Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study.

OBJECTIVE: This study assesses the concordance in migraine diagnosis between an online, self-administered, Computer-based, Diagnostic Engine (CDE) and semi-structured interview (SSI) by a headache specialist, both using International Classification of Headache Disorders, 3rd edition (ICHD-3) criteria. BACKGROUND: Delay in accurate diagnosis is a major barrier to headache care. Accurate computer-based algorithms may help reduce the need for SSI-based encounters to arrive at correct ICHD-3 diagnosis. METHODS: Between March 2018 and August 2019, adult participants were recruited from three academic headache centers and the community via advertising to our cross-sectional study. Participants completed two evaluations: phone interview conducted by headache specialists using the SSI and a web-based expert questionnaire and analytics, CDE. Participants were randomly assigned to either the SSI followed by the web-based questionnaire or the web-based questionnaire followed by the SSI. Participants completed protocols a few minutes apart. The concordance in migraine/probable migraine (M/PM) diagnosis between SSI and CDE was measured using Cohen's kappa statistics. The diagnostic accuracy of CDE was assessed using the SSI as reference standard. RESULTS: Of the 276 participants consented, 212 completed both SSI and CDE (study completion rate = 77%; median age = 32 years [interquartile range: 28-40], female:male ratio = 3:1). Concordance in M/PM diagnosis between SSI and CDE was: κ = 0.83 (95% confidence interval [CI]: 0.75-0.91). CDE diagnostic accuracy: sensitivity = 90.1% (118/131), 95% CI: 83.6%-94.6%; specificity = 95.8% (68/71), 95% CI: 88.1%-99.1%. Positive and negative predictive values = 97.0% (95% CI: 91.3%-99.0%) and 86.6% (95% CI: 79.3%-91.5%), respectively, using identified migraine prevalence of 60%. Assuming a general migraine population prevalence of 10%, positive and negative predictive values were 70.3% (95% CI: 43.9%-87.8%) and 98.9% (95% CI: 98.1%-99.3%), respectively. CONCLUSION: The SSI and CDE have excellent concordance in diagnosing M/PM. Positive CDE helps rule in M/PM, through high specificity and positive likelihood ratio. A negative CDE helps rule out M/PM through high sensitivity and low negative likelihood ratio. CDE that mimics SSI logic is a valid tool for migraine diagnosis.

Exploring Natural Clusters of Chronic Migraine Phenotypes: A Cross-Sectional Clinical Study.

Heterogeneity in chronic migraine (CM) presents significant challenge for diagnosis, management, and clinical trials. To explore naturally occurring clusters of CM, we utilized data reduction methods on migraine-related clinical dataset. Hierarchical agglomerative clustering and principal component analyses (PCA) were conducted to identify natural clusters in 100 CM patients using 14 migraine-related clinical variables. Three major clusters were identified. Cluster I (29 patients) - the severely impacted patient featured highest levels of depression and migraine-related disability. Cluster II (28 patients) - the minimally impacted patient exhibited highest levels of self-efficacy and exercise. Cluster III (43 patients) - the moderately impacted patient showed features ranging between Cluster I and II. The first 5 principal components (PC) of the PCA explained 65% of variability. The first PC (eigenvalue 4.2) showed one major pattern of clinical features positively loaded by migraine-related disability, depression, poor sleep quality, somatic symptoms, post-traumatic stress disorder, being overweight and negatively loaded by pain self-efficacy and exercise levels. CM patients can be classified into three naturally-occurring clusters. Patients with high self-efficacy and exercise levels had lower migraine-related disability, depression, sleep quality, and somatic symptoms. These results may ultimately inform different management strategies.

Spontaneous extracranial hemorrhagic phenomena in primary headache disorders: A systematic review of published cases.

Background Head pain is a cardinal feature of primary headache disorders (PHDs) and is often accompanied by autonomic and vasomotor symptoms and/or signs. Spontaneous extracranial hemorrhagic phenomena (SEHP), including epistaxis, ecchymosis, and hematohidrosis (a disorder of bleeding through sweat glands), are poorly characterized features of PHDs. Aim To critically appraise the association between SEHP and PHDs by systematically reviewing and pooling all reports of SEHP associated with headaches. Methods Advanced searches using the PubMed/MEDLINE, Web of Science, Cochrane Library, Google Scholar, and ResearchGate databases were carried out for clinical studies by combining the terms "headache AND ecchymosis", "headache AND epistaxis", and "headache AND hematohidrosis" spanning all medical literature prior to October 10, 2015. Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology guidelines were applied. Results A total of 105 cases of SEHP associated with PHDs (83% migraine and 17% trigeminal autonomic cephalgias) were identified (median age 27 years, male to female ratio 1:2.3); 63% had epistaxis, 33% ecchymosis, and 4% hematohidrosis. Eighty-three percent of studies applied the International Classification of Headache Disorders diagnostic criteria. Eighty percent of the reported headaches were episodic and 20% were chronic. Twenty-four percent of studies reported recurrent episodes of SEHP. Conclusions Our results suggest that SEHP may be rare features of PHDs. Future studies would benefit from the systematic characterization of these phenomena.