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.

Background: Headache 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. Methods: A 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). Results: One-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. Conclusions: Differences 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 registration: This study was registered with clinicaltrials.gov with ID NCT05377437.

Background: The diagnosis of headache disorders relies on the correct classification of individual headache attacks. Currently, this is mainly done by clinicians in a clinical setting, which is dependent on subjective self-reported input from patients. Existing classification apps also rely on self-reported information and lack validation. Therefore, the exploratory mBrain study investigates moving to continuous, semi-autonomous and objective follow-up and classification based on both self-reported and objective physiological and contextual data. Methods: The data collection set-up of the observational, longitudinal mBrain study involved physiological data from the Empatica E4 wearable, data-driven machine learning (ML) algorithms detecting activity, stress and sleep events from the wearables’ data modalities, and a custom-made application to interact with these events and keep a diary of contextual and headache-specific data. A knowledge-based classification system for individual headache attacks was designed, focusing on migraine, cluster headache (CH) and tension-type headache (TTH) attacks, by using the classification criteria of ICHD-3. To show how headache and physiological data can be linked, a basic knowledge-based system for headache trigger detection is presented. Results: In two waves, 14 migraine and 4 CH patients participated (mean duration 22.3 days). 133 headache attacks were registered (98 by migraine, 35 by CH patients). Strictly applying ICHD-3 criteria leads to 8/98 migraine without aura and 0/35 CH classifications. Adapted versions yield 28/98 migraine without aura and 17/35 CH classifications, with 12/18 participants having mostly diagnosis classifications when episodic TTH classifications (57/98 and 32/35) are ignored. Conclusions: Strictly applying the ICHD-3 criteria on individual attacks does not yield good classification results. Adapted versions yield better results, with the mostly classified phenotype (migraine without aura vs. CH) matching the diagnosis for 12/18 patients. The absolute number of migraine without aura and CH classifications is, however, rather low. Example cases can be identified where activity and stress events explain patient-reported headache triggers. Continuous improvement of the data collection protocol, ML algorithms, and headache classification criteria (including the investigation of integrating physiological data), will further improve future headache follow-up, classification and trigger detection. Trial registration This trial was retrospectively registered with number NCT04949204 on 24 June 2021 at www.clinicaltrials.gov.

Background: Real-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. Methods: The 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”. Results: Forty-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”. Conclusion: Our 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 registration: This study was registered with PROSPERO with ID CRD42022369366.