Rofo 2016; 188 - RK325_5
DOI: 10.1055/s-0036-1581742

Automated assessment of early hypoxic brain edema in non-enhanced CT predicts outcome in patients after cardiac arrest

U Hanning 1, P Sporns 1, P Lebiedz 2, T Niederstadt 1, T Zoubi 1, R Schmidt 3, S Knecht 4, W Heindel 1, A Kemmling 5
  • 1Universitätsklinikum Münster, Institut für Klinische Radiologie, Münster
  • 2Universitätsklinikum Münster, Department für Kardiologie und Angiologie, Münster
  • 3Westfälische Wilhelms-Universität Münster, Institut für Biometrie und klinische Forschung, Münster
  • 4St. Mauritius Therapieklinik, Klinik für Neurologie, Meerbusch
  • 5Universitätsklinikum Schleswig-Holstein, Institut für Neuroradiologie, Lübeck

Zielsetzung:

Early prediction of potential neurological recovery in patients after cardiac arrest is challenging. Recent studies suggest that the densitrometic gray-white matter ratio (GWR) determined from cranial computed tomography (CT) scans is a reliable predictor of poor outcome. We evaluated an automated, rater independent method to determine GWR in CT as an early objective imaging predictor of clinical outcome.

Material und Methodik:

We analyzed imaging data of 84 patients after cardiac arrest that underwent noncontrast CT within 24 hours after arrest. To determine GWR in CT we applied two methods using a recently published automated probabilistic gray-white matter segmentation algorithm (GWR_aut) and conventional manual measurements within gray-white regions of interest (GWR_man). Neurological outcome was graded by the cerebral performance category (CPC) score at intensive care unit discharge. The performance of GWR measures (automated and manual) to predict the binary clinical endpoints of poor (CPC3 – 5) and good outcome (CPC1 – 2) was assessed by ROC analysis with increasing discrimination thresholds. Results of GWR_aut were compared to GWR_man of two raters.

Ergebnisse:

Of 84 patients, 55 (65%) showed a poor outcome. ROC curve analysis revealed reliable outcome prediction of GWR_aut (AUC 0.860) and GWR_man (AUC 0.707 and 0.699, respectively). Predictive power of GWR_aut was higher than GWR_man by each rater (p = 0.019 and p = 0.021, respectively) at an optimal cut-off of 1.084 to predict poor outcome (optimal criterion with 92.7% sensitivity, 72.4% specificity).

Schlussfolgerungen:

Automated quantification of GWR in CT may be used as an objective observer-independent imaging marker for outcome in patients after cardiac arrest.