z, Kasprowicz M et al (2012) ICM+: a versatile software for assessment of CSF dynamics. Acta Neurochir Suppl 114:75–79. Intracranial Press. Brain Monit. XIV. Springer

    Article  PubMed  Google Scholar 

  53. Mertz L (2014) Saving lives and money with smarter hospitals: streaming analytics, other new tech help to balance costs and benefits. IEEE Pulse 5:33–36

    Article  PubMed  Google Scholar 

  54. Makarenko S, Griesdale DE, Gooderham P, Sekhon MS (2016) Multimodal neuromonitoring for traumatic brain injury: a shift towards individualized therapy. J Clin Neurosci 26:8–13

    Article  PubMed  Google Scholar 

  55. Carlson AP, William Shuttleworth C, Mead B et al (2017) Cortical spreading depression occurs during elective neurosurgical procedures. J Neurosurg 126:266–273. https://doi.org/10.3171/2015.11.JNS151871

    Article  PubMed  Google Scholar 

  56. Le Roux P (2016) Intracranial pressure monitoring and management. In: Laskowitz D, Grant G (eds) Translational research in traumatic brain injury. CRC, Boca Raton

    Google Scholar 

  57. (2016) IBM research streaming analytics solution saves time and lives—IBM. http://researcher.watson.ibm.com/researcher/view_group.php?id=1775. Accessed 25 Sep 2017

  58. Weiner MW, Veitch DP, Aisen PS et al (2017) The Alzheimer’s Disease Neuroimaging Initiative 3: continued innovation for clinical trial improvement. Alzheimers Dement 13:561–571. https://doi.org/10.1016/j.jalz.2016.10.006

    Article  PubMed  Google Scholar 

  59. Weiner MW, Veitch DP, Aisen PS et al (2017) Recent publications from the Alzheimer’s Disease Neuroimaging Initiative: reviewing progress toward improved AD clinical trials. Alzheimers Dement 13:e1–e85. https://doi.org/10.1016/j.jalz.2016.11.007

    Article  PubMed  PubMed Central  Google Scholar 

  60. Yue JK, Vassar MJ, Lingsma HF et al (2013) Transforming research and clinical knowledge in traumatic brain injury pilot: multicenter implementation of the common data elements for traumatic brain injury. J Neurotrauma 30:1831–1844. https://doi.org/10.1089/neu.2013.2970

    Article  PubMed  PubMed Central  Google Scholar 

  61. Maas AIR, Menon DK, Steyerberg EW et al (2015) Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI): a prospective longitudinal observational study. Neurosurgery 76:67–80. https://doi.org/10.1227/NEU.0000000000000575

    Article  PubMed  Google Scholar 

  62. Marmarou A, Lu J, Butcher I et al (2007) IMPACT database of traumatic brain injury: design and description. J Neurotrauma 24:239–250

    Article  PubMed  Google Scholar 

  63. Ivory M (2015) Federal interagency traumatic brain injury research (FITBIR) bioinformatics platform for the advancement of collaborative traumatic brain injury research and analysis

    Google Scholar 

  64. Goldberger AL, Amaral LAN, Glass L et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101:e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215

    Article  PubMed  CAS  Google Scholar 

  65. Kim N, Krasner A, Kosinski C et al (2016) Trending autoregulatory indices during treatment for traumatic brain injury. J Clin Monit Comput 30:821–831. https://doi.org/10.1007/s10877-015-9779-3

    Article  PubMed  Google Scholar 

  66. Piper I, Citerio G, Chambers I et al (2003) The BrainIT group: concept and core dataset definition. Acta Neurochir 145:615–629

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Neurosurgery, McGovern School of Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA

    Jude P. J. Savarraj, Mary F. McGuire, Ryan Kitagawa & Huimahn Alex Choi

Authors
  1. Jude P. J. Savarraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mary F. McGuire
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ryan Kitagawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huimahn Alex Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huimahn Alex Choi .

Editor information

Editors and Affiliations

  1. Department of Pediatric Surgery, McGovern Medical School, The University of Texas Health Sciences Center at Houston, Houston, Texas, USA

    Amit K. Srivastava

  2. Department of Pediatric Surgery, McGovern Medical School, The University of Texas Health Sciences Center at Houston, Houston, Texas, USA

    Charles S. Cox

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Savarraj, J.P.J., McGuire, M.F., Kitagawa, R., Choi, H.A. (2018). Advanced Informatics Methods in Acute Brain Injury Research. In: Srivastava, A., Cox, C. (eds) Pre-Clinical and Clinical Methods in Brain Trauma Research. Neuromethods, vol 139. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8564-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8564-7_14

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8563-0

  • Online ISBN: 978-1-4939-8564-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation