The Journal of head trauma rehabilitation · 2026
Applying a Novel Diagnostic Code System to Identify Postconcussive Symptom Subgroups Among Service Members With Mild Traumatic Brain Injury.
СтатьяНазвание и аннотация: Перевод в очереди
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Kinney Adam R, Pickett Treven C, Bowles Amy O, Forster Jeri E, Adams Rachel Sayko, Baugh Christine M, DeGraba Thomas, Caban Jesus, Lyle Amber, Smith Alexandra, Tung Melinda, Wal Isabelle, Brenner Lisa A
Аннотация
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.
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.