Timing of brain computed tomography and accuracy of outcome prediction after cardiac arrest

Regional Brain Net Water Uptake in Computed Tomography after Cardiac Arrest - A Novel Biomarker for Neuroprognostication

BACKGROUND

Selective water uptake by neurons and glial cells and subsequent brain tissue oedema are key pathophysiological processes of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest (CA). Although brain computed tomography (CT) is widely used to assess the severity of HIE, changes of brain radiodensity over time have not been investigated. These could be used to quantify regional brain net water uptake (NWU), a potential prognostic biomarker.

METHODS

We conducted an observational prognostic accuracy study including a derivation (single center cardiac arrest registry) and a validation (international multicenter TTM2 trial) cohort. Early (<6 h) and follow-up (>24 h) head CTs of CA patients were used to determine regional NWU for grey and white matter regions after co-registration with a brain atlas. Neurological outcome was dichotomized as good versus poor using the Cerebral Performance Category Scale (CPC) in the derivation cohort and Modified Rankin Scale (mRS) in the validation cohort.

RESULTS

We included 115 patients (81 derivation, 34 validation) with out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA). Regional brain water content remained unchanged in patients with good outcome. In patients with poor neurological outcome, we found considerable regional water uptake with the strongest effect in the basal ganglia. NWU >8% in the putamen and caudate nucleus predicted poor outcome with 100% specificity (95%-CI: 86-100%) and 43% (moderate) sensitivity (95%-CI: 31-56%).

CONCLUSION

This pilot study indicates that NWU derived from serial head CTs is a promising novel biomarker for outcome prediction after CA. NWU >8% in basal ganglia grey matter regions predicted poor outcome while absence of NWU indicated good outcome. NWU and follow-up CTs should be investigated in larger, prospective trials with standardized CT acquisition protocols.

Neuromuscular blocking agents during targeted temperature management for out-of-hospital cardiac arrest patients

BACKGROUND

Neuromuscular blocking agents (NMBAs) can control shivering during targeted temperature management (TTM) of patients with cardiac arrest. However, the effectiveness of NMBA use during TTM on neurologic outcomes remains unclear. We aimed to evaluate the association between NMBA use during TTM and favorable neurologic outcomes after out-of-hospital cardiac arrest (OHCA).

MATERIALS AND METHODS

A multicenter, prospective, observational cohort study from 2019 to 2021. It included OHCA patients who received TTM after hospitalization. We conducted overlap weight propensity-score analyses after multiple imputation to evaluate the effect of NMBAs during TTM. The primary outcome was a favorable neurological outcome, defined as a cerebral performance category of 1 or 2 at discharge. Subgroup analyses were conducted based on initial monitored rhythm and brain computed tomography findings.

RESULTS

Of the 516 eligible patients, 337 received NMBAs during TTM. In crude analysis, the proportion of patients with favorable neurological outcome was significantly higher in the NMBA group (38.3% vs. 16.8%; risk difference (RD): 21.5%; 95% confidence interval (CI): 14.0% to 29.1%). In weighted analysis, a significantly higher proportion of patients in the NMBA group had a favorable neurological outcome compared to the non-NMBA group (32.7% vs. 20.9%; RD: 11.8%; 95% CI: 1.2% to 22.3%). In the subgroup with an initial shockable rhythm and no hypoxic encephalopathy, the NMBA group showed significantly higher proportions of favorable neurological outcomes.

CONCLUSIONS

The use of NMBAs during TTM was significantly associated with favorable neurologic outcomes at discharge for OHCA patients. NMBAs may have benefits in selected patients with initial shockable rhythm and without poor prognostic computed tomography findings.

Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study

PURPOSE

Application of standardised and automated assessments of head computed tomography (CT) for neuroprognostication after out-of-hospital cardiac arrest.

