An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score.

Area of Science:

• Neurology
• Clinical Prediction Modeling
• Stroke Medicine

Background:

• Predicting functional outcome in acute ischemic stroke (AIS) is crucial for patient management.
• Existing prediction tools may lack simplicity or rely on data not immediately available.
• A need exists for an accessible scoring system for early risk stratification.

Purpose of the Study:

• To develop and validate a straightforward, integer-based score to predict functional outcome in AIS.
• The score utilizes variables readily obtainable upon emergency room admission.
• To enhance clinical decision-making and research in stroke care.

Main Methods:

• Logistic regression analysis was employed in a derivation cohort (ASTRAL registry) to identify predictors of unfavorable outcome (modified Rankin Scale score >2 at 3 months).
• An integer-based point-scoring system was generated from the β-coefficients of the multivariate model.
• Internal validation (2-fold cross-validation) and external validation in two independent cohorts (Athens and Vienna) were performed.

Main Results:

• Six independent predictors were identified: Age (A), Stroke Severity (S - NIH Stroke Scale), Stroke onset to Admission time (T), Visual fields (R), acute Glucose (A), and Level of Consciousness (L).
• The ASTRAL score demonstrated strong predictive performance with an Area Under the Curve (AUC) of 0.850 in the derivation cohort and good calibration.
• External validation showed robust performance with pooled AUC of 0.902, indicating reliability across different populations.

Conclusions:

• The ASTRAL score is a simple, integer-based tool for predicting functional outcomes in acute ischemic stroke patients.
• It effectively utilizes six easily accessible variables at hospital admission.
• The score's strong performance in external validation suggests its utility in clinical practice and stroke research.