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Algorithm for Symptom Attribution and Classification Following Possible Mild Traumatic Brain Injury.

Theresa Louise-Bender Pape1, Amy A Herrold, Bridget Smith

  • 1The Department of Veterans Affairs (VA), Edward Hines, Jr. VA Hospital, Research Service, Hines, Illinois (Drs Pape and Herrold); The Department of Veterans Affairs (VA), Edward Hines, Jr. VA Hospital, Center for Innovation for Complex Chronic Healthcare, Hines, Illinois (Drs Pape, Herrold, Smith, and Evans); Departments of Psychiatry & Behavioral Sciences (Dr Herrold) and Physical Medicine and Rehabilitation (Dr Pape), Northwestern University, Feinberg School of Medicine, Chicago, Illinois; The Department of Veterans Affairs (VA), Lexington VAMC C-306, Lexington, Kentucky (Ms Jenkins and Drs Schleenbaker and High); Departments of Physical Medicine and Rehabilitation, Neurosurgery, and Psychology, University of Kentucky, Lexington (Ms Jenkins and Drs Schleenbaker and High); The Department of Veterans Affairs (VA), Spinal Cord Injury QUERI, Edward Hines, Jr. VA Hospital, Hines, Illinois (Drs Smith and Evans); Northwestern University, Feinberg School of Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, and Department of Pediatrics, Child Health Research Program, Stanley Manne Children's Research Institute, Chicago, Illinois (Dr Smith); Department of Psychology, University of Kentucky, Lexington (Mr Harp and Dr Shandera-Ochsner); Northwestern University, Feinberg School of Medicine, Center for Healthcare Studies, Institute for Public Health and Medicine, Chicago, Illinois (Dr Evans); and The Department of Veterans Affairs (VA), Southern AZ VA Health Care System (3-124), Tucson, Arizona (Dr Babcock-Parziale).

The Journal of Head Trauma Rehabilitation
|February 2, 2016
PubMed
Summary

A new algorithm for mild traumatic brain injury (mTBI) symptom classification, the Symptom Attribution and Classification Algorithm (SACA), shows different diagnoses compared to current methods. SACA may offer a more precise way to identify valid profiles in Veterans with mTBI and behavioral health conditions.

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Area of Science:

  • Neuroscience and Traumatic Brain Injury Research
  • Clinical Psychology and Behavioral Health

Background:

  • Mild traumatic brain injury (mTBI) diagnosis and symptom attribution are complex.
  • Existing diagnostic methods may lead to varied interpretations of symptom causality.
  • Veterans often present with complex polytrauma, including mTBI and behavioral health (BH) conditions.

Purpose of the Study:

  • To introduce a heuristic model for a Symptom Attribution and Classification Algorithm (SACA) for mTBI.
  • To compare SACA-derived diagnoses with those from the Comprehensive TBI Evaluation (CTBIE).
  • To evaluate the precision of different measures in identifying questionably valid psychological profiles.

Main Methods:

  • A cross-sectional study involving 422 Veterans at VA Polytrauma sites.
  • Utilized SACA, CTBIE, Structured TBI Diagnostic Interview, MMPI-2-RF, Letter Memory Test, and Validity-10.
  • Compared symptom attribution and classification between SACA and CTBIE under various criteria.

Main Results:

  • SACA and CTBIE diagnoses showed significant differences (P < .01).
  • CTBIE attributed 16% to 500% more symptoms to mTBI, BH, or mTBI + BH compared to SACA.
  • The MMPI-2-RF F-scale was more precise in identifying questionably valid profiles for mTBI + BH than Validity-10 and Letter Memory Test.

Conclusions:

  • Symptom attribution-based diagnoses vary significantly between SACA and current practices.
  • SACA offers a structured framework for clinical practice, resource allocation, and future mTBI research.
  • The MMPI-2-RF F-scale shows potential for improved identification of valid psychological profiles in complex mTBI cases.