Comparison of Long-Term Outcomes and Associated Factors between Younger and Older Rural Ischemic Stroke Patients

Functional connectome hierarchy of thalamus impacts fatigue in acute stroke patients

This study aimed to explore the topographic features of thalamic subregions, functional connectomes and hierarchical organizations between thalamus and cortex in poststroke fatigue patients. We consecutively recruited 121 acute ischemic stroke patients (mean age: 59 years) and 46 healthy controls matched for age, sex, and educational level. The mean age was 59 years (range 19-80) and 38% of acute stroke patients were females. Resting-state functional and structural magnetic resonance imaging were conducted on all participants. The fatigue symptoms were measured using the Fatigue Severity Scale. The thalamic functional subdivisions corresponding to the canonical functional network were defined using the winner-take-all parcellation method. Thalamic functional gradients were derived using the diffusion embedding analysis. The results suggested abnormal functional connectivity of thalamic subregions primarily located in the temporal lobe, posterior cingulate gyrus, parietal lobe, and precuneus. The thalamus showed a gradual increase from the medial to the lateral in all groups, but the right thalamus shifted more laterally in poststroke fatigue patients than in non- poststroke fatigue patients. Poststroke fatigue patients also had higher gradient scores in the somatomotor network and the right medial prefrontal and premotor thalamic regions, but lower values in the right lateral prefrontal thalamus. The findings suggested that poststroke fatigue patients had altered functional connectivity and thalamocortical hierarchical organizations, providing new insights into the neural mechanisms of the thalamus.

Thrombolysis for central retinal artery occlusion: An individual participant-level meta-analysis

BACKGROUND

Whether thrombolysis improves outcomes in non-arteritic central retinal artery occlusion (naCRAO) is uncertain. We aimed to evaluate the rate of visual recovery after intra-venous thrombolysis (IVT) or intra-arterial thrombolysis (IAT) administration of tissue plasminogen activator (tPA) or urokinase among patients with naCRAO and explore the parameters affecting the final visual acuity (VA).

AIM

We systematically searched six databases. Logarithm of the minimum angle of resolution (logMAR) and VA of ⩾20/100 were used to quantify visual recovery. To explore the role of other factors on visual recovery, we defined two models for studies with aggregated data (designs 1 and 2) and 16 models for individual participant data (IPD, models 1-16).

SUMMARY OF REVIEW

We included data from 771 patients out of 72 publications in nine languages. Visual improvement for ⩾0.3 logMAR was reported in 74.3% of patients who received IVT-tPA within 4.5 h (CI: 60.9-86.0%; unadjusted rate: 73.2%) and 60.0% of those who received IAT-tPA within 24 h (CI: 49.1-70.5%; unadjusted rate: 59.6%). VA of ⩾20/100 was observed among 39.0% of patients after IVT-tPA within 4.5 h and 21.9% of those with IAT-tPA within 24 h. IPD models highlighted the association between improved visual outcomes and VA at presentation, at least 2 weeks follow-up before reporting the final VA, antiplatelet therapy, and shorter symptom onset to thrombolysis window.

CONCLUSION

Early thrombolytic therapy with tPA is associated with enhanced visual recovery in naCRAO. Future studies should refine the optimum time window for thrombolysis in naCRAO.

Defining the Age of Young Ischemic Stroke Using Data-Driven Approaches

Introduction: The cut-point for defining the age of young ischemic stroke (IS) is clinically and epidemiologically important, yet it is arbitrary and differs across studies. In this study, we leveraged electronic health records (EHRs) and data science techniques to estimate an optimal cut-point for defining the age of young IS. Methods: Patient-level EHRs were extracted from 13 hospitals in Pennsylvania, and used in two parallel approaches. The first approach included ICD9/10, from IS patients to group comorbidities, and computed similarity scores between every patient pair. We determined the optimal age of young IS by analyzing the trend of patient similarity with respect to their clinical profile for different ages of index IS. The second approach used the IS cohort and control (without IS), and built three sets of machine-learning models—generalized linear regression (GLM), random forest (RF), and XGBoost (XGB)—to classify patients for seventeen age groups. After extracting feature importance from the models, we determined the optimal age of young IS by analyzing the pattern of comorbidity with respect to the age of index IS. Both approaches were completed separately for male and female patients. Results: The stroke cohort contained 7555 ISs, and the control included 31,067 patients. In the first approach, the optimal age of young stroke was 53.7 and 51.0 years in female and male patients, respectively. In the second approach, we created 102 models, based on three algorithms, 17 age brackets, and two sexes. The optimal age was 53 (GLM), 52 (RF), and 54 (XGB) for female, and 52 (GLM and RF) and 53 (RF) for male patients. Different age and sex groups exhibited different comorbidity patterns. Discussion: Using a data-driven approach, we determined the age of young stroke to be 54 years for women and 52 years for men in our mainly rural population, in central Pennsylvania. Future validation studies should include more diverse populations.