Outcome prediction with resting-state functional connectivity after cardiac arrest.

Wagner, Franca; Hänggi, Matthias; Weck, Anja; Pastore-Wapp, Manuela; Wiest, Roland; Kiefer, Claus (2020). Outcome prediction with resting-state functional connectivity after cardiac arrest. Scientific reports, 10(1), p. 11695. Springer Nature 10.1038/s41598-020-68683-y

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Predicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential contribution of resting-state-functional magnetic resonance imaging (RS-fMRI) in predicting neurological outcome. RS-fMRI was used to evaluate functional and effective connectivity within the default mode network in a cohort of 90 comatose patients and their impact on functional neurological outcome after 3 months. The RS-fMRI processing protocol comprises the evaluation of functional and effective connectivity within the default mode network. Seed-to-voxel and ROI-to-ROI feature analysis was performed as starting point for a supervised machine-learning approach. Classification of the Cerebral Performance Category (CPC) 1-3 (good to acceptable outcome) versus CPC 4-5 (adverse outcome) achieved a positive predictive value of 91.7%, sensitivity of 90.2%, and accuracy of 87.8%. A direct link to the level of consciousness and outcome after 3 months was identified for measures of segregation in the precuneus, in medial and right frontal regions. Thalamic connectivity appeared significantly reduced in patients without conscious response. Decreased within-network connectivity in the default mode network and within cortico-thalamic circuits correlated with clinical outcome after 3 months. Our results indicate a potential role of these markers for decision-making in comatose patients early after cardiac arrest.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > Clinic of Intensive Care
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Wagner, Franca; Hänggi, Matthias; Weck, Anja; Pastore-Wapp, Manuela; Wiest, Roland and Kiefer, Claus

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2045-2322

Publisher:

Springer Nature

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

12 Aug 2020 09:05

Last Modified:

16 Aug 2020 02:51

Publisher DOI:

10.1038/s41598-020-68683-y

PubMed ID:

32678212

Additional Information:

Franca Wagner and Matthias Hänggi contributed equally.

BORIS DOI:

10.7892/boris.145885

URI:

https://boris.unibe.ch/id/eprint/145885

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