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Updated: Oct 31, 2025

Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy
Published on: October 28, 2022
Predictive Value of Gray-Matter-White-Matter Ratio on Brain Computed Tomography for Delayed Encephalopathy after
Shu Li Wang1, Meng Mei Ma1,2, Guang Wei Lv1
1Emergency Department, Cangzhou Central Hospital, No. 16 Xinhua Road, Yunhe Qu, Cangzhou City 061000, China.
Background:
This study is aimed at determining the predictive value of the gray-matter-white-matter ratio (GWR) on brain computed tomography for delayed encephalopathy after acute carbon monoxide (CO) poisoning (DEACMP).
Methods:
This retrospective cohort study reviewed 352 patients with acute CO poisoning and who underwent the brain computed tomography test. These patients were admitted to Cangzhou Central Hospital from May 2010 to May 2020. The patients were divided into the DEACMP (n = 16) and non-DEACMP (n = 336) groups. Pearson's correlation coefficients were computed for correlation analysis. The predictive value of GWR for DEACMP was evaluated by using logistic regression analysis and receiver operator characteristic curves.
Results:
The morbidity of DEACMP was 4.5% (16/352). The GWR-basal ganglia, GWR-cerebrum, and GWR-average in the DEACMP group were lower than those in the non-DEACMP group. Correlation analysis indicated that GWR-basal ganglia (r = 0.276; P < 0.001), GWR-cerebrum (r = 0.163; P = 0.002), and GWR-average (r = 0.200; P < 0.001) were correlated with DEACMP. Multivariate logistic regression analysis revealed that reduced GWR-basal ganglia, GWR-cerebrum, and GWR-average were independent risk factors (P < 0.001; P = 0.008; P = 0.001; respectively). Compared with GWR-cerebrum and GWR-average, GWR-basal ganglia had a higher area under the curve of 0.881 (95% confidence interval: 0.783-0.983) with sensitivity and specificity of 93.8% and 68.7%, respectively. The cut-off value of GWR-basal ganglia was 1.055.
Conclusion:
GWR, especially GWR-basal ganglia, is an early useful predictor for DEACMP.
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