EASY score (Eloquent, Age and baseline SYmptoms score) for outcome prediction in patients with acute ischemic stroke

Influence of Age on Outcomes of Endovascular Treatment in Posterior versus Anterior Circulation Stroke: A Comparative Analysis in a Nationwide Registry

INTRODUCTION

It is unknown whether the influence of age on clinical outcomes post endovascular treatment (EVT) is similar in posterior circulation stroke (PCS) compared to anterior circulation stroke (ACS).

METHODS

Data were extracted from the ETIS registry, a nationwide prospective, multicenter, observational registry for stroke EVT procedures in France. We included patients treated with EVT for PCS or ACS between January 2015 and August 2023. The relationship between patient age and clinical outcome variables was studied using regression analysis. Good clinical outcome was defined as modified Rankin scale score ≤2 at 90 days.

RESULTS

The study included 935 patients with PCS and 11,427 patients with ACS. There were no significant differences between PCS and ACS in the influence of age on the likelihood of good clinical outcome. The influence of age on the likelihood of mortality was significantly different. Mortality was higher for PCS between 30 and 85 years, without significant differences in younger or older patients. Elderly patients (≥80 years) with PCS had similar rates of good clinical outcome and mortality compared to patients with ACS from the same age group.

CONCLUSION

In this comparative analysis assessing the impact of age on EVT outcomes in posterior versus anterior circulation stroke, differences were observed in middle-aged patients, whereas outcomes tended to converge in young and elderly age groups. Elderly PCS patients had similar outcomes as ACS patients from the same age group.

Predicting self-perceived manual ability at three and six months after stroke: A prospective longitudinal study

BACKGROUND

Recovery of manual ability is a critical issue in rehabilitation. Currently, little is known regarding the baseline predictors of self-perceived manual ability, which could capture information on individual's perceived functional ability, especially in carrying-out routine tasks outside clinical settings.

OBJECTIVE

To identify baseline predictors, which can be easily obtained within clinical settings, of self-perceived manual ability at three and six months after discharge from a stroke unit.

METHODS

A 6-month longitudinal study was carried-out. Participants were recruited from a stroke unit of a public hospital. The dependent outcome was self-perceived manual ability, and the following predictors were investigated: age, stroke severity, upper-limb motor impairments, cognitive function, muscle strength, and functional capacity. Linear regression analyses were employed to identify multivariate predictors of manual ability at three and six months after discharge (α=5%).

RESULTS

Participated 131 individuals, 69 women (mean age of 60 years). Regression analyses revealed that stroke severity and age accounted for 31% and 47% of the variance in manual ability at three and six months after stroke, respectively. Stroke severity was the best predictor of manual ability at three (R2=29%; F=44.7; p<0.0001) and six months (R2=45%; F=88.2; p<0.0001) after stroke, respectively.

CONCLUSION

Stroke severity showed to be the best predictor of manual ability at both three and six months after stroke. Although significant, age added little to the explained variance.

Explainable machine learning for long-term outcome prediction in two-center stroke patients after intravenous thrombolysis

Objective

Neurological outcome prediction in patients with ischemic stroke is very critical in treatment strategy and post-stroke management. Machine learning techniques with high accuracy are increasingly being developed in the medical field. We studied the application of machine learning models to predict long-term neurological outcomes in patients with after intravenous thrombolysis.

Methods

A retrospective cohort study was performed to review all stroke patients with intravenous thrombolysis. Patients with modified Rankin Score (mRs) less than two at three months post-thrombolysis were considered as good outcome. The clinical features between stroke patients with good and with poor outcomes were compared using three different machine learning models (Random Forest, Support Vector Machine and Logistic Regression) to identify which performed best. Two datasets from the other stroke center were included accordingly for external verification and performed with explainable AI models.

Results

Of the 488 patients enrolled in this study, and 374 (76.6%) patients had favorable outcomes. Patients with higher mRs at 3 months had increased systolic pressure, blood glucose, cholesterol (TC), and 7-day National Institute of Health Stroke Scale (NIHSS) score compared to those with lower mRs. The predictability and the areas under the curves (AUC) for the random forest model was relatively higher than support vector machine and LR models. These findings were further validated in the external dataset and similar results were obtained. The explainable AI model identified the risk factors as well.

Conclusion

Explainable AI model is able to identify NIHSS_Day7 is independently efficient in predicting neurological outcomes in patients with ischemic stroke after intravenous thrombolysis.

Prognostic nomogram for the outcomes in acute stroke patients with intravenous thrombolysis

Background and purpose

The prediction of neurological outcomes in ischemic stroke patients is very useful in treatment choices, as well as in post-stroke management. This study is to develop a convenient nomogram for the bedside evaluation of stroke patients with intravenous thrombolysis.

Materials and methods

We reviewed all enrolled stroke patients with intravenous thrombolysis retrospectively. Favorable outcome was defined as modified Rankin Score (mRs) less than 2 at 90 days post thrombolysis. We compared the clinical characteristics between patients with favorable outcome and poor outcome. Then, we applied logistic regression models and compared their predictability.

Results

A total of 918 patients were enrolled in this study, 448 patients from one hospital were included to develop a nomogram, whereas 470 patients from the other hospital were used for the external validation. Associated risk factors were identified by multivariate logistic regression. The nomogram was validated by the area under the receiver operating characteristic curve (AUC). A nomogram was developed with baseline NIHSS, blood sugar, blood cholesterol level, part-and full anterior circulation infarction (OCSP type). The AUC was 0.767 (95% CI 0.653–0.772) and 0.836 (95% CI 0.697–0.847) in the derivation and external validation cohorts, respectively. The calibration plot for the probability of severe neurological outcome showed an optimal agreement between the prediction by nomogram and actual observation in both derivation and validation cohorts.

Conclusion

A convenient outcome evaluation nomogram for patients with intravenous thrombolysis was developed, which could be used by physicians in making clinical decisions and predicting patients’ prognosis.