A Common Data Element-Based Adjudication Process for mTBI Clinical Profiles: A Targeted Multidomain Clinical Trial...

Researchers have long sought reliable tools to classify mild traumatic brain injury (mTBI) profiles. While clinical interviews and exams are standard, less is known about how well standardized assessments can align with clinician judgments. Prior work has shown that common data elements (CDEs) can help standardize data collection in mTBI research. However, it remains unclear how often CDEs align with clinical profiles identified by trained professionals. This gap motivated a study to assess the agreement between clinician- and CDE-based classifications. The study aimed to determine if CDE cutoff scores could reliably predict specific mTBI profiles. Existing methods rely on subjective clinical impressions, but objective data could improve diagnostic consistency. No prior work had resolved how well CDEs capture the nuances of clinical profiles. This uncertainty drove the need to evaluate the predictive value of CDEs in mTBI classification.

The study aimed to assess how often clinician-identified mTBI profiles align with those derived from CDE cutoff scores. Researchers wanted to determine if CDEs could reliably predict specific clinical profiles in mTBI patients. The goal was to evaluate the agreement between clinical and standardized assessments. The study focused on migraine/headache, vestibular, and anxiety/mood profiles. Participants were assessed within months of injury to capture early symptoms. The team used logistic regression to identify which CDEs best predicted clinical profiles. The study sought to clarify the usefulness of CDEs in identifying mTBI profiles. This approach could help standardize diagnostic tools in clinical practice.

Seventy-one participants with recent mTBI were evaluated using clinical interviews and multidomain assessments. A licensed clinician with concussion training identified clinical profiles. CDE assessments were administered by a researcher to measure symptom severity. CDE scores exceeding cutoffs were used to classify participants into profiles. A multidisciplinary team reviewed both clinician and CDE classifications. Logistic regression models were used to calculate odds ratios for profile prediction. The study focused on six CDE assessments covering different domains. The team analyzed agreement rates and predictive strength of each CDE.

Migraine/headache, vestibular, and anxiety/mood profiles showed the highest agreement between clinician and CDE assessments. Participants with high Global Severity Index scores were 3.9 times more likely to have anxiety/mood profiles. Those with high Headache Impact Test-6 scores were 8.81 times more likely to have migraine profiles. Vestibular/Ocular Motor Screening items predicted vestibular profiles with moderate accuracy. Pittsburgh Sleep Quality Index scores predicted sleep profiles with some reliability. CDEs from migraine, vestibular, and anxiety domains showed clinical utility. Cognitive and ocular CDEs had limited predictive value for mTBI profiles. The results suggest that certain CDEs may support clinical classification of mTBI profiles.

The study found that CDEs from migraine, vestibular, and anxiety domains may help identify specific mTBI profiles. CDE cutoff scores showed varying predictive strength for different profiles. The highest agreement was observed for migraine, vestibular, and anxiety/mood classifications. Sleep-related CDEs had moderate predictive value for sleep profiles. Cognitive and ocular CDEs had limited utility in identifying mTBI profiles. The findings suggest that selected CDEs may support clinical decision-making. However, the study does not establish that CDEs are essential for diagnosis. The results propose that CDEs could be useful tools in mTBI classification.