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Migraine is a disabling primary headache disorder that directly affects more than one billion people worldwide. Despite its widespread prevalence, migraine remains under-diagnosed and under-treated. To support clinical decision-making, we convened a European panel of experts to develop a ten-step approach to the diagnosis and management of migraine. Each step was established by expert consensus and supported by a review of current literature, and the Consensus Statement is endorsed by the European Headache Federation and the European Academy of Neurology. In this Consensus Statement, we introduce typical clinical features, diagnostic criteria and differential diagnoses of migraine. We then emphasize the value of patient centricity and patient education to ensure treatment adherence and satisfaction with care provision. Further, we outline best practices for acute and preventive treatment of migraine in various patient populations, including adults, children and adolescents, pregnant and breastfeeding women, and older people. In addition, we provide recommendations for evaluating treatment response and managing treatment failure. Lastly, we discuss the management of complications and comorbidities as well as the importance of planning long-term follow-up.In this Consensus Statement, which is endorsed by the European Headache Federation and the European Academy of Neurology, an expert panel provides recommendations for the diagnosis and management of migraine to support clinical decision-making by general practitioners, neurologists and headache specialists.

BackgroundSeveral drugs are available for the preventive treatment of both episodic and chronic migraine. The choice of which therapy to initiate first, second, or third is not straightforward and is based on multiple factors, including general efficacy, tolerability, potential for serious adverse events, comorbid conditions, and costs. Recently, a new class of migraine preventive drugs was introduced, i.e. monoclonal antibodies against calcitonin gene-related peptide (CGRP) or its receptor.MethodsThe present article summarizes the evidence gathered with this new migraine preventive drug class from randomized placebo-controlled clinical trials. It further puts this into perspective next to the evidence gained by the most widely used agents for the prevention of episodic and chronic migraine with an emphasis on efficacy and the robustness with which this efficacy signal was obtained.ResultsAlthough being a relatively new class of migraine preventive drugs, monoclonal antibodies blocking the CGRP pathway have an efficacy which is at least comparable if not higher than those of the currently used preventive drugs. Moreover, the robustness of this efficacy signal is substantiated by several randomized clinical trials each including large numbers of patients. In addition, because of their excellent tolerability and with long-term safety data emerging, they seem to have an unprecedented efficacy over adverse effect profile, clearly resulting in an added value for migraine prevention.ConclusionsBalancing the data presented in the current manuscript with additional data concerning long term safety on the one hand and cost issues on the other hand, can be of particular use to health policy makers to implement this new drug class in the prevention of migraine.

The monoclonal antibodies (mAbs) blocking the calcitonin-gene related peptide (CGRP) pathway, collectively called here “anti-CGRP/rec mAbs”, have dramatically improved preventive migraine treatment. Although their efficacy and tolerability were proven in a number of randomized controlled trials (RCTs) and, maybe even more convincingly, in real world settings, a number of open questions remain. In this narrative review, we will analyze published data allowing insight in some of the uncertainties related to the use of anti-CGRP/rec mAbs in clinical practice: their differential efficacy in migraine subtypes, outcome predictors, switching between molecules, use in children and adolescents, long-term treatment adherence and persistence, effect persistence after discontinuation, combined treatment with botulinum toxin or gepants, added-value and cost effectiveness, effectiveness in other headache types, and potential contraindications based on known physiological effects of CGRP. While recent studies have already provided hints for some of these questions, many of them will not find reliable and definitive answers before larger studies, registries or dedicated RCTs are available.

BackgroundReal-world data are accumulating on the effectiveness, tolerability and safety of anti-calcitonin gene-related peptide pathway monoclonal antibodies for the preventive treatment of migraine. We performed a systematic review of the methodology of prospective, observational, clinic-based real-world evidence studies with these drugs in both episodic and chronic migraine.MethodsThe objectives were to evaluate the definitions and reported outcomes used, and to perform a risk of bias assessment for each of the different studies. PubMed and EMBASE were systematically queried for relevant scientific articles. Study quality assessment of the included studies was conducted using the “National Heart, Lung and Blood Institute (NHLBI) Study Quality Assessment Tool for Before-After (Pre-Post) Studies with No Control Group”.ResultsForty-six studies fitted the criteria for the systematic review and were included in the analysis. Ten studies (21.7%) defined a migraine day for the study, while only 5 studies defined a headache day for the study (10.9%). The most common primary endpoint/objective of the studies was change in monthly migraine days (n = 16, 34.8%), followed by responder rate (n = 15, 32.6%) and change in monthly headache days (n = 5, 10.9%). Eight studies (17.4%) did not define the primary endpoint/objective. Thirty-three studies were graded as “good” quality and 13 studies were graded as “fair”.ConclusionOur analysis shows rather significant heterogeneity and/or lack of predefined primary outcomes/objectives, definitions of outcomes measures and the use of longitudinal monitoring (e.g. headache diaries). Standardization of terminology, definitions and protocol procedures for real-world evidence studies of preventive treatments for migraine are recommended.Trial registrationThis study was registered with PROSPERO with ID CRD42022369366.

