Identifying and predicting headache trajectories among those with acute post-traumatic headache.

OBJECTIVES/BACKGROUND: Post-traumatic headache (PTH) is a common symptom following mild traumatic brain injury (mTBI). Currently, there is no identified way to accurately predict if, when, and at what pace a person will have PTH improvement. In our prior studies, we focused on predicting headache improvement at 3 months post-mTBI. However, that approach may overlook individual differences in how headaches evolve over time. This study aimed to identify individual subgroups based on their headache trajectories and to develop machine-learning (ML) models for early prediction of headache evolution.

METHODS

Participants with acute PTH completed a daily electronic headache diary over 3 months, recording their headache-related symptoms. Tensor decomposition was utilized to extract latent factors underlying the time-varying symptoms. We applied clustering techniques on the latent factors to identify patient subgroups with varying headache improvement trajectory. Next, we developed an ML method to classify each individual into a headache trajectory subgroup as early as possible within the 3-month interval.

RESULTS

Seventy-three individuals with acute PTH (mean age = 44.8 years, SD = 14.0; 50 females/23 males) were enrolled between 0 and 59 days post-mTBI. Data from 54 individuals were used as the training cohort for model training, and 19 individuals were used as the test cohort for model evaluation. Tensor decomposition extracted two latent factors: one factor representing the overall state of PTH severity and disability and the other representing the improvement state of these symptoms over 3 months. Clustering identified four patient subgroups with distinct headache evolution trajectories: (1) severe symptoms without improvement, (2) severe symptoms with mild improvement, (3) milder symptoms with substantial improvement, and (4) mildest symptoms with mild improvement. The proposed ML model achieved 0.80 cross-validation accuracy in classifying individuals with PTH into subgroups for the training cohort and 0.84 accuracy for the test cohort. Notably, the model required only the first 2 weeks of headache data to accurately identify the subgroup with the mildest headaches, 3 additional weeks to identify the subgroup with the most severe headaches and no improvement in 3 months, and 2 additional weeks to distinguish the remaining subgroups.

CONCLUSION

This study identified subgroups of individuals with acute PTH with distinct headache improvement trajectories. The proposed ML method accurately classified individuals into these subgroups using the minimally necessary early headache data for each person, including detecting the subgroup with the mildest headaches at 2 weeks. This approach could offer an estimated forecast of headache burden over time and could assist clinicians with determining treatment needs and eligibility for PTH clinical trials.