Multimodal assessment using early brain CT and blood pH improve prediction of neurologic outcomes after pediatric cardiac arrest.

Abstract

Early prediction of neurologic prognosis in children resuscitated from cardiac arrest is a major challenge. This study aimed to investigate the usefulness of a combined model based on brain computed tomography (CT) and initial blood gas analysis to predict neurologic prognoses in pediatric patients after cardiac arrest. We retrospectively analyzed the medical records of patients resuscitated after cardiac arrest from 2000 to 2018. Patients aged one month to 18 years were included. Gray to white matter ratio (GWR), ambient cistern effacement (ACE), and blood gas analysis were studied. The primary outcome was neurological prognosis, which was evaluated using the Pediatric Cerebral Performance Category (PCPC) scale at discharge. Of 97 resuscitated patients, 64 brain CT images were available. Fourteen patients had a good neurologic outcome (PCPC 1-3) and 50 patients a poor neurologic outcome (PCPC 4-6). The multimodal model (AUC 0.897) containing GWR of basal ganglia (BG), ACE, and blood pH was found to be superior for predicting poor neurologic prognosis than single variable models (AUC of GWR-BG: 0.744, ACE: 0.804, pH: 0.747). Interestingly, we found the GWR-BG cutoff value for specificity 100% differed significantly between patients <4 years (cutoff value: 1.08, p = 0.04) and ≥4 years (cutoff value: 1.18, p = 0.004). The combination of GWR-BG, ambient cistern effacement, and blood pH was found to usefully predict neurological outcome in children resuscitated from cardiac arrest. In addition, the cutoff value of GWR-BG for the prediction of neurologic outcome was found to increase with age.