METHODS

Prospective, international, multicentre, observational study within the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Routine CTs from adult unconscious patients obtained > 48 h ≤ 7 days post-arrest were assessed qualitatively and quantitatively by seven international raters blinded to clinical information using a pre-published protocol. Grey-white-matter ratio (GWR) was calculated from four (GWR-4) and eight (GWR-8) regions of interest manually placed at the basal ganglia level. Additionally, GWR was obtained using an automated atlas-based approach. Prognostic accuracies for prediction of poor functional outcome (modified Rankin Scale 4-6) for the qualitative assessment and for the pre-defined GWR cutoff < 1.10 were calculated.

RESULTS

140 unconscious patients were included; median age was 68 years (interquartile range [IQR] 59-76), 76% were male, and 75% had poor outcome. Standardised qualitative assessment and all GWR models predicted poor outcome with 100% specificity (95% confidence interval [CI] 90-100). Sensitivity in median was 37% for the standardised qualitative assessment, 39% for GWR-8, 30% for GWR-4 and 41% for automated GWR. GWR-8 was superior to GWR-4 regarding prognostic accuracies, intra- and interrater agreement. Overall prognostic accuracy for automated GWR (area under the curve [AUC] 0.84, 95% CI 0.77-0.91) did not significantly differ from manually obtained GWR.

CONCLUSION

Standardised qualitative and quantitative assessments of CT are reliable and feasible methods to predict poor functional outcome after cardiac arrest. Automated GWR has the potential to make CT quantification for neuroprognostication accessible to all centres treating cardiac arrest patients.

Good outcome prediction after out-of-hospital cardiac arrest: A prospective multicenter observational study in Korea (the KORHN-PRO registry)

AIM

To assess the ability of clinical examination, biomarkers, electrophysiology and brain imaging, individually or in combination to predict good neurological outcomes at 6 months after CA.

METHODS

This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0, which included adult out-of-hospital cardiac arrest (OHCA) patients (≥18 years). Good outcome predictors were defined as both pupillary light reflex (PLR) and corneal reflex (CR) at admission, Glasgow Coma Scale Motor score (GCS-M) >3 at admission, neuron-specific enolase (NSE) <17 µg/L at 24-72 h, a median nerve somatosensory evoked potential (SSEP) N20/P25 amplitude >4 µV, continuous background without discharges on electroencephalogram (EEG), and absence of anoxic injury on brain CT and diffusion-weighted imaging (DWI).

RESULTS

A total of 1327 subjects were included in the final analysis, and their median age was 59 years; among them, 412 subjects had a good neurological outcome at 6 months. GCS-M >3 at admission had the highest specificity of 96.7% (95% CI 95.3-97.8), and normal brain DWI had the highest sensitivity of 96.3% (95% CI 92.9-98.4). When the two predictors were combined, the sensitivities tended to decrease (ranging from 2.7-81.1%), and the specificities tended to increase, ranging from81.3-100%. Through the explorative variation of the 2021 European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) prognostication strategy algorithms, good outcomes were predicted, with a specificity of 83.2% and a sensitivity of 83.5% in patients by the algorithm.

CONCLUSIONS

Clinical examination, biomarker, electrophysiology, and brain imaging predicted good outcomes at 6 months after CA. When the two predictors were combined, the specificity further improved. With the 2021 ERC/ESICM guidelines, the number of indeterminate patients and the uncertainty of prognostication can be reduced by using a good outcome prediction algorithm.

Brain computed tomography after resuscitation from in-hospital cardiac arrest

BACKGROUND

Few data characterize the role of brain computed tomography (CT) after resuscitation from in-hospital cardiac arrest (IHCA). We hypothesized that identifying a neurological etiology of arrest or cerebral edema on brain CT are less common after IHCA than after resuscitation from out-of-hospital cardiac arrest (OHCA).

METHODS

We included all patients comatose after resuscitation from IHCA or OHCA in this retrospective cohort analysis. We abstracted patient and arrest clinical characteristics, as well as pH and lactate, to estimate systemic illness severity. Brain CT characteristics included quantitative measurement of the grey-to-white ratio (GWR) at the level of the basal ganglia and qualitative assessment of sulcal and cisternal effacement. We compared GWR distribution by stratum (no edema ≥1.30, mild-to-moderate <1.30 and >1.20, severe ≤1.20) and newly identified neurological arrest etiology between IHCA and OHCA groups.

