The DASH score: a simple score to assess risk for development of malignant middle cerebral artery infarction

Takashi Shimoyama1, Kazumi Kimura1, Junichi Uemura1

  • 1Department of Stroke Medicine, Kawasaki Medical School, 557 Matsushima, Kurashiki City, Okayama 701-0192, Japan.

Abstract

Insights

A new DASH score effectively predicts malignant MCA infarction (MMI) risk in large MCA infarctions. This simple grading scale uses DWI ASPECTS, ACA territory involvement, M1 SVS, and hyperglycemia to assess MMI development.

Area of Science:

  • Neurology
  • Radiology
  • Critical Care Medicine

Background:

  • Malignant Middle Cerebral Artery (MCA) Infarction (MMI) poses a significant risk for neurological deterioration and mortality.
  • Accurate risk assessment is crucial for timely intervention in patients with large MCA infarctions.

Purpose of the Study:

  • To develop a simple grading scale for predicting the risk of malignant MCA infarction (MMI).
  • To identify independent clinical and radiological factors associated with MMI development.

Main Methods:

  • Retrospective analysis of 119 patients with MCA infarction and proximal vessel occlusion within 24 hours of onset.
  • MRI including DWI and T2*-gradient echo sequences were used.
  • Multivariate logistic regression identified independent predictors of MMI, defined by clinical deterioration, midline shift, or herniation within 48 hours.

Main Results:

  • The study identified four independent predictors of MMI: DWI ASPECTS ≤ 3, ACA territory involvement, M1 susceptibility vessel sign (SVS), and hyperglycemia (glucose ≥ 145 mg/dl).
  • These factors were incorporated into the DASH score. The likelihood of MMI increased significantly with higher scores (9.1% for score 0 to 96.8% for score 3-4).
  • The DASH score demonstrated excellent predictive accuracy with a C-statistic of 0.88.

Conclusions:

  • The developed DASH score reliably assesses the risk of malignant MCA infarction in patients with large MCA infarctions.
  • This simple grading scale can aid clinicians in identifying high-risk patients who may benefit from closer monitoring or early intervention.