BackgroundHeadache medicine is largely based on detailed history taking by physicians analysing patients’ descriptions of headache. Natural language processing (NLP) structures and processes linguistic data into quantifiable units. In this study, we apply these digital techniques on self-reported narratives by patients with headache disorders to research the potential of analysing and automatically classifying human-generated text and information extraction in clinical contexts.MethodsA prospective cross-sectional clinical trial collected self-reported narratives on headache disorders from participants with either migraine or cluster headache. NLP was applied for the analysis of lexical, semantic and thematic properties of the texts. Machine learning (ML) algorithms were applied to classify the descriptions of headache attacks from individual participants into their correct group (migraine versus cluster headache).ResultsOne-hundred and twenty-one patients (81 participants with migraine and 40 participants with cluster headache) provided a self-reported narrative on their headache disorder. Lexical analysis of this text corpus resulted in several specific key words per diagnostic group (cluster headache: Dutch (nl): “oog” | English (en): “eye”, nl: “pijn” | en: “pain” and nl: “terug” | en: “back/to come back”; migraine: nl: “hoofdpijn” | en: “headache”, nl: “stress” | en: “stress” and nl: “misselijkheid” | en: “nausea”). Thematic and sentiment analysis of text revealed largely negative sentiment in texts by both patients with migraine and cluster headache. Logistic regression and support vector machine algorithms with different feature groups performed best for the classification of attack descriptions (with F1-scores for detecting cluster headache varying between 0.82 and 0.86) compared to naïve Bayes classifiers.ConclusionsDifferences in lexical choices between patients with migraine and cluster headache are detected with NLP and are congruent with domain expert knowledge of the disorders. Our research shows that ML algorithms have potential to classify patients’ self-reported narratives of migraine or cluster headache with good performance. NLP shows its capability to discern relevant linguistic aspects in narratives from patients with different headache disorders and demonstrates relevance in clinical information extraction. The potential benefits on the classification performance of larger datasets and neural NLP methods can be investigated in the future.Trial registrationThis study was registered with clinicaltrials.gov with ID NCT05377437.

Medication overuse in primary headache disorders is a worldwide phenomenon and has a role in the chronification of headache disorders. The burden of disease on individuals and societies is significant due to high costs and comorbidities. In the Third Edition of the International Classification of Headache Disorders, medication-overuse headache is recognized as a separate secondary entity next to mostly primary headache disorders, although many clinicians see the disease as a sole complication of primary headache disorders. In this review, we explore the historical background of medication-overuse headache, its epidemiology, phenomenology, pathophysiology and treatment options. The review explores relevant unanswered questions and summarizes the current debates in medication-overuse headache.

BackgroundWe performed a post hoc, subgroup analysis of a phase 3, randomized, double-blind, placebo-controlled study of erenumab for prevention of episodic migraine (STRIVE) to determine the efficacy and safety of erenumab in women with self-reported menstrual migraine.MethodsPatients received placebo, erenumab 70 mg, or erenumab 140 mg subcutaneously once monthly during the 6-month double-blind treatment phase of STRIVE. Women who reported history of menstrual migraine and who were ≤ 50 years old were included in the analysis. Endpoints were change from baseline in monthly migraine days (MMD) and monthly acute migraine-specific medication days (MSMD; among patients who took acute migraine-specific medications at baseline), proportion of patients achieving ≥ 50% reduction from baseline in MMD, and incidence of adverse events.ResultsAmong 814 women enrolled in STRIVE, 232 (28.5%) reported a history of menstrual migraine and were ≤ 50 years old. Of the 232 patients, 214 (92%) had a baseline MMD > 5, suggesting a high proportion of women with attacks outside of the 5-day perimenstrual window (2 days before and 3 days after the start of menstruation). Information on “migraine days” includes (and does not discriminate between) perimenstrual and intermenstrual migraine attacks. Between-group differences from placebo over months 4–6 for erenumab 70 mg and 140 mg were − 1.8 (P = 0.001) and − 2.1 (P < 0.001) days for MMD and − 1.6 (P = 0.002) and − 2.4 (P < 0.001) days for acute MSMD, respectively. The odds of having a ≥ 50% reduction from baseline in MMD over months 4–6 were 2.2 (P = 0.024) and 2.8 (P = 0.002) times greater for erenumab 70 mg and 140 mg, respectively, than for placebo. Erenumab had an overall safety profile comparable to placebo.ConclusionData from this subgroup analysis of women with menstrual migraine are consistent with data from the overall STRIVE episodic migraine population, supporting the efficacy and safety of erenumab in women who experience menstrual migraine.Trial registration: ClinicalTrials.gov, NCT02456740. Registered 28 May 2015.