RESULTS

We included 2,306 subjects, of whom 420 (18.2%) suffered IHCA. Fewer IHCA subjects underwent post-arrest brain CT versus OHCA subjects (149 (35.5%) vs 1,555 (82.4%), p < 0.001). Cerebral edema for IHCA versus OHCA was more often absent (60.1% vs. 47.5%) or mild-to-moderate (34.3% vs. 27.9%) and less often severe (5.6% vs. 24.6%). A neurological etiology of arrest was identified on brain CT in 0.5% of IHCA versus 3.2% of OHCA.

CONCLUSIONS

Although severe edema was less frequent in IHCA relative to OHCA, mild-to-moderate or severe edema occurred in one in three patients after IHCA. Unsuspected neurological etiologies of arrest were rarely discovered by CT scan in IHCA patients.

Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images

BACKGROUND

This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes.

METHODS

Patients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations.

RESULTS

Overall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR.

CONCLUSIONS

Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

Feasibility of Magnetic Resonance-Based Conductivity Imaging as a Tool to Estimate the Severity of Hypoxic-Ischemic Brain Injury in the First Hours After Cardiac Arrest

BACKGROUND

Early identification of the severity of hypoxic-ischemic brain injury (HIBI) after cardiac arrest can be used to help plan appropriate subsequent therapy. We evaluated whether conductivity of cerebral tissue measured using magnetic resonance-based conductivity imaging (MRCI), which provides contrast derived from the concentration and mobility of ions within the imaged tissue, can reflect the severity of HIBI in the early hours after cardiac arrest.

METHODS

Fourteen minipigs were resuscitated after 5 min or 12 min of untreated cardiac arrest. MRCI was performed at baseline and at 1 h and 3.5 h after return of spontaneous circulation (ROSC).

RESULTS

In both groups, the conductivity of cerebral tissue significantly increased at 1 h after ROSC compared with that at baseline (P = 0.031 and 0.016 in the 5-min and 12-min groups, respectively). The increase was greater in the 12-min group, resulting in significantly higher conductivity values in the 12-min group (P = 0.030). At 3.5 h after ROSC, the conductivity of cerebral tissue in the 12-min group remained increased (P = 0.022), whereas that in the 5-min group returned to its baseline level.

CONCLUSIONS

The conductivity of cerebral tissue was increased in the first hours after ROSC, and the increase was more prominent and lasted longer in the 12-min group than in the 5-min group. Our findings suggest the promising potential of MRCI as a tool to estimate the severity of HIBI in the early hours after cardiac arrest.

Grey-to-White Matter Ratio Values in Early Head Computed Tomography (CT) as a Predictor of Neurologic Outcomes in Survivors of Out-of-Hospital Cardiac Arrest Based on Severity of Hypoxic-Ischemic Brain Injury

BACKGROUND

Hypoxic-ischemic brain injury (HIBI) is a common complication of out-of-hospital cardiac arrest (OHCA).

OBJECTIVES

We investigated whether grey-to-white matter ratio (GWR) values, measured using early head computed tomography (HCT), were associated with neurologic outcomes based on the severity of HIBI in survivors of OHCA.

METHODS

This retrospective multicenter study included adult comatose OHCA survivors who underwent an HCT scan within 2 h after the return of spontaneous circulation. HIBI severity was assessed using the revised post-Cardiac Arrest Syndrome for Therapeutic hypothermia (rCAST) scale (low, moderate, and severe). Poor neurologic outcomes were defined as Cerebral Performance Categories 3 to 5 at 6 months after OHCA.

