The journal of headache and pain
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Triptans are migraine-specific acute treatments. A well-accepted definition of triptan failure is needed in clinical practice and for research. The primary aim of the present Consensus was to provide a definition of triptan failure. To develop this definition, we deemed necessary to develop as first a consensus definition of effective treatment of an acute migraine attack and of triptan-responder. ⋯ The novel definitions can be useful in clinical practice for the assessment of acute attack treatments patients with migraine. They may be helpful in identifying people not responding to triptans and in need for novel acute migraine treatments. The definitions will also be of help in standardizing research on migraine acute care.
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Clinical Trial
Real-world experience with 240 mg of galcanezumab for the preventive treatment of cluster headache.
Galcanezumab of 300 mg monthly is the FDA approved preventive medication for cluster headache (CH) during the cluster period. Compared to the 120 mg galcanezumab syringe for the treatment of migraines, the 100 mg syringe for CH has globally not been as widely available. The aim of our study was to investigate the preventive efficacy and tolerability of two 120 mg galcanezumab doses for episodic CH in clinical practices. ⋯ One 240 mg dose of galcanezumab with/without conventional therapy for the prevention of CH is considered effective and safe in clinical practices, as seen in the clinical trial of galcanezumab.
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Migraine aura is a transient, fully reversible visual, sensory, or other central nervous system symptom that classically precedes migraine headache. This study aimed to investigate cerebral blood flow (CBF) alterations of migraine with aura patients (MwA) and without aura patients (MwoA) during inter-ictal periods, using arterial spin labeling (ASL). ⋯ In this study, CBF abnormalities of MwA were identified in multiple brain regions, which might help better understand migraine-stroke connection mechanisms and may guide patient-specific decision-making.
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To identify and validate the neural signatures of resting-state oscillatory connectivity for chronic migraine (CM), we used machine learning techniques to classify patients with CM from healthy controls (HC) and patients with other pain disorders. The cross-sectional study obtained resting-state magnetoencephalographic data from 240 participants (70 HC, 100 CM, 35 episodic migraine [EM], and 35 fibromyalgia [FM]). Source-based oscillatory connectivity of relevant cortical regions was calculated to determine intrinsic connectivity at 1-40 Hz. ⋯ The classification model with these features exhibited excellent performance in distinguishing patients with CM from HC (accuracy ≥ 86.8%, area under the curve (AUC) ≥ 0.9) and from those with EM (accuracy: 94.5%, AUC: 0.96). The model also achieved high performance (accuracy: 89.1%, AUC: 0.91) in classifying CM from other pain disorders (FM in this study). These resting-state magnetoencephalographic electrophysiological features yield oscillatory connectivity to identify patients with CM from those with a different type of migraine and pain disorder, with adequate reliability and generalizability.
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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. ⋯ 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.