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

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.

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).

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).

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.