Prognostic value of ¹⁸F-FDG brain PET as an early indicator of neurological outcome in a rat cardiac arrest model

Purpose: Accurate prediction of a good neurological outcome in post-arrest comatose patients is important. In this study, we evaluated the feasibility of brain glucose metabolism measured on ¹⁸F-FDG PET for the prediction of neurological outcome in a cardiac arrest and resuscitation rat model. Methods: Cardiac arrest and resuscitation (CAR) was induced using Sprague Dawley rats. Baseline and post 3-hour ¹⁸F-FDG brain PET images were acquired. Standardized uptake value ratio (SUVR) was calculated by dividing regional SUV with whole brain SUV. To evaluate neurologic outcome of CAR, the Morris water maze (MWM) test was performed after 2-week. Rats were classified as mild vs. severe deficit group based on the MWM result, and the differences in SUVR between the groups were compared. Results: Of 18 CAR rats, 8 were classified as mild deficit group, while 10 were classified as severe deficit group. On baseline PET, regional SUVR of mild deficit group showed no significant differences compared with severe group. Post 3-hour PET showed different regional distribution between groups; mild deficit group showed significantly higher SUVR of fore brain regions (e.g. frontal association cortex, cingulate cortex, medial prefrontal cortex, motor cortex, orbitofrontal cortex, parietal association cortex, retro-splenial cortex, somatosensory cortex, and visual cortex; all p<0.05) compared with severe deficit group, while significantly lower SUVR of hindbrain regions (e.g. pons and medulla; all p<0.05). Forebrain-to-hindbrain ratio predicted the development of severe neurologic deficit with a sensitivity of 90% and specificity of 100% with an optimal cutoff value of 1.22 (AUC 0.969, p<0.05). Conclusions: The results suggest the potential usefulness of ¹⁸F-FDG brain PET in early prediction of long-term neurological outcome in cardiac arrest-induced brain injury. [asterisk][asterisk]This work was supported by the National Research Foundation (NRF) funded by the Ministry of Education of Korea (2017R1D1A1B03035305, Dae Hee Kim; 2018R1D1A1B07049400, Hai-Jeon Yoon).