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Outcome prediction with resting-state functional connectivity after cardiac arrest
Franca Wagner1, Matthias Hänggi2, Anja Weck2
1Department of Diagnostic and Interventional Neuroradiology, Support Center for Advanced Neuroimaging (SCAN), Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, 3010, Bern, Switzerland. franca.wagner@insel.ch.
Scientific Reports
|July 18, 2020
Summary
Resting-state functional MRI (RS-fMRI) can predict neurological outcomes in comatose patients post-cardiac arrest. Brain connectivity patterns identified by RS-fMRI aid in assessing patient prognosis and guiding clinical decisions.
Area of Science:
- Neuroscience
- Medical Imaging
- Critical Care Medicine
Background:
- Predicting neurological outcome in comatose patients after successful cardiopulmonary resuscitation (CPR) is clinically challenging.
- Accurate prognostication is crucial for guiding treatment decisions and resource allocation.
Purpose of the Study:
- To evaluate the utility of resting-state functional magnetic resonance imaging (RS-fMRI) in predicting neurological outcome in comatose patients.
- To assess the impact of functional and effective connectivity within the default mode network (DMN) on 3-month functional outcomes.
Main Methods:
- RS-fMRI was employed in 90 comatose patients to analyze functional and effective connectivity within the DMN.
- A supervised machine-learning approach utilized seed-to-voxel and ROI-to-ROI feature analysis.
- Patient outcomes were classified using the Cerebral Performance Category (CPC) scale (CPC 1-3 good outcome vs. CPC 4-5 adverse outcome).
Main Results:
- The machine-learning model achieved high accuracy (87.8%), sensitivity (90.2%), and positive predictive value (91.7%) in classifying patient outcomes.
- Reduced thalamic connectivity was observed in patients lacking a conscious response.
- Decreased within-network connectivity in the DMN and cortico-thalamic circuits correlated significantly with poorer clinical outcomes at 3 months.
Conclusions:
- RS-fMRI measures of DMN and cortico-thalamic connectivity show significant potential as biomarkers for predicting neurological outcomes in comatose patients after cardiac arrest.
- These imaging markers may assist clinicians in early decision-making for post-cardiac arrest care.
