Cutrer, Fred M

Background Migraine is a highly prevalent neurological disorder with many treatment options, both pharmacological and non-pharmacological. Artificial intelligence (AI) has great potential to optimize treatment selection strategies for individual patients. This review provides an overview of AI models and the techniques used to predict migraine treatment outcomes. Methods We conducted a literature search in PubMed and examined studies that reported employing AI models to predict migraine preventive and acute treatment outcomes. We also explored incorporating AI/machine learning to enhance personalized migraine treatment strategies, including forecasting migraine attacks. Additionally, we summarized future research directions, including incorporating multimodality data, using AI frameworks for the discovery of novel treatment targets, and advancing the field with innovative AI techniques such as digital twins, conversational AI and virtual AI agents. Results Studies have employed ML and deep learning on a combination of clinical features and imaging data to predict acute or preventive migraine treatment outcomes with reported success. Continued model optimization, validation, and prospective assessment of the clinical utility of deploying ML models in real-world settings are crucial. Conclusions While AI has demonstrated success in predicting migraine treatment responses, future research incorporating novel AI techniques and diverse data sources could pave the way to advance personalized migraine treatment.

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.

This report reviews a series of 3 patients who developed superficial siderosis following posterior fossa operations in which dural closure was incomplete. In all 3 patients, revision surgery and complete duraplasty was performed to halt the progression of superficial siderosis. Following surgery, 2 patients experienced resolution of their CSF xanthochromia while 1 patient had reduced CSF xanthochromia. In this paper the authors also review the etiology, pathophysiology, diagnosis, and treatment of this condition. The authors suggest that posterior fossa dural patency and pseudomeningocele are risk factors for the latent development of superficial siderosis and recommend that revision duraplasty be performed in patients with posterior fossa pseudomeningoceles and superficial siderosis to prevent progression of the disease.