RESULTS

Among 354 patients, 27% were women and 224 (63.3%) had poor neurologic outcomes. The distribution of severity was 19.5% low, 47.5% moderate, and 33.1% severe. The area under the receiver operating curves of the GWR values for predicting rCAST severity (low, moderate, and severe) were 0.52, 0.62, and 0.79, respectively. The severe group had significantly higher predictive performance than the moderate group (p = 0.02). Multivariate logistic regression analysis revealed a significant association between GWR values and poor neurologic outcomes in the moderate group (adjusted odds ratio = 0.012, 95% CI 0.0-0.54, p = 0.02).

CONCLUSIONS

In this cohort study, GWR values measured using early HCT demonstrated variations in predicting neurologic outcomes based on HIBI severity. Furthermore, GWR in the moderate group was associated with poor neurologic outcomes.

Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches

Objective

To establish a deep learning model for the detection of hypoxic-ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.

Methods

168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images).

Results

All optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping.

Conclusion

Our proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.

Machine learning-based predictor for neurologic outcomes in patients undergoing extracorporeal cardiopulmonary resuscitation

Background

We investigated the predictors of poor neurological outcomes in extracorporeal cardiopulmonary resuscitation (ECPR) patients using machine learning (ML) approaches.

Methods

This study was a retrospective, single-center, observational study that included adult patients who underwent ECPR while hospitalized between January 2010 and December 2020. The primary outcome was neurologic status at hospital discharge as assessed by the Cerebral Performance Categories (CPC) score (scores range from 1 to 5). We trained and tested eight ML algorithms for a binary classification task involving the neurological outcomes of survivors after ECPR.

Results

During the study period, 330 patients were finally enrolled in this analysis; 143 (43.3%) had favorable neurological outcomes (CPC score 1 and 2) but 187 (56.7%) did not. From the eight ML algorithms initially considered, we refined our analysis to focus on the three algorithms, eXtreme Gradient Boosting, random forest, and Stochastic Gradient Boosting, that exhibited the highest accuracy. eXtreme Gradient Boosting models exhibited the highest accuracy among all the machine learning algorithms (accuracy: 0.739, area under the curve: 0.837, Kappa: 0.450, sensitivity: 0.700, specificity: 0.740). Across all three ML models, mean blood pressure emerged as the most influential variable, followed by initial serum lactate, and arrest to extracorporeal membrane oxygenation (ECMO) pump-on-time as important predictors in machine learning models for poor neurological outcomes following successful ECPR.

Conclusions

In conclusion, machine learning methods showcased outstanding predictive accuracy for poor neurological outcomes in patients who underwent ECPR.

Quantitative analysis of early apparent diffusion coefficient values from MRIs for predicting neurological prognosis in survivors of out-of-hospital cardiac arrest: an observational study

BACKGROUND

This study aimed to quantitatively analyse ultra-early brain diffusion-weighted magnetic resonance imaging (DW-MRI) findings to determine the apparent diffusion coefficient (ADC) threshold associated with neurological outcomes in comatose survivors of out-of-hospital cardiac arrest (OHCA).

METHODS

This retrospective study included adult survivors of comatose OHCA who underwent DW-MRI imaging scans using a 3-T MRI scanner within 6 h of the return of spontaneous circulation (ROSC). We investigated the association between neurological outcomes and ADC values obtained through voxel-based analysis on DW-MRI. Additionally, we constructed multivariable logistic regression models with pupillary light reflex (PLR), serum neuron-specific enolase (NSE), and ADC values as independent variables to predict poor neurological outcomes. The primary outcome was poor neurological outcome 6 months after ROSC, determined by the Cerebral Performance Category 3-5.

