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Updated: Aug 5, 2026
System for Focal, Closed-System Central Nervous System Injury
Published on: November 29, 2024
Applying a Novel Diagnostic Code System to Identify Postconcussive Symptom Subgroups Among Service Members With Mild
Adam R Kinney1, Treven C Pickett, Amy O Bowles
1Author Affiliations:VA Rocky Mountain Mental Illness Research Education and Clinical Center, Aurora, CO (Kinney, Forster, Adams, and Smith); University of Colorado, Anschutz Medical Campus, Aurora, CO (Kinney, Forster, Baugh, Smith, and Brenner); National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD (Pickett, DeGraba, and Lyle); Brain Injury Rehabilitation Service, Department of Rehabilitation Medicine, Brooke Army Medical Center, JBSA Ft Sam Houston, Texas (Bowles); Department of Health Law, Policy and Management, Boston University School of Public Health, Boston, MA (Adams); VA Brain Health Coordinating Center, VA Rocky Mountain Regional Medical Center, Aurora, CO (Brenner); Program Executive Office, Defense Healthcare Management Systems, Rosslyn, Virginia (Caban, Tung, and Wal); Department of Rehabilitation, Uniform Services University of the Health Sciences, Bethesda, MD (Pickett).
Objective:
First, to describe the development of a novel postconcussive symptom (PCS) code set for identifying symptoms via medical records. Second, to apply it to a population-based cohort of service members with a history of mild traumatic brain injury (mTBI) per Military Health System (MHS) records, to identify distinct subgroups based on patterns of risk for specific PCS.
Setting:
MHS.
Participants:
Population-based sample of service members with mTBI who served in the Army, Air Force, Navy, and Marine Corps and received a diagnosis within the MHS (n = 148 293).
Design:
Retrospective cohort study using medical record data from the MHS spanning 2002 to 2021.
Main Measures:
In collaboration with clinical experts, we iteratively refined a novel PCS code set comprised of ICD-9/10 codes based on Neurobehavioral Symptom Inventory categories, when possible. We used latent class analysis (LCA) with a split-sample cross-validation procedure to identify subgroups of service members with mTBI based on probability of receiving each PCS diagnosis.
Results:
The final PCS code set included 20 symptoms, spanning vestibular, sensory, cognitive, and mood/behavioral-related symptoms. The LCA supported 5 distinct subgroups, the most prevalent being the Minimal subgroup (59%), characterized by low probability of all PCS. The next most common class was the Headache class (16%), followed by the Mood-Behavioral (15%), Headache-Sleep (6%), and Headache-Mood-Sleep (5%) classes.
Conclusions:
Using a novel PCS code set leveraging routinely collected data, we identified 5 clinically meaningful and statistically distinct subgroups based on symptom patterns in a population-based cohort of service members with a history of mTBI. These subgroups provide a nuanced, person-centered understanding of symptoms among those with a history of TBI and can inform targeted interventions and policies aimed at meeting the needs of these service members. Further, findings establish a foundation for investigating risk factors and outcomes across subgroups, informing prognostication.
