Changes of migraine aura with advancing age of patients

Scutelnic, Adrian; Drangova, Hristina; Klein, Antonia; Slavova, Nedelina; Beyeler, Morin; Lippert, Julian; Silimon, Norbert; Meinel, Thomas R.; Arnold, Marcel; Fischer, Urs; Riederer, Franz; Mattle, Heinrich P.; Jung, Simon; Schankin, Christoph J.

doi:10.1186/s10194-023-01642-w

J Headache Pain

2023

DOI: 10.1186/s10194-023-01642-w

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Changes of migraine aura with advancing age of patients

Adrian Scutelnic,

Hristina Drangova,

Antonia Klein

et al.

Abstract: Aim Given the similar presentation of migraine aura and acute ischemic stroke, advancing patient age might change the characteristics of migraine with aura (MA) and be clinically important. Clinical data, however, are limited. Experimental studies indicate a decrease in the magnitude of cortical spreading depression (CSD), the pathophysiological correlate of migraine aura, with advancing age. Our study aimed to assess the influence of age on the clinical features of MA. … Show more

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Cited by 3 publications

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References 17 publications

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“…Following publication of this article [ 1 ], the author group became aware of some errors in the Results section of the Abstract.…”

mentioning

confidence: 99%

Correction: Changes of migraine aura with advancing age of patients

Scutelnic,

Drangova,

Klein

et al. 2023

J Headache Pain

Self Cite

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“…Following publication of this article [ 1 ], the author group became aware of some errors in the Results section of the Abstract.…”

mentioning

confidence: 99%

Correction: Changes of migraine aura with advancing age of patients

Scutelnic,

Drangova,

Klein

et al. 2023

J Headache Pain

Self Cite

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Field-testing the explicit diagnostic criteria for transient ischemic attack: a diagnostic accuracy study

Scutelnic,

auf der Maur,

Branca

et al. 2024

J Neurol

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Background and aim Explicit diagnostic criteria for transient ischemic attack (TIA) (EDCT) have been recently proposed based on the assumption, that a migraine aura-like symptom is not typical for a TIA. However, migraine-like symptoms have been unexpectedly frequent in patients with confirmed ischemic stroke. This cross-sectional study aimed to field-test the EDCT to distinguish transient neurological symptoms caused by cerebral infarction from those caused by migraine aura. Methods The sensitivity, specificity, positive and negative predictive values of the EDCT score were calculated in samples of patients with (i) transient symptoms caused by cerebral infarction confirmed by imaging and (ii) patients with migraine with aura diagnosed according to the International Classification of Headache Disorders 3rd edition. Sensitivity, specificity, positive and negative predictive values of the original and modified EDCT were calculated, as well as area under the curve adjusted for age and sex using the logistic regression method. Results The study population included 59 patients with cerebral infarction and 324 patients with migraine with aura. The median age of the stroke group was 72 (IQR 61–81) and of the migraine group 39 (IQR 29–53). There were 36 (61%) men in the stroke group and 221 (68%) women in the migraine group. For the detection of TIA with imaging-proven cerebral infarction, the original EDCT had a sensitivity of 90% (95%CI 79–96), a specificity 77% (95%CI 72–82), a positive predictive value of 42% (95%CI 33–51), and the negative predictive value 98% (95% CI 95–99). For the modified EDCT, the sensitivity was 81% (95%CI 69–90), the specificity 97% (95%CI 94–98), the positive predictive value 81% (95%CI 69–90), and the negative predictive value 97% (95%CI 94–98). Conclusions The original and modified EDCT criteria miss up to 1 of 10 and 1 of 5 patients, respectively, with transient symptoms due to cerebral infarction. However, the modified EDCT criteria are more specific but less sensitive in detection of ischemic events. The optimal combination of clinical markers to reliably distinguish TIA from migraine aura remains to be found.

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Application Value of a Machine Learning Model in Predicting Mild Depression Associated with Migraine without Aura

Cui,

Pei1,

Yang

2024

Br J Hosp Med

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Aims/Background To investigate the application value of a machine learning model in predicting mild depression associated with migraine without aura (MwoA). Methods 178 patients with MwoA admitted to the Department of Neurology of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from March 2022 to March 2024 were selected as subjects. According to their inpatient medical records, 38 patients were selected as the validation group by random number method, and the remaining 140 patients were included in the modelling group. According to the diagnosis results, the patients in the modelling group and validation group were further divided into a MwoA with mild depression group and a MwoA without mild depression group. Results The results of univariate analysis and Multivariate logistic regression analysis showed that gender, course of disease, attack frequency, headache duration, Migraine Disability Assessment Questionnaire (MIDAS), and Headache Impact Test-6 (HIT-6) score were independent influencing factors for mild depression in MwoA patients (p < 0.05). The receiver operating characteristic (ROC) analysis results showed that the area under the curve of the established prediction model for MwoA patients with mild depression in the modelling group and the validation group was 0.982 and 0.901, respectively, the sensitivity was 0.978 and 0.857, respectively, and the specificity was 0.892 and 0.929, respectively. Conclusion Gender, course of disease, seizure frequency, headache duration, MIDAS score, and HIT-6 score are independent influencing factors for mild depression in patients with MwoA. The model displays good performance for the prediction of mild depression in patients with MwoA.

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Changes of migraine aura with advancing age of patients

Cited by 3 publications

References 17 publications

Correction: Changes of migraine aura with advancing age of patients

Correction: Changes of migraine aura with advancing age of patients

Field-testing the explicit diagnostic criteria for transient ischemic attack: a diagnostic accuracy study

Application Value of a Machine Learning Model in Predicting Mild Depression Associated with Migraine without Aura

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