Deep Learning-Based Computed Tomography Perfusion Imaging to Evaluate the Effectiveness and Safety of Thrombolytic Therapy for Cerebral Infarct with Unknown Time of Onset.

This study was aimed to discuss the effectiveness and safety of deep learning-based computed tomography perfusion (CTP) imaging in the thrombolytic therapy for acute cerebral infarct with unknown time of onset. A total of 100 patients with acute cerebral infarct with unknown time of onset were selected as the research objects. All patients received thrombolytic therapy. According to different image processing methods, they were divided into the algorithm group (artificial intelligence algorithm-based image processing group) and the control group (conventional method-based image processing group). After that, the evaluations of effectiveness and safety of thrombolytic therapy for the patients with acute cerebral infarct in the two groups were compared. The research results demonstrated that artificial intelligence algorithm-based CTP imaging showed significant diagnostic effects and the image quality in the algorithm group was remarkably higher than that in the control group (P < 0.05). Besides, the overall image quality of algorithm group was relatively higher. The differences in the National Institute of Health stroke scale (NIHSS) scores for the two groups indicated that the thrombolytic effect on the algorithm group was superior to that on the control group. Thrombolytic therapy for the algorithm group showed therapeutic effects on neurologic impairment. The symptomatic intracranial hemorrhage rate of the algorithm group within 24 hours was lower than the hemorrhage conversion rate of the control group, and the difference between the two groups was 14%. The data differences between the two groups showed statistical significance (P < 0.05). The results demonstrated that the safety of guided thrombolytic therapy for the algorithm group was higher than that in the control group. To sum up, deep learning-based CTP images showed the clinical application values in the diagnosis of cerebral infarct.

High Fibrinogen to Albumin Ratio: A Novel Marker for Risk of Stroke-Associated Pneumonia?

Background: Stroke-associated pneumonia (SAP) is associated with poor prognosis after acute ischemic stroke (AIS). Purpose: This study aimed to describe the parameters of coagulation function and evaluate the association between the fibrinogen-to-albumin ratio (FAR) and SAP in patients with AIS. Patients and methods: A total of 932 consecutive patients with AIS were included. Coagulation parameters were measured at admission. All patients were classified into two groups according to the optimal cutoff FAR point at which the sum of the specificity and sensitivity was highest. Propensity score matching (PSM) was performed to balance potential confounding factors. Univariate and multivariate logistic regression analyses were applied to identify predictors of SAP. Results: A total of 100 (10.7%) patients were diagnosed with SAP. The data showed that fibrinogen, FAR, and D-dimer, prothrombin time (PT), activated partial thromboplastin time (aPTT) were higher in patients with SAP, while albumin was much lower. Patients with SAP showed a significantly increased FAR when compared with non-SAP (P < 0.001). Patients were assigned to groups of high FAR (≥0.0977) and low FAR (<0.0977) based on the optimal cut-off value. Propensity score matching analysis further confirmed the association between FAR and SAP. After adjusting for confounding and risk factors, multivariate regression analysis showed that the high FAR (≥0.0977) was an independent variable predicting the occurrence of SAP (odds ratio =2.830, 95% CI = 1.654-4.840, P < 0.001). In addition, the FAR was higher in the severe pneumonia group when it was assessed by pneumonia severity index (P = 0.008). Conclusions: High FAR is an independent potential risk factor of SAP, which can help clinicians identify high-risk patients with SAP after AIS.

Relationship between plasma high-mobility group box-1 levels and clinical outcomes of ischemic stroke.

PURPOSE: High-mobility group box-1 (HMGB1) is regarded as a central mediator of inflammation and involved in many inflammatory diseases. This study aimed to investigate impact of plasma HMGB1 level on 1-year clinical outcomes of ischemic stroke. METHODS: Plasma HMGB1 levels of 338 patients were quantified by enzyme-linked immunosorbent assay. The end points were mortality and unfavorable outcome (modified Rankin Scale score>2) after 1 year. RESULTS: Plasma HMGB1 level emerged as an independent predictor of 1-year clinical outcomes. Its prognostic value was similar to National Institutes of Health Stroke Scale score's. It improved prognostic value of National Institutes of Health Stroke Scale score. CONCLUSION: Plasma HMGB1 level represents a novel biomarker for predicting 1-year clinical outcomes of ischemic stroke.