Collateral Circulation and BNP in Predicting Outcome of Acute Ischemic Stroke Patients with Atherosclerotic versus Cardioembolic Cerebral Large-Vessel Occlusion Who Underwent Endovascular Treatment

Bibliometric Analysis of Psychological Distress in Stroke: Research Trends, Hot Spots, and Prospects- An Emphasis on China

Background

Currently, the psychological problems of stroke patients are of great concern. It is a hot topic of clinical care research to analyze and discuss the current status and hot spots, frontiers and development trends of research on psychological distress of stroke patients, and to develop and implement psycho-social care programs to improve the quality of life of patients.However, there is an absence of visual overviews to assess the published literature systematically.

Methods

The Web of Science (WOS) database was used to search the relevant literature in this field, spanning the period 2009-2023, and the countries, institutions, and research keywords in this field were visualized and analyzed by CiteSpace analysis software.

Results

An analysis of 416 papers found that the overall trend of psychological distress in stroke patients was increasing, and the research hotspots were mainly focusing on the relationship between different risk factors and psychological distress in stroke patients, psychological distress in stroke caregivers, positive psychology in stroke patients, and interventions on psychological distress in stroke patients. In the future, the research population may gradually shift to stroke caregivers, and the research focus will be on developing and studying scales.

Conclusion

Visual analysis of psychological distress studies in stroke patients can provide strategies for clinical interventions and broaden thinking about clinical care.

Association of intravascular enhancement sign on 3D-T1W TSE with collateral status in middle cerebral artery occlusion stroke

OBJECTIVE

The significance of the intravascular enhancement sign (IVES) on high-resolution magnetic resonance vascular wall imaging (HR-VWI) remains unclear. This study aimed to investigate the correlation between the IVES and collateral assessment derived from digital subtraction angiography (DSA).

METHOD

A total of 75 patients with occlusion of the first segment of the middle cerebral artery (MCA) who underwent HR-VWI and DSA examinations at our research institution between November 2016 and February 2023 were included. The number of vessels with IVES, IVES-Alberta Stroke Program Early Computed Tomography Score (ASPECTS), American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) collateral grade, and DSA collateral blood flow grade were retrospectively evaluated. Correlations between these indicators were assessed using Spearman's correlation.

RESULTS

Interrater agreement was good for the assessment of HR-VWI and DSA indicators. After adjustments for age, degree of wall enhancement, and hypertension, a multivariable ordinal logistic regression model identified both the number of IVES vessels (OR = 1.37; 95%CI [1.06-1.78]; P = 0.017) and IVES-ASPECTS (OR = 2.00; 95%CI [1.03-3.87]; P = 0.041) as independent predictors of ischemic stroke. In the patient group with acute ischemic stroke, we found weak correlations between the number of IVES vessels and the ASITN/SIR collateral grade (rho = -0.35; P = 0.002) and between the IVES-ASPECTS and ASITN/SIR collateral grade (rho = -0.27; P = 0.02). Moreover, there were strong correlations between the number of IVES vessels and the DSA collateral blood flow grade (rho = -0.74; P < 0.001) and between the IVES-ASPECTS and the DSA collateral blood flow grade (rho = -0.65; P < 0.001). The number of IVES vessels correlated strongly with the IVES-ASPECTS (rho = 0.92, P < 0.001).

CONCLUSION

We find that the IVES is closely associated with sluggish collateral blood flow, which further confirms the hemodynamic mechanism underlying the IVES in MCA occlusion.

Calculated plasma volume status is associated with poor outcomes in acute ischemic stroke treated with endovascular treatment

Background and purpose

The impact of calculated plasma volume status (PVS) on the prognosis of acute ischemic stroke treated with endovascular treatment (EVT) remains undetermined. This study aimed to investigate the association between PVS and 90 days functional outcomes after EVT.

Methods

We enrolled patients treated with EVT in the anterior circulation from a prospective registry. The endpoint was a modified Rankin scale score of ≥3 points at 90 days after EVT. We used multivariable logistic regression models to investigate the association between PVS and poor outcomes. We used the restricted cubic spline to present the linearity between PVS and poor outcomes.

Results

Among the 187 enrolled patients (median age, 65 years; 35.8% women), a total of 81 patients (43.3%) experienced poor outcomes at 90 days. In multivariable analyses, PVS was associated with poor outcomes despite increasing confounding factors (odds ratio, 3.157; 95% confidence interval, 1.942-5.534; P < 0.001). The restricted cubic spline revealed a positive correlation between PVS and the risk of poor outcomes after EVT (P for nonlinearity = 0.021).

Conclusion

Our study found that an elevated PVS value was associated with poor outcomes after EVT. Further prospective cohorts were warranted to evaluate the utility of PVS in AIS treated with EVT.

Predicting functional outcome in acute ischemic stroke patients after endovascular treatment by machine learning

Background

Endovascular therapy (EVT) was the standard treatment for acute ischemic stroke with large vessel occlusion. Prognosis after EVT is always a major concern. Here, we aimed to explore a predictive model for patients after EVT.

Method

A total of 156 patients were retrospectively enrolled. The primary outcome was functional dependence (defined as a 90-day modified Rankin Scale score ≤ 2). Least absolute shrinkage and selection operator and univariate logistic regression were used to select predictive factors. Various machine learning algorithms, including multivariate logistic regression, linear discriminant analysis, support vector machine, k-nearest neighbors, and decision tree algorithms, were applied to construct prognostic models.

Result

Six predictive factors were selected, namely, age, baseline National Institute of Health Stroke Scale (NIHSS) score, Alberta Stroke Program Early CT (ASPECT) score, modified thrombolysis in cerebral infarction score, symptomatic intracerebral hemorrhage (sICH), and complications (pulmonary infection, gastrointestinal bleeding, and cardiovascular events). Based on these variables, various models were constructed and showed good discrimination. Finally, a nomogram was constructed by multivariate logistic regression and showed a good performance.

Conclusion

Our nomogram, which was composed of age, baseline NIHSS score, ASPECT score, recanalization status, sICH, and complications, showed a very good performance in predicting outcome after EVT.