Post-anoxic quantitative MRI changes may predict emergence from coma and functional outcomes at discharge

Alexandra S Reynolds1, Xiaotao Guo2, Elizabeth Matthews1

  • 1Department of Neurology, Columbia University, New York, NY, USA.

Resuscitation
|June 19, 2017
PubMed
Abstract

Insights

Quantitative MRI shows promise in predicting neurological outcomes after cardiac arrest. Diffusion-weighted imaging (DWI) metrics are superior to pupillary reflexes for predicting coma persistence and poor functional outcomes post-cardiac arrest.

Area of Science:

  • Neurology
  • Radiology
  • Critical Care Medicine

Background:

  • Traditional neurological prognosis predictors are unreliable post-targeted temperature management.
  • Absence of pupillary reflexes is a reliable predictor of poor outcome.
  • Diffusion-weighted imaging (DWI) shows potential for predicting neurological recovery.

Purpose of the Study:

  • Compare quantitative MRI characteristics with pupillary exam for predicting outcomes.
  • Evaluate the predictive value of apparent diffusion coefficient (ADC) sequences.
  • Assess prediction of coma persistence and functional outcomes at discharge.

Main Methods:

  • Identified 69 patients with MRIs within seven days of cardiac arrest.
  • Used a semi-automated algorithm for quantitative volumetric analysis of ADC sequences.
  • Estimated ROC-AUC to compare predictive values of MRI and pupillary exam.

Main Results:

  • ≥2.8% diffusion restriction at ADC ≤650×10-6m2/s predicted failure to wake up with 100% specificity.
  • ADC changes at ≤450 and ≤650×10-6m2/s significantly outperformed pupillary reflexes in predicting coma persistence.
  • >0.01% diffusion restriction at ADC ≤450×10-6m2/s predicted poor functional outcome in survivors with 100% specificity.

Conclusions:

  • Quantitative brain MRI, specifically post-anoxic diffusion changes, aids in predicting neurological prognosis.
  • DWI metrics offer a more objective and potentially superior method for outcome prediction compared to pupillary exams.
  • This imaging technique may improve clinical decision-making for patients after cardiac arrest.

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