Prognostic significance of blood pressure parameters after mechanical thrombectomy according to collateral status

Abstract Background Mechanical thrombectomy (MT) has been proven as an effective and safe therapy for patients with acute ischemic stroke from large vessel occlusion. However, there is still a controversial topic about post-procedural management including blood pressure (BP). Methods A total of 294 patients who received MT in Second Affiliated Hospital of Soochow University from April 2017 to September 2021 were included consecutively. The association of blood pressure parameters (BPV and hypotension time) with poor functional outcome was evaluated using logistic regression models. Meanwhile, the effects of BP parameters on mortality was analyzed using cox proportional hazards regression models. Furthermore, the corresponding multiplicative term was added to the above models to study the interaction between BP parameters and CS. Results Two hundred ninety four patients were included finally. The mean age was 65.5 years. At the 3-month follow-up, 187(61.5%) had poor functional outcome and 70(23.0%) died. Regardless of the CS, BP CV is positively associated with poor outcome. Hypotension time was negatively associated with poor outcome. We conducted a subgroup analysis according to CS. BPV was significantly associated with mortality at 3-month and displayed a trend toward poor outcome for patients with poor CS only. The interaction between SBP CV and CS with respect to mortality after adjusting for confounding factors was statistically significant (P for interaction = 0.025) and the interaction between MAP CV and CS with respect to mortality after multivariate adjustment was also statistically significant (P for interaction = 0.005). Conclusion In MT-treated stroke patients, higher BPV in the first 72 h is significantly associated with poor functional outcome and mortality at 3-month regardless of CS. This association was also found for hypotension time. Further analysis showed CS modified the association between BPV and clinical prognosis. BPV displayed a trend toward poor outcome for patients with poor CS

Functional connectivity of the language area in migraine: a preliminary classification model

Abstract Background Migraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and treatment. Methods Baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI(rfMRI), and diffusion weighted scans were obtained from 31 patients with migraine, and 17 controls. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into diagnostic groups based on functional connectivity (FC) and derive networks and parcels contributing to the model. PageRank centrality analysis was also performed on the structural connectome to identify changes in hubness. Results Our model attained an area under the receiver operating characteristic curve (AUC-ROC) of 0.68, which rose to 0.86 following hyperparameter tuning. FC of the language network was most predictive of the model’s classification, though patients with migraine also demonstrated differences in the accessory language, visual and medial temporal regions. Several analogous regions in the right hemisphere demonstrated changes in PageRank centrality, suggesting possible compensation. Conclusions Although our small sample size demands caution, our preliminary findings demonstrate the utility of our method in providing a network-based perspective to diagnosis and treatment of migraine