RESULTS

Overall, 131 patients (26% female) were analysed, of whom 74 (57%) showed poor neurological outcomes. The group with a poor neurological outcome had lower mean whole brain ADC values (739.1 vs. 787.1 × 10-6 mm/s) and higher percentages of voxels with ADC below threshold in all ranges (250-1150) (all P < 0.001). The mean whole brain ADC values (area under the receiver operating characteristic curve [AUC] 0.83) and the percentage of voxels with ADC below 600 (AUC 0.81) had the highest sensitivity of 51% (95% confidence interval [CI] 39.4-63.1; cut-off value ≤ 739.2 × 10-6 mm2/s and > 17.2%, respectively) when the false positive rate (FPR) was 0%. In the multivariable model, which also included PLR, NSE, and mean whole brain ADC values, poor neurological outcome was predicted with the highest accuracy (AUC 0.91; 51% sensitivity). This model showed more accurate prediction and sensitivity at an FPR of 0% than did the combination of PLR and NSE (AUC 0.86; 30% sensitivity; P = 0.03).

CONCLUSIONS

In this cohort study, early voxel-based quantitative ADC analysis after ROSC was associated with poor neurological outcomes 6 months after cardiac arrest. The mean whole brain ADC value demonstrated the highest sensitivity when the FPR was 0%, and including it in the multivariable model improved the prediction of poor neurological outcomes.

Associations between clinical characteristics of cardiac arrest and early CT head findings of hypoxic ischaemic brain injury following out-of-hospital cardiac arrest

BACKGROUND/OBJECTIVE

Post-cardiac arrest patients are vulnerable to hypoxic-ischaemic brain injury (HIBI), but HIBI may not be identified until computed tomography (CT) scan of the brain is obtained post-resuscitation and stabilization. We aimed to evaluate the association of clinical arrest characteristics with early CT findings of HIBI to identify those at the highest risk for HIBI.

METHODS

This is a retrospective analysis of out-of-hospital cardiac arrest (OHCA) patients who underwent whole-body imaging. Head CT reports were analyzed with an emphasis on findings suggestive of HIBI; HIBI was present if any of the following were noted on the neuroradiologist read: global cerebral oedema, sulcal effacement, blurred grey-white junction, and ventricular compression. The primary exposure was duration of cardiac arrest. Secondary exposures included age, cardiac vs noncardiac etiology, and witnessed vs unwitnessed arrest. The primary outcome was CT findings of HIBI.

RESULTS

A total of 180 patients (average age 54 years, 32% female, 71% White, 53% witnessed arrest, 32% cardiac etiology of arrest, mean CPR duration of 15 ± 10 minutes) were included in this analysis. CT findings of HIBI were seen in 47 (48.3%) patients. Multivariate logistic regression demonstrated a significant association between CPR duration and HIBI (adjusted OR = 1.1, 95% CI 1.01-1.11, p < 0.01).

CONCLUSION

Signs of HIBI are commonly seen on CT head within 6 hours of OHCA, occurring in approximately half of patients, and are associated with CPR duration. Determining risk factors for abnormal CT findings can help clinically identify patients at higher risk for HIBI and target interventions appropriately.

Brain injury markers in blood predict signs of hypoxic ischaemic encephalopathy on head computed tomography after cardiac arrest

BACKGROUND/AIM

Signs of hypoxic ischaemic encephalopathy (HIE) on head computed tomography (CT) predicts poor neurological outcome after cardiac arrest. We explore whether levels of brain injury markers in blood could predict the likelihood of HIE on CT.

METHODS

Retrospective analysis of CT performed at 24-168 h post cardiac arrest on clinical indication within the Target Temperature Management after out-of-hospital cardiac arrest-trial. Biomarkers prospectively collected at 24- and 48 h post-arrest were analysed for neuron specific enolase (NSE), neurofilament light (NFL), total-tau and glial fibrillary acidic protein (GFAP). HIE was assessed through visual evaluation and quantitative grey-white-matter ratio (GWR) was retrospectively calculated on Swedish subjects with original images available.

RESULTS

In total, 95 patients were included. The performance to predict HIE on CT (performed at IQR 73-116 h) at 48 h was similar for all biomarkers, assessed as area under the receiving operating characteristic curve (AUC) NSE 0.82 (0.71-0.94), NFL 0.79 (0.67-0.91), total-tau 0.84 (0.74-0.95), GFAP 0.79 (0.67-0.90). The predictive performance of biomarker levels at 24 h was AUC 0.72-0.81. At 48 h biomarker levels below Youden Index accurately excluded HIE in 77.3-91.7% (negative predictive value) and levels above Youden Index correctly predicted HIE in 73.3-83.7% (positive predictive value). NSE cut-off at 48 h was 48 ng/ml. Elevated biomarker levels irrespective of timepoint significantly correlated with lower GWR.

