Abstract
Purpose: Predicting neurologic prognosis in pediatric patients recovered after cardiac arrest is more difficult than in adults. This study hypothesized that a combination model of early electroencephalography, brain computed tomography (CT), and laboratory findings improve prediction performance of neurologic outcome in pediatric patients after cardiac arrest. Methods: We retrospectively analyzed the medical records of pediatric patients resuscitated after non-traumatic cardiac arrest. Clinical features, electroencephalography, gray matter to white matter attenuation ratio on brain CT, and laboratory findings were analyzed. The primary outcome was neurologic prognosis based on the Pediatric Cerebral Performance Category score. Results: Of 21 patients, seven (33.3%) were classified as a good neurologic outcome group and 14 (66.7%) were classified as a poor neurologic outcome group. The good outcome group was associated with a slow and disorganized electroencephalographic background pattern (P = 0.006), reactivity (P = 0.006), and electrographic seizures (P = 0.03). The frequency of a suppressed electroencephalographic background pattern was significantly higher in the poor outcome group (P = 0.006). The poor outcome group was also associated with a low level of gray matter to white matter attenuation ratio (P = 0.03) and hyperammonemia (P = 0.003). The area under curve of the combined model, consisting of electroencephalographic background, gray matter to white matter attenuation ratio, and ammonia was the highest at 0.959 (0.772-0.999) with a specificity of 100%. Conclusion: Unfavorable electroencephalographic background, low gray matter to white matter attenuation ratio on brain CT, and hyperammonemia are associated with poor neurologic outcome in children after cardiac arrest.