scholarly journals Prognostic Values of the Gray-to-White Matter Ratio on Brain Computed Tomography Images for Neurological Outcomes after Cardiac Arrest: A Meta-Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wen Jie Wang ◽  
Jie Cui ◽  
Guang Wei Lv ◽  
Shun Yi Feng ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cardiac Arrest ◽  
White Matter ◽  
Basal Ganglion ◽  
Prognostic Value ◽  
Meta Analysis ◽  
Brain Computed Tomography ◽  
Brain Ct ◽  
Neurological Outcomes ◽  
Computed Tomography Images

Background and Purpose. The gray-to-white matter ratio (GWR) on brain computed tomography (CT) is associated with neurological outcomes after cardiac arrest (CA); however, the prognostic value of GWR in CA patients has yet to be confirmed. Therefore, we conducted a meta-analysis of related studies to investigate the prognostic value of GWR on brain CT for neurological outcomes after CA. Materials and Methods. The PubMed, ScienceDirect, Web of Science, and China National Knowledge Infrastructure databases were searched for all relevant articles published before March 31, 2020, without any language restrictions. The pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated with a random-effects model using Stata 14.0 software. Result. A total of 24 eligible studies with 2812 CA patients were recruited in the meta-analysis. The pooled result showed that decreased GWR was correlated with poor neurological outcomes after CA ( OR = 11.28 , 95% CI: 6.29–20.21, and P < 0.001 ) with moderate heterogeneity ( I 2 = 71.5 % , P < 0.001 ). The pooled sensitivity and specificity were 0.58 (95% CI: 0.47–0.68) and 0.95 (95% CI: 0.87–0.98), respectively. The area under the curve (AUC) of GWR was 0.84 (95% CI: 0.80–0.87). Compared with GWR (cerebrum) and GWR (average), GWR using the basal ganglion level of brain CT had the highest AUC of 0.87 (0.84–0.90). Subgroup analysis indicated that heterogeneity may be derived from the time of CT measurement, preset specificity, targeted temperature management, or proportion of cardiac etiology. Sensitivity analysis indicated that the result was stable, and Deeks’ plot showed no possible publication bias ( P = 0   .64 ). Conclusion. Current research suggests that GWR, especially using the basal ganglion level of brain CT, is a useful parameter for determining neurological outcomes after CA.

scholarly journals The prognostic value of gray–white matter ratio on brain computed tomography in adult comatose cardiac arrest survivors

2018 ◽  
Vol 81 (7) ◽  
pp. 599-604 ◽  
Author(s):  
Gan-Nan Wang ◽  
Xu-Feng Chen ◽  
Jin-Ru Lv ◽  
Na-Na Sun ◽  
Xiao-Quan Xu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cardiac Arrest ◽  
White Matter ◽  
Prognostic Value ◽  
Brain Computed Tomography

scholarly journals Multimodal Long-Term Predictors of Outcome in Out of Hospital Cardiac Arrest Patients Treated with Targeted Temperature Management at 36 °C

2021 ◽  
Vol 10 (6) ◽  
pp. 1331
Author(s):  
Erik Roman-Pognuz ◽  
Jonathan Elmer ◽  
Frank X. Guyette ◽  
Gabriele Poillucci ◽  
Umberto Lucangelo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cardiac Arrest ◽  
White Matter ◽  
Temperature Management ◽  
Single Test ◽  
Brain Computed Tomography ◽  
Good Outcome ◽  
Eeg Reactivity ◽  
Single Predictor

Introduction: Early prediction of long-term outcomes in patients resuscitated after cardiac arrest (CA) is still challenging. Guidelines suggested a multimodal approach combining multiple predictors. We evaluated whether the combination of the electroencephalography (EEG) reactivity, somatosensory evoked potentials (SSEPs) cortical complex and Gray to White matter ratio (GWR) on brain computed tomography (CT) at different temperatures could predict survival and good outcome at hospital discharge and six months after the event. Methods: We performed a retrospective cohort study including consecutive adult, non-traumatic patients resuscitated from out-of-hospital CA who remained comatose on admission to our intensive care unit from 2013 to 2017. We acquired SSEPs and EEGs during the treatment at 36 °C and after rewarming at 37 °C, Gray to white matter ratio (GWR) was calculated on the brain computed tomography scan performed within six hours of the hospital admission. We primarily hypothesized that SSEP was associated with favor-able functional outcome at distance and secondarily that SSEP provides independent information from EEG and CT. Outcomes were evaluated using the Cerebral Performance Category (CPC) scale at six months from discharge. Results: Of 171 resuscitated patients, 75 were excluded due to missing data or uninterpretable neurophysiological findings. EEG reactivity at 37 °C has been shown the best single predictor of good out-come (AUC 0.803) while N20P25 was the best single predictor for survival at each time point. (AUC 0.775 at discharge and AUC 0.747 at six months follow up). The predictive value of a model including EEG reactivity, average GWR, and SSEP N20P25 amplitude was superior (AUC 0.841 for survival and 0.920 for good out-come) to any combination of two tests or any single test. Conclusions: Our study, in which life-sustaining treatments were never suspended, suggests SSEP cortical complex N20P25, after normothermia and off sedation, is a reliable predictor for survival at any time. When SSEP cortical complex N20P25 is added into a model with GWR average and EEG reactivity, the predictivity for good outcome and survival at distance is superior than each single test alone.

