Does Combining Biomarkers and Brain Images Provide Improved Prognostic Predictive Performance for Out-Of-Hospital Cardiac Arrest Survivors before Target Temperature Management?
Seung Ha SonIn Ho LeeJung-Soo ParkIn Sool YooSeung Whan KimJin Woong LeeSeung RyuYeonho YouJin Hong MinYong Chul ChoWon Joon JeongSe Kwang OhSung Uk ChoHong Joon AhnChangshin KangDong Hoon LeeByung Kook LeeChun Song YounPublished in: Journal of clinical medicine (2020)
We examined whether combining biomarkers measurements and brain images early after the return of spontaneous circulation improves prognostic performance compared with the use of either biomarkers or brain images for patients with cardiac arrest following target temperature management (TTM). This retrospective observational study involved comatose out-of-hospital cardiac arrest survivors. We analyzed neuron-specific enolase levels in serum (NSE) or cerebrospinal fluid (CSF), grey-to-white matter ratio by brain computed tomography, presence of high signal intensity (HSI) in diffusion-weighted imaging (DWI), and voxel-based apparent diffusion coefficient (ADC). Of the 58 patients, 33 (56.9%) had poor neurologic outcomes. CSF NSE levels showed better prognostic performance (area under the curve (AUC) 0.873, 95% confidence interval (CI) 0.749-0.950) than serum NSE levels (AUC 0.792, 95% CI 0.644-0.888). HSI in DWI showed the best prognostic performance (AUC 0.833, 95% CI 0.711-0.919). Combining CSF NSE levels and HSI in DWI had better prognostic performance (AUC 0.925, 95% CI 0.813-0.981) than each individual method, followed by the combination of serum NSE levels and HSI on DWI and that of CSF NSE levels and the percentage of voxels of ADC (AUC 0.901, 95% CI 0.792-0.965; AUC 0.849, 95% CI 0.717-0.935, respectively). Combining CSF/serum NSE levels and HSI in DWI before TTM improved the prognostic performance compared to either each individual method or other combinations.
Keyphrases
- diffusion weighted imaging
- white matter
- cardiac arrest
- contrast enhanced
- magnetic resonance imaging
- computed tomography
- cerebrospinal fluid
- diffusion weighted
- deep learning
- resting state
- optical coherence tomography
- young adults
- convolutional neural network
- multiple sclerosis
- type diabetes
- magnetic resonance
- adipose tissue
- cardiopulmonary resuscitation
- subarachnoid hemorrhage