BackgroundThere have been several calls for estimations of costs and consequences of headache interventions to inform European public-health policies. In a previous paper, in the absence of universally accepted methodology, we developed headache-type-specific analytical models to be applied to implementation of structured headache services in Europe as the health-care solution to headache. Here we apply this methodology and present the findings.MethodsData sources were published evidence and expert opinions, including those from an earlier economic evaluation framework using the WHO-CHOICE model. We used three headache-type-specific analytical models, for migraine, tension-type-headache (TTH) and medication-overuse-headache (MOH). We considered three European Region case studies, from Luxembourg, Russia and Spain to include a range of health-care systems, comparing current (suboptimal) care versus target care (structured services implemented, with provider-training and consumer-education). We made annual and 5-year cost estimates from health-care provider and societal perspectives (2020 figures, euros). We expressed effectiveness as healthy life years (HLYs) gained, and cost-effectiveness as incremental cost-effectiveness-ratios (ICERs; cost to be invested/HLY gained). We applied WHO thresholds for cost-effectiveness.ResultsThe models demonstrated increased effectiveness, and cost-effectiveness (migraine) or cost saving (TTH, MOH) from the provider perspective over one and 5 years and consistently across the health-care systems and settings. From the societal perspective, we found structured headache services would be economically successful, not only delivering increased effectiveness but also cost saving across headache types and over time. The predicted magnitude of cost saving correlated positively with country wage levels. Lost productivity had a major impact on these estimates, but sensitivity analyses showed the intervention remained cost-effective across all models when we assumed that remedying disability would recover only 20% of lost productivity.ConclusionsThis is the first study to propose a health-care solution for headache, in the form of structured headache services, and evaluate it economically in multiple settings. Despite numerous challenges, we demonstrated that economic evaluation of headache services, in terms of outcomes and costs, is feasible as well as necessary. Furthermore, it is strongly supportive of the proposed intervention, while its framework is general enough to be easily adapted and implemented across Europe.

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient monitoring, replacing subjective, intermittent self-reporting with objective, continuous monitoring. However, collecting and analyzing data from wearables presents several challenges, such as data entry errors, non-wear periods, missing data, and wearable artifacts. In this work, we explore these data analysis challenges using two real-world datasets (mBrain21 and ETRI lifelog2020). We introduce practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized pipeline for detecting non-wear periods. Additionally, we propose a visualization-oriented approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we present a bootstrapping methodology to evaluate the variability of wearable-derived features in the presence of partially missing data segments. Prioritizing transparency and reproducibility, we provide open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for improving wearable data collection and analysis.

BackgroundHealth economic evaluations support health-care decision-making by providing information on the costs and consequences of health interventions. No universally accepted methodology exists for modelling effectiveness and cost-effectiveness of interventions designed to close treatment gaps for headache disorders in countries of Europe (or elsewhere). Our aim here, within the European Brain Council’s Value-of-Treatment project, was to develop headache-type-specific analytical models to be applied to implementation of structured headache services in Europe as the health-care solution to headache.MethodsWe developed three headache-type-specific decision-analytical models using the WHO-CHOICE framework and adapted these for three European Region country settings (Luxembourg, Russia and Spain), diverse in geographical location, population size, income level and health-care systems and for which we had population-based data. Each model compared current (suboptimal) care vs target care (delivered in accordance with the structured headache services model). Epidemiological and economic data were drawn from studies conducted by the Global Campaign against Headache; data on efficacy of treatments were taken from published randomized controlled trials; assumptions on uptake of treatments, and those made for Healthy Life Year (HLY) calculations and target-care benefits, were agreed with experts. We made annual and 5-year cost estimates from health-care provider (main analyses) and societal (secondary analyses) perspectives (2020 figures, euros).ResultsThe analytical models were successfully developed and applied to each country setting. Headache-related costs (including use of health-care resources and lost productivity) and health outcomes (HLYs) were mapped across populations. The same calculations were repeated for each alternative (current vs target care). Analyses of the differences in costs and health outcomes between alternatives and the incremental cost-effectiveness ratios are presented elsewhere.ConclusionsThis study presents the first headache-type-specific analytical models to evaluate effectiveness and cost-effectiveness of implementing structured headache services in countries in the European Region. The models are robust, and can assist policy makers in allocating health budgets between interventions to maximize the health of populations.