scholarly journals Multimodal Long Term Predictors of Outcome in Out of Hospital Cardiac Arrest Patients Treated with Targeted Temperature Management at 36°C

2021 ◽  
Author(s):  
Erik Roman-Pognuz ◽  
Jonathan Elmer ◽  
Frank X Guyette ◽  
gabriele poillucci ◽  
umberto lucangelo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cardiac Arrest ◽  
White Matter ◽  
Temperature Management ◽  
Single Test ◽  
Brain Computed Tomography ◽  
Good Outcome ◽  
Eeg Reactivity ◽  
Single Predictor

Introduction Early prediction of long term outcomes in patients resuscitated after cardiac arrest (CA) is still challenging. Guidelines suggested a multimodal approach combining multiple predictors. We evaluated whether the combination of the electroencephalography (EEG) reactivity, somatosensory evoked potentials (SSEPs) cortical complex and Gray to White matter ratio (GWR) on brain computed tomography (CT) at different temperatures could predict survival and good outcome at hospital discharge and after six months. Methods We performed a retrospective cohort study including consecutive adult, non-traumatic patients resuscitated from out-of-hospital CA who remained comatose on admission to our intensive care unit from 2013 to 2017. We acquired SSEPs and EEGs during the treatment at 36&deg;C and after rewarming at 37&deg;C, Gray to white matter ratio (GWR) was calculated on the brain computed tomography scan performed within six hours of the hospital admission. We primarily hypothesized that SSEP was associated with favorable functional outcome at distance and secondarily that SSEP provides independent information from EEG and CT. Outcomes were evaluated using the Cerebral Performance Category (CPC) scale at six months from discharge. Results Of 171 resuscitated patients, 75 were excluded due to missing of data or uninterpretable neurophysiological findings. EEG reactivity at 37 &deg;C has been shown the best single predictor of good outcome (AUC 0.803) while N20P25 was the best single predictor for survival at each time point. (AUC 0.775 at discharge and AUC 0.747 at six months follow up) Predictive value of a model including EEG reactivity, average GWR, and SSEP N20P25 amplitude was superior (AUC 0.841 for survival and 0.920 for good outcome) to any combination of two tests or any single test. Conclusion Our study, in which life-sustaining treatments were never suspended, suggests SSEP cortical complex N20P25, after normothermia ad off sedation, is a reliable predictor for survival at any time. When SSEP cortical complex N20P25 is added into a model with GWR average and EEG reactivity, the predictivity for good outcome and survival at distance is superior than each single test alone.

scholarly journals Prognostic Value of Muscle Mass Measured via Brain Computed Tomography in Neurocritically Ill Patients

2020 ◽  
Author(s):  
Yun Im Lee ◽  
Ryoung-Eun Ko ◽  
Joonghyun Ahn ◽  
Keumhee C. Carriere ◽  
Jeong-Am Ryu
Keyword(s):  
Computed Tomography ◽  
Skeletal Muscle ◽  
Muscle Mass ◽  
Neurological Outcome ◽  
Skeletal Muscle Mass ◽  
Brain Computed Tomography ◽  
Brain Ct ◽  
Neurological Outcomes ◽  
Poor Neurological Outcome

Abstract Background To investigate whether skeletal muscle mass estimated via brain computed tomography (CT) can be used to predict neurological outcomes in neurocritically ill patients. Methods This is a retrospective, observational study. Adult patients who were admitted to the neurosurgical intensive care unit (ICU) in tertiary hospital from January 2010 to September 2019 were eligible. We included patients who were hospitalized in the neurosurgical ICU for more than 7 days. Cross-sectional areas of paravertebral muscle at the first cervical vertebra level (C1-CSA) and temporalis muscle thickness (TMT) on brain CT were measured to evaluate skeletal muscle mass. Primary outcome was Glasgow Outcome Scale score at 3 months. Results Among 189 patients, 167 (88.4%) survived until discharge from the hospital. Of these survivors, 81 (42.9%) patients had favorable neurologic outcomes. Initial TMT values and follow-up TMT values were higher in patients with favorable neurologic outcome compared to those with poor neurological outcome (p = 0.003 and p = 0.001, respectively). Initial the C1-CSA/body surface area was greater in patients with poor neurological outcome than in those with favorable outcome (p = 0.029). In multivariable analysis, age (adjusted odds ratio [OR]: 2.05, 95% confidence interval [CI]: 1.543–2.724), BMI (adjusted OR: 0.74, 95% CI: 0.638–0.849), use of mannitol (adjusted OR: 27.45, 95% CI: 4.833–155.860), change of C1-CSA (adjusted OR: 1.36, 95% CI: 1.054–1.761), and change of TMT (adjusted OR: 1.27, 95% CI: 1.028–1.576) were significantly associated with poor neurological outcome (Hosmer–Lemeshow test, Chi-square = 11.4, df = 8, p = 0.178) with the areas under curve of 0.803 (95% CI 0.740–0.866) using 10-fold cross validation method. Especially, the risk of poor neurologic outcome was proportional to changes of C1-CSA and TMT. Conclusions In this study, the follow-up skeletal muscle mass at first week from ICU admission, based on changes in C1-CSA and TMT, was associated with neurological prognosis in neurocritically ill patients. Eventually, brain CT-measured sarcopenia may be helpful in predicting poor neurological outcomes in these patients.

Sign in / Sign up

Export Citation Format

Share Document