Can Optic Nerve Sheath Images on a Thin-Slice Brain Computed Tomography Reconstruction Predict the Neurological Outcomes in Cardiac Arrest Survivors?
Sung Ho KwonSang Hoon OhJinhee JangChun Song YounKyu Nam ParkChun-Song YounHan Joon KimJee Yong LimHyo Joon KimHyo Jin BangPublished in: Journal of clinical medicine (2022)
We analyzed the prognostic performance of optic nerve sheath diameter (ONSD) on thin-slice (0.6 mm) brain computed tomography (CT) reconstruction images as compared to routine-slice (4 mm) images. We conducted a retrospective analysis of brain CT images taken within 2 h after cardiac arrest. The maximal ONSD (mONSD) and optic nerve sheath area (ONSA) were measured on thin-slice images, and the routine ONSD (rONSD) and gray-to-white matter ratio (GWR) were measured on routine-slice images. We analyzed their area under the receiver operator characteristic curve (AUC) and the cutoff values for predicting a poor 6-month neurological outcome (a cerebral performance category score of 3-5). Of the 159 patients analyzed, 113 patients had a poor outcome. There was no significant difference in rONSD between the outcome groups ( p = 0.116). Compared to rONSD, mONSD (AUC 0.62, 95% CI: 0.54-0.70) and the ONSA (AUC 0.63, 95% CI: 0.55-0.70) showed better prognostic performance and had higher sensitivities to determine a poor outcome (mONSD, 20.4% [95% CI, 13.4-29.0]; ONSA, 16.8% [95% CI, 10.4-25.0]; rONSD, 7.1% [95% CI, 3.1-13.5]), with specificity of 95.7% (95% CI, 85.2-99.5). A combined cutoff value obtained by both the mONSD and GWR improved the sensitivity (31.0% [95% CI, 22.6-40.4]) of determining a poor outcome, while maintaining a high specificity. In conclusion, rONSD was clinically irrelevant, but the mONSD had an increased sensitivity in cutoff having acceptable specificity. Combination of the mONSD and GWR had an improved prognostic performance in these patients.
Keyphrases
- optic nerve
- computed tomography
- optical coherence tomography
- cardiac arrest
- end stage renal disease
- white matter
- deep learning
- image quality
- convolutional neural network
- ejection fraction
- newly diagnosed
- chronic kidney disease
- prognostic factors
- magnetic resonance imaging
- peritoneal dialysis
- type diabetes
- positron emission tomography
- blood pressure
- resting state
- clinical practice
- dual energy
- multiple sclerosis
- cardiopulmonary resuscitation
- brain injury
- patient reported outcomes
- subarachnoid hemorrhage
- blood brain barrier
- patient reported