We here present evidence-based guidelines for the pharmacological treatment of migraine. These guidelines, created by the Italian Society for the Study of Headache and the International Headache Society, aim to offer clear, actionable recommendations to healthcare professionals. They incorporate evidence-based recommendations from randomized controlled trials and expert-based opinions. The guidelines follow the Grading of Recommendations, Assessment, Development and Evaluation approach for assessing the quality of evidence. The guideline development involved a systematic review of literature across multiple databases, adherence to Cochrane review methods, and a structured framework for data extraction and interpretation. Although the guidelines provide a robust foundation for migraine treatment, they also highlight gaps in current research, such as the paucity of head-to-head drug comparisons and the need for long-term outcome studies. These guidelines serve as a resource to standardize migraine treatment and promote high-quality care across different healthcare settings.

We here present evidence-based guidelines for the pharmacological treatment of migraine. These guidelines, created by the Italian Society for the Study of Headache and the International Headache Society, aim to offer clear, actionable recommendations to healthcare professionals. They incorporate evidence-based recommendations from randomized controlled trials and expert-based opinions. The guidelines follow the GRADE approach for assessing the quality of evidence. The guideline development involved a systematic review of literature across multiple databases, adherence to Cochrane review methods, and a structured framework for data extraction and interpretation. Although the guidelines provide a robust foundation for migraine treatment, they also highlight gaps in current research, such as the paucity of head-to-head drug comparisons and the need for long-term outcome studies. These guidelines serve as a resource to standardize migraine treatment and promote high-quality care across different healthcare settings.

Early diagnosis and appropriate treatment are essential in meningitis and encephalitis management. We aimed to implement and verify an artificial intelligence (AI) model for early aetiological determination of patients with encephalitis and meningitis, and identify important variables in the classification process. In this retrospective observational study, patients older than 18 years old with meningitis or encephalitis at two centres in South Korea were enrolled for development (n = 283) and external validation (n = 220) of AI models, respectively. Their clinical variables within 24 h after admission were used for the multi-classification of four aetiologies including autoimmunity, bacteria, virus, and tuberculosis. The aetiology was determined based on the laboratory test results of cerebrospinal fluid conducted during hospitalization. Model performance was assessed using classification metrics, including the area under the receiver operating characteristic curve (AUROC), recall, precision, accuracy, and F1 score. Comparisons were performed between the AI model and three clinicians with varying neurology experience. Several techniques (eg, Shapley values, F score, permutation feature importance, and local interpretable model-agnostic explanations weights) were used for the explainability of the AI model. Between January 1, 2006, and June 30, 2021, 283 patients were enrolled in the training/test dataset. An ensemble model with extreme gradient boosting and TabNet showed the best performance among the eight AI models with various settings in the external validation dataset (n = 220); accuracy, 0.8909; precision, 0.8987; recall, 0.8909; F1 score, 0.8948; AUROC, 0.9163. The AI model outperformed all clinicians who achieved a maximum F1 score of 0.7582, by demonstrating a performance of F1 score greater than 0.9264. This is the first multiclass classification study for the early determination of the aetiology of meningitis and encephalitis based on the initial 24-h data using an AI model, which showed high performance metrics. Future studies can improve upon this model by securing and inputting time-series variables and setting various features about patients, and including a survival analysis for prognosis prediction. MD-PhD/Medical Scientist Training Program through the 10.13039/501100003710 Korea Health Industry Development Institute , funded by the Ministry of Health & Welfare, Republic of Korea.

