The journal of headache and pain
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Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting. ⋯ When validated using follow-up data, which was not included during model training, the model achieved 91%, 94%, 89% accuracies and 0.96, 0.94, 0.98 AUC for the total, interictal, and ictal/peri-ictal phases, respectively, confirming its robustness. Feature importance and clinical association analyses exhibited that the somatomotor, limbic, and default mode regions could be reliable markers of migraine. Our findings, which demonstrate a robust diagnostic performance using multimodal MRI features and a machine-learning framework, may offer a valuable approach for clinical diagnosis across diverse cohorts and help alleviate the decision-making burden for clinicians.
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Diagnosing headache disorders poses significant challenges, particularly in primary and secondary levels of care (PSLC), potentially leading to misdiagnosis and underdiagnosis. This study evaluates diagnostic agreement for migraine, tension-type headache (TTH), and cluster headache (CH) between PSLC and tertiary care (TLC) and assesses adherence to the International Classification of Headache Disorders 3rd edition (ICHD-3) guidelines. ⋯ Our findings indicate a significant degree of diagnostic agreement across different levels of care according to the ICHD-3 guidelines. However, there remains insufficient reliability in clinical diagnostics, highlighting the need for continued efforts to improve the early recognition and diagnostic accuracy and consistency of primary headaches to optimize patient care and treatment outcomes in Germany.
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Multicenter Study Observational Study
Effectiveness and tolerability of rimegepant in the acute treatment of migraine: a real-world, prospective, multicentric study (GAINER study).
Rimegepant, a novel oral calcitonin gene-related peptide receptor antagonist, has been recently approved for the acute migraine treatment. While its efficacy was confirmed in randomized clinical trials, no data is available regarding real-life effectiveness and tolerability. GAINER, a prospective, multicentric study, aimed to evaluate rimegepant effectiveness and tolerability in the real-world setting. ⋯ Our data confirms rimegepant effectiveness and safety in the acute migraine treatment in a real-world setting in a cohort of participants that includes subjects with episodic or chronic migraine, medication overuse and a high number of prior preventive treatment failures.
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Migraine is a common primary headache disorder, less frequently affecting men than women, and often regarded as predominantly a "women's disease." Despite this, migraine in men presents with unique characteristics in terms of symptoms, treatment responses, comorbidities, and pain perception. Historically, research has focused more on migraine in women, overlooking critical male-specific aspects. ⋯ Men are underrepresented in clinical migraine research. In contrast, preclinical studies often focus solely in male animals as a result of various misconceptions. This disparity necessitates greater focus on sex-specific aspects of migraine to enhance diagnosis, treatment, and research. Addressing stigma, increasing healthcare access, and ensuring balanced sex and gender representation in future studies is crucial for a comprehensive understanding and effective management of migraine for all patients.
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Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions. ⋯ Despite these advances, challenges such as data standardization, model explainability, and ethical considerations remain pivotal. Collaborative efforts between clinicians, biomedical and biotechnological engineers, AI scientists, legal representatives and bioethics experts are essential to overcoming these barriers and unlocking AI's full potential in transforming headache research and healthcare. This is a call to action in proposing novel frameworks for integrating AI-based technologies into headache care.