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Symptom profiles following combat injury and long-term quality of life: a latent class analysis
Andrew J MacGregor1, Amber L Dougherty2,3, Edwin W D'Souza2,3
1Medical Modeling, Simulation, and Mission Support Department, Naval Health Research Center, 140 Sylvester Road, San Diego, CA, 92106, USA. andrewmacgregor@protonmail.com.
Purpose:
The purpose of this study was to identify symptom profiles among U.S. military personnel within 1 year after combat injury and assess the relationship between the symptom profiles and long-term quality of life (QoL).
Methods:
The study sample consisted of 885 military personnel from the Expeditionary Medical Encounter Database who completed (1) a Post-Deployment Health Assessment (PDHA) within 1 year following combat injury in Iraq or Afghanistan, and (2) a survey for the Wounded Warrior Recovery Project (WWRP), a longitudinal study tracking patient-reported outcomes (e.g., QoL) in injured military personnel. Fifteen self-reported symptoms from the PDHA were assessed using latent class analysis to develop symptom profiles. Multivariable linear regression assessed the predictive effect of symptom profiles on QoL using the physical (PCS) and mental (MCS) component summary scores from the 36-Item Short Form Survey included in the WWRP. Time between PDHA and WWRP survey ranged from 4.3 to 10.5 years (M = 6.6, SD = 1.3).
Results:
Five distinct symptom profiles were identified: low morbidity (50.4%), multimorbidity (15.6%), musculoskeletal (14.0%), psycho-cognitive (11.1%), and auditory (8.9%). Relative to low morbidity, the multimorbidity (β = - 5.45, p < 0.001) and musculoskeletal (β = - 4.23, p < 0.001) profiles were associated with lower PCS, while the multimorbidity (β = - 4.25, p = 0.002) and psycho-cognitive (β = - 3.02, p = 0.042) profiles were associated with lower MCS.
Conclusion:
Multimorbidity, musculoskeletal, and psycho-cognitive symptom profiles were the strongest predictors of lower QoL. These profiles can be employed during screening to identify at-risk service members and assist with long-term clinical planning, while factoring in patient-specific impairments and preferences.
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