Emerging evidence suggests that oral health conditions may exacerbate migraine, and saliva is a potential source of biomarkers for migraine. The 3-way interaction of the oral-gut-brain axis has been implicated in several neurologic disorders, but has rarely been studied in migraine. This study examined the oral and gut microbiomes simultaneously and identified several key oral microbes that may influence migraine. In this cross-sectional case-control study, participants were divided into 3 groups: episodic migraine (n = 55), chronic migraine (n = 55), and healthy control (HC) (n = 55). Demographic and clinical characteristics; lifestyle factors; and biological samples including saliva, stool, and blood were collected. Composition, function, and community type of the oral and gut microbiomes were compared among the 3 groups. Oral dysbiosis was more pronounced than gut dysbiosis in the migraine groups, with 13 oral genera significantly enriched or depleted compared with HCs. The migraine groups showed increased abundance of Gemella, Streptococcus, Granulicatella, and Rothia and decreased abundance of Alloprevotella, Veillonella, Haemophilus, Selenomonas, Campylobacter, Cardiobacterium, Megasphaera, and Kingella after adjustment for demographic and lifestyle factors including diet. The enriched oral genera within the migraine groups were associated with carbohydrate metabolic pathways, whereas the depleted oral genera were associated with pathways related to nitrogen. A significant proportion of the oral microbial signatures of migraine included genera capable of reducing nitrate and/or nitrite. Some of these oral microbial signatures of migraine had a relative abundance that was positively or negatively associated with the number of headache days per 30 days and formed distinct microbial clusters in both the oral cavity and gut. Machine learning classifiers using the oral microbiome effectively classified migraine status, with an area under the receiver-operating characteristic curve of 0.83-0.88. Our findings suggest that oral dysbiosis may be involved in the development of migraine and highlight specific oral microbes as potential diagnostic biomarkers and therapeutic targets for migraine.

Background: Delirium affects up to 50% of patients following high-risk surgeries and is associated with poor long-term prognosis. This study employed machine learning to predict delirium using polysomnography (PSG) and sleep-disorder questionnaire data, and aimed to identify key sleep-related factors for improved interventions and patient outcomes. Methods: We studied 912 adults who underwent surgery under general anesthesia at a tertiary hospital (2013–2024) and had PSG within 5 years of surgery. Delirium was assessed via clinical diagnoses, antipsychotic prescriptions, and psychiatric consultations within 14 days postoperatively. Sleep-related data were collected using PSG and questionnaires. Machine learning predictions were performed to identify postoperative delirium, focusing on model accuracy and feature importance. Results: This study divided the 912 patients into an internal training set (700) and an external test set (212). Univariate analysis identified significant delirium risk factors: midazolam use, prolonged surgery duration, and hypoalbuminemia. Sleep-related variables such as fewer rapid eye movement (REM) episodes and higher daytime sleepiness were also linked to delirium. An extreme gradient-boosting-based classification task achieved an AUC of 0.81 with clinical variables, 0.60 with PSG data alone, and 0.84 with both, demonstrating the added value of PSG data. Analysis of Shapley additive explanations values highlighted important predictors: surgery duration, age, midazolam use, PSG-derived oxygen saturation nadir, periodic limb movement index, and REM episodes, demonstrating the relationship between sleep patterns and the risk of delirium. Conclusions: The artificial intelligence model integrates clinical and sleep variables and reliably identifies postoperative delirium, demonstrating that sleep-related factors contribute to its identification. Predicting patients at high risk of developing postoperative delirium and closely monitoring them could reduce the costs and complications associated with delirium.

OBJECTIVES: Clinical studies have suggested an association between migraine and the occurrence of Parkinson’s disease (PD). However, it is unknown whether migraine affects PD risk. We aimed to investigate the incidence of PD in patients with migraine and to determine the risk factors affecting the association between migraine and PD incidence.METHODS: Using the Korean National Health Insurance System database (2002-2019), we enrolled all Koreans aged ≥40 years who participated in the national health screening program in 2009. International Classification of Diseases (10th revision) diagnostic codes and Rare Incurable Diseases System diagnostic codes were used to define patients with migraine (within 12 months of enrollment) and newly diagnosed PD.RESULTS: We included 214,193 patients with migraine and 5,879,711 individuals without migraine. During 9.1 years of follow-up (55,435,626 person-years), 1,973 (0.92%) and 30,664 (0.52%) individuals with and without migraine, respectively, were newly diagnosed with PD. Following covariate adjustment, patients with migraine showed a 1.35-fold higher PD risk than individuals without migraine. The incidence of PD was not significantly different between patients with migraine with aura and those without aura. In males with migraine, underlying dyslipidemia increased the risk of PD (p=0.012). In contrast, among females with migraine, younger age (<65 years) increased the risk of PD (p=0.038).CONCLUSIONS: Patients with migraine were more likely to develop PD than individuals without migraine. Preventive management of underlying comorbidities and chronic migraine may affect the incidence of PD in these patients. Future prospective randomized clinical trials are warranted to clarify this association.