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. 2011 Apr;42(4):985-92.
doi: 10.1161/STROKEAHA.110.594879. Epub 2011 Feb 17.

Predicting clinical outcome in comatose cardiac arrest patients using early noncontrast computed tomography

Ona Wu  1 , Leonardo M Batista, Fabricio O Lima, Mark G Vangel, Karen L Furie, David M Greer
Affiliations

Predicting clinical outcome in comatose cardiac arrest patients using early noncontrast computed tomography

Ona Wu et al. Stroke. 2011 Apr.

Abstract

Background and purpose: Early assessment of the likelihood of neurological recovery in comatose cardiac arrest survivors remains challenging. We hypothesize that quantitative noncontrast computed tomography (NCCT) combined with neurological assessments, are predictive of outcome.

Methods: We analyzed data sets acquired from comatose cardiac arrest patients who underwent CT within 72 hours of arrest. Images were semiautomatically segmented into anatomic regions. Median Hounsfield units (HU) were measured regionally and in the whole brain (WB). Outcome was based on the 6-month modified Rankin Scale (mRS) score. Logistic regression was used to combine Glasgow Coma Scale (GCS) score measured on Day 3 post arrest (GCS_Day3) with imaging to predict poor outcome (mRS>4).

Results: WB HU (P=0.02) and the ratio of HU in the putamen to the posterior limb of the internal capsule (PLIC) (P=0.004) from 175 datasets from 151 patients were univariate predictors of poor outcome. Thirty-three patients underwent hypothermia treatment. Multivariate analysis showed that combining median HU in the putamen (P=0.0006) and PLIC (P=0.007) was predictive of poor outcome. Combining WB HU and GCS_Day3 resulted in 72% [61% to 80%] sensitivity and 100% [73% to 100%] specificity for predicting poor outcome in 86 patients with measurable GCS_Day3. This was an improvement over prognostic performance based on GCS_Day3≤8 (98% sensitive but 71% specific).

Discussion: Combining density changes on CT with GCS_Day3 may be useful for predicting poor outcome in comatose cardiac arrest patients who are neither rapidly improving nor deteriorating. Improved prognostication with CT compared with neurological assessments can be achieved in patients treated with hypothermia.

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Figures

Figure 1
Figure 1
(A) Probabilistic atlas: different color codes represent the probability of tissue found at a position to belong to various tissue regions (i.e. CN, putamen, thalamus, cerebellum, FL, insula, OL, PL, TL, WM, PLIC, CC, and CorRad), shown in axial, sagittal and coronal views. For clarity, only tissue with probability > 50% of classification is shown. Also shown, using the same dynamic range, are co-registered NCCT from (B) a 66 year-old woman who had a 6 month mRS=6 (died) and (C) a 41 year-old woman who had a 6 month mRS=1 (good outcome).
Figure 2
Figure 2
Receiver operator characteristic curves (ROC) for prediction of poor outcome using WB, putamen and PLIC, and putamen/PLIC individually and in combination with GCS_Day3. Also shown is the ROC curve for the model consisting of WB, FL, cerebellum, insula and GCS_Day3 which outperformed the other models at 100% specificity.
See this image and copyright information in PMC

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