Dumkrieger, Gina

Abstract 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.

Background Headache is a common symptom following mild traumatic brain injury (mTBI). Post-traumatic headache (PTH), a secondary headache disorder that develops after mTBI, often persists for months or years. To identify potential recovery mechanisms and prognostic biomarkers, the present study investigated whether longitudinal changes in pain-induced brain activation differ between healthy controls (HC) and PTH participants showing headache improvement and those without improvement. Methods Thirty-three participants who met International Classification of Headache Disorders, 3rd edition, criteria for acute PTH within 59 days post-mTBI and 33 HC participants were included with no significant differences in demographics. All participants underwent functional magnetic resonance imaging scans at baseline, four weeks, and 16 weeks post-enrollment using a thermal stimulation paradigm with noxious and non-painful heat stimuli. ‘Painful vs. Non-Painful Heat’ contrasts were generated using SPM12. PTH improvement was assessed at three months post-enrollment via electronic headache diaries. Two-sample t -tests compared the brain activation between HC and PTH at baseline. Linear mixed-effects models examined longitudinal changes for HC, PTH improvement and non-improvement groups across visits. Generalized linear models compared these groups within visits. Results Baseline analysis revealed several regions with significantly higher activation in acute PTH compared to HC, including bilateral postcentral gyrus, right superior temporal gyrus, right middle temporal gyrus, left inferior parietal gyrus, right superior parietal gyrus, left ventral striatum, left olfactory cortex, left gyrus rectus, and left middle occipital gyrus. Over time, the PTH improvement group demonstrated progressive normalization across all identified brain regions, whereas the non-improvement group showed only partial normalization in left ventral striatum, left olfactory cortex, and left gyrus rectus. Sustained elevated activation in specific regions distinguished PTH participants without headache improvement from those with headache improvement, suggesting potential biomarkers for persistent PTH. Conclusions Our findings demonstrate significantly altered pain-induced brain activations in participants with acute PTH compared to HC. Longitudinal analysis revealed distinct recovery trajectories: progressive normalization in the improvement group versus persistent alterations in the non-improvement group. These neuroimaging patterns may serve as biomarkers for identifying individuals at risk for persistent PTH, with implications for early intervention and personalized treatment approaches.

Background: Prior studies indicate a relationship between migraine and MACE. Here we assess whether brain white matter hyperintensities (WMH), which are a common imaging finding in patient with migraine, contribute to the risk of MACE above demographics and common risk factors for MACE. Methods: 60,454 patients, ages 18-89, with a ICD-9 or ICD-10 migraine diagnosis code in 2010 or later were identified from the Mayo Clinic electronic health record. Only patients who were seen for migraine in the Neurology Department were included. Patients with a migraine diagnosis who did not have MACE were included only if they had at least two visits at Mayo Clinic during five years. Only patients with sex and race information were included. The final cohort included 577 migraine patients with and 598 migraine patients without MACE. Presence of WMH was determined from radiology notes. Individuals without a brain MRI were assumed not to have WMH. A logistic regression model that included sex, race, known lifetime MACE risk factors (atrial fibrillation, diabetes, hypertension, hyperlipidemia, tobacco use) and WMH as independent variables to predict MACE outcome was fit. Results: Significant factors that increased the risk of MACE in individuals with migraine included being Black or African American (adjusted OR: 2.9, 95% CI: 1.24-6.82, p = 0.014), presence of atrial fibrillation (adj. OR: 1.63, 95% CI: 1.23-2.17, p < 0.001), diabetes (adj. OR: 1.34, 95% CI: 1.02-1.75, p = 0.036), hypertension (adj. OR: 1.9, 95% CI: 1.39-2.6, p < 0.001), tobacco use (adj. OR: 1.66, 95% CI: 1.29-2.14, p < 0.001), and the presence of WMH (adj. OR: 1.43, 95% CI: 1.1-1.87, p = 0.008). Hyperlipidemia showed a marginal association (adj. OR: 1.34, 95% CI: 0.99-1.81, p = 0.061), while other variables such as sex and other racial/ethnic groups did not significantly alter the risk of MACE outcome. Discussion: Results indicate that African American race and presence of WMH in addition to common comorbidities independently increase the risk of MACE outcome.

