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Automated Extraction of Decision Rules for Predicting Lumbar Drain Outcome by Analyzing Overnight Intracranial Pressure

  • Xiao HuEmail author
  • Robert Hamilton
  • Kevin Baldwin
  • Paul M. Vespa
  • Marvin Bergsneider
Chapter
Part of the Acta Neurochirurgica Supplementum book series (NEUROCHIRURGICA, volume 114)

Abstract

Background: Extended lumbar drain (ELD) has become a popular pre-shunt workup test to help diagnose normal pressure hydrocephalus (NPH). Unfortunately, this procedure requires a substantial time investment for both the family and hospital. In this study, we investigate how accurate the prediction of ELD outcome can be achieved by using simple decision rules automatically derived from pulse morphological metrics of overnight ICP recordings. Our ultimate goal is to test the hypothesis that overnight ICP monitoring, empowered by subsequent signal analysis, could be an alternative to ELD.

Methods: The present study involved 54 patients with both ELD and overnight ICP recordings; the ICP morphological analysis was performed using the MOCAIP algorithm. Furthermore, the distribution of individual metric from the overnight recording was characterized using five aggregation functions (features). Then an algorithm was developed to automatically discover the most accurate “if-then” decision rule for each of the five feature functions. In addition, the best combination of two decision rules, either using “AND” or “OR” operator, was obtained.

Findings: Rules based on five individual feature functions achieved an accuracy of 70.4%, 72.2%, 74.1%, 72.2%, and 79.6% respectively. However, “OR” combination of two features improved accuracy to 88.9%.

Conclusion: We showed an algorithm to discover decision rules that can potentially predict ELD outcome.

Keywords

Intracranial pressure Pulse morphology Hydrocephalus Hierarchical clustering 

Notes

Conflict of interest statement

A PCT application was filed by UCLA for the pulse morphology analysis algorithm used in this work. The present work is partially supported by NINDS grants NS059797, NS054881, and NS066008.

References

  1. 1.
    Governale LS et al (2008) Techniques and complications of external lumbar drainage for normal pressure hydrocephalus. Neurosurgery 63(4 Suppl 2):379–384; discussion 384PubMedCrossRefGoogle Scholar
  2. 2.
    Walchenbach R et al (2002) The value of temporary external lumbar CSF drainage in predicting the outcome of shunting on normal pressure hydrocephalus. J Neurol Neurosurg Psychiatry 72(4):503–506PubMedGoogle Scholar
  3. 3.
    Marmarou A et al (2005) Diagnosis and management of idiopathic normal-pressure hydrocephalus: a prospective study in 151 patients. J Neurosurg 102(6):987–997PubMedCrossRefGoogle Scholar
  4. 4.
    Marmarou A et al (2005) The value of supplemental prognostic tests for the preoperative assessment of idiopathic normal-pressure hydrocephalus. Neurosurgery 57(3 Suppl):S17–S28; discussion ii–vPubMedGoogle Scholar
  5. 5.
    Raftopoulos C et al (1992) Morphological quantitative analysis of intracranial pressure waves in normal pressure hydrocephalus. Neurol Res 14(5):389–396PubMedGoogle Scholar
  6. 6.
    Eide PK, Sorteberg W (2010) Diagnostic intracranial pressure monitoring and surgical management in idiopathic normal pressure hydrocephalus: a 6-year review of 214 patients. Neurosurgery 66(1):80–91PubMedCrossRefGoogle Scholar
  7. 7.
    Stephensen H et al (2005) Objective B wave analysis in 55 patients with non-communicating and communicating hydrocephalus. J Neurol Neurosurg Psychiatry 76(7):965–970PubMedCrossRefGoogle Scholar
  8. 8.
    Woodworth GF et al (2009) Cerebrospinal fluid drainage and dynamics in the diagnosis of normal pressure hydrocephalus. Neurosurgery 64(5):919–925; discussion 925–926PubMedCrossRefGoogle Scholar
  9. 9.
    Eide PK (2006) A new method for processing of continuous intracranial pressure signals. Med Eng Phys 28(6):579–587PubMedCrossRefGoogle Scholar
  10. 10.
    Hu X et al (2009) Morphological clustering and analysis of continuous intracranial pressure. IEEE Trans Biomed Eng 56(3):696–705PubMedCrossRefGoogle Scholar
  11. 11.
    Hu X et al (2010) Intracranial pressure pulse morphological features improved detection of decreased cerebral blood flow. Physiol Meas 31:679–695PubMedCrossRefGoogle Scholar
  12. 12.
    Kasprowicz M et al (2010) Pattern recognition of overnight intracranial pressure slow waves using morphological features of intracranial pressure pulse. J Neurosci Methods 190(2):310–318PubMedCrossRefGoogle Scholar
  13. 13.
    Zong W, Mark R (2003) An open-source algorithm to detect onset of arterial blood pressure pulses. Comput Cardiol 30:259–262Google Scholar
  14. 14.
    Scalzo F et al (2009) Regression analysis for peak designation in pulsatile pressure signals. Med Biol Eng Comput 47(9):967–977PubMedCrossRefGoogle Scholar
  15. 15.
    Scalzo F et al (2008) Random subwindows for robust peak recognition in intracranial pressure signals. Lect Notes Comput Sci 5358:370–380CrossRefGoogle Scholar
  16. 16.
    Eide PK, Brean A (2010) Cerebrospinal fluid pulse pressure amplitude during lumbar infusion in idiopathic normal pressure hydrocephalus can predict response to shunting. Cerebrospinal Fluid Res 7:5PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 2012

Authors and Affiliations

  • Xiao Hu
    • 1
    • 2
    Email author
  • Robert Hamilton
    • 1
    • 2
  • Kevin Baldwin
    • 1
  • Paul M. Vespa
    • 3
  • Marvin Bergsneider
    • 1
    • 2
  1. 1.Neural Systems and Dynamics Laboratory, Department of Neurosurgery, David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA
  2. 2.Biomedical Engineering Graduate Program, Henry Samueli School of Engineering and Applied ScienceUniversity of CaliforniaLos AngelesUSA
  3. 3.Neurocritical Care Program, Department of Neurosurgery, David Geffen School of MedicineUniversity of CaliforniaLos AngelesUSA

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