Introduction: Prior data suggest that migraine increases the risk of stroke. However, there is a lack of understanding whether specific migraine features like white matter hyperintensities (WMH) commonly found on brain magnetic resonance (MR) imaging in migraine patients and migraine medications might contribute to Major Adverse Cardiovascular Event (MACE) outcomes including ischemic and hemorrhagic stroke. We assess MACE risk estimation model stratified by event types using Mayo Clinic Electronic Health Record (EHR) data. Methods: Case and control sets, based on MACE occurrence among migraine patients at Mayo Clinic Neurology Department, were propensity matched on age, sex, and MACE risk-factors (atrial fibrillation, diabetes, hyperlipidemia, hypertension, tobacco use) to capture migraine-specific predictors ( Figure 1) . Migraine medications were categorized into 9 classes; Triptans, Ditans, Gepants, Ergots, calcitonin gen-related peptide targeting medications (CGRPs), Beta-blockers, Topiramate, Tricyclic antidepressants, serotonin-norepinephrine reuptake inhibitors (SNRIs). Demographics (age and sex), migraine characteristics (chronic migraine, migraine with aura), and presence of WMH on MR imaging were curated. To model non-linear relationship between predictors, two comparative Random Forest (RF) models were evaluated for MACE prediction; i) Model-1: using demographics, migraine characteristics and WMH, ii) Model-2: medications, demographics, migraine characteristics and WMH ( Table 1) . Results: Model-2 outperformed Model-1 indicating combined interaction between medications, WMH, and migraine characteristics for estimating MACE risk. Model-2 achieved AUC of 0.68 for overall MACE, but showed higher performance for ischemic stroke (AUC 0.76). We visualized the effects of input features on ischemic stroke risk for four individual patients using SHAP force plots ( Figure 2 ). The model often considered use of betablockers, chronic migraine, and presence of WMH as factors for increasing ischemic stroke risk. Discussion: Our experiments show that medications, migraine features, WMH and demographics are more effective estimators for ischemic risk stroke risk compared to overall MACE risk, indicating stronger predictive correlation between ischemic stroke and migraine related clinical features. Interestingly, the model inferred the clinical practice pattern of not prescribing Triptans (TRIPTAN=0) to the patients with high risk of stroke from the data itself.

Abstract Objective The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicians and researchers in the headache field to facilitate interdisciplinary communications and research. Background The application of AI to the study of headache and other healthcare challenges is growing rapidly. It is critical that these findings be accurately interpreted by headache specialists, but this can be difficult for non‐AI specialists. Methods This paper is a narrative review of the fundamentals required to understand ML/AI headache research. Using guidance from key leaders in the field of headache medicine and AI, important references were reviewed and cited to provide a comprehensive overview of the terminology, methodology, applications, pitfalls, and bias of AI. Results We review how AI models are created, common model types, methods for evaluation, and examples of their application to headache medicine. We also highlight potential pitfalls relevant when consuming AI research, and discuss ethical issues of bias, privacy and abuse generated by AI. Additionally, we highlight recent related research from across headache‐related applications. Conclusion Many promising current and future applications of ML and AI exist in the field of headache medicine. Understanding the fundamentals of AI will allow readers to understand and critically appraise AI‐related research findings in their proper context. This paper will increase the reader's comfort in consuming AI/ML‐based research and will prepare them to think critically about related research developments.

Abstract Objective To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications. Background Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial‐and‐error approach is inefficient. Methods In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow‐up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta‐blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene‐related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre‐trained a deep neural network, “TabNet,” using 145 variables, then employed TabNet‐embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non‐responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held‐out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance. Results Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta‐blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta‐blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin. Conclusion We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine‐specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.