CONCLUSION

Biomarker levels can assess the likelihood of a patient presenting with HIE on CT and could be used to select suitable patients for CT-examination during neurological prognostication in unconscious cardiac arrest patients.

Inter-rater agreement between humans and computer in quantitative assessment of computed tomography after cardiac arrest

Background

Head computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods.

Methods

Three blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication.

Results

Inter-rater agreement on GWR was very good (ICC 0.82–0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78–0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction.

Conclusion

Human and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.

Prognostic accuracy of head computed tomography for prediction of functional outcome after out-of-hospital cardiac arrest: Rationale and design of the prospective TTM2-CT-substudy

Background

Head computed tomography (CT) is a guideline recommended method to predict functional outcome after cardiac arrest (CA), but standardized criteria for evaluation are lacking. To date, no prospective trial has systematically validated methods for diagnosing hypoxic-ischaemic encephalopathy (HIE) on CT after CA. We present a protocol for validation of pre-specified radiological criteria for assessment of HIE on CT for neuroprognostication after CA.

Methods/design

This is a prospective observational international multicentre substudy of the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Patients still unconscious 48 hours post-arrest at 13 participating hospitals were routinely examined with CT. Original images will be evaluated by examiners blinded to clinical data using a standardized protocol. Qualitative assessment will include evaluation of absence/presence of "severe HIE". Radiodensities will be quantified in pre-specified regions of interest for calculation of grey-white matter ratios (GWR) at the basal ganglia level. Functional outcome will be dichotomized into good (modified Rankin Scale 0-3) and poor (modified Rankin Scale 4-6) at six months post-arrest. Prognostic accuracies for good and poor outcome will be presented as sensitivities and specificities with 95% confidence intervals (using pre-specified cut-offs for quantitative analysis), descriptive statistics (Area Under the Receiver Operating Characteristics Curve), inter- and intra-rater reliabilities according to STARD guidelines.

Conclusions

The results from this prospective trial will validate a standardized approach to radiological evaluations of HIE on CT for prediction of functional outcome in comatose CA patients.The TTM2 trial and the TTM2 CT substudy are registered at ClinicalTrials.gov NCT02908308 and NCT03913065.

A pilot study of methods for prediction of poor outcome by head computed tomography after cardiac arrest

INTRODUCTION

In Sweden, head computed tomography (CT) is commonly used for prediction of neurological outcome after cardiac arrest, as recommended by guidelines. We compare the prognostic ability and interrater variability of routine and novel CT methods for prediction of poor outcome.

METHODS

Retrospective study including patients from Swedish sites within the Target Temperature Management after out-of-hospital cardiac arrest trial examined with CT. Original images were assessed by two independent radiologists blinded from clinical data with eye-balling without pre-specified criteria, and with a semi-quantitative assessment. Grey-white-matter ratios (GWR) were quantified using models with 4-20 manually placed regions of interest. Prognostic abilities and interrater variability were calculated for prediction of poor outcome (modified Rankin Scale 4-6 at six months) for early (<24h) and late (≥24h) examinations.

RESULTS

68/106 (64%) of included patients were examined <24h post-arrest. Eye-balling predicted poor outcome with 89-100% specificity and 15-78% sensitivity. GWR <24h predicted neurological outcome with unsatisfactory to satisfactory Area Under the Receiver Operating Characteristics Curve (AUROC: 0.54-0.64). GWR ≥24h yielded very good to excellent AUROC (0.80-0.93). Sensitivities increased >2-3 fold in examinations performed after 24h compared to early examinations. Combining eye-balling with GWR<1.15 predicted poor outcome without false positives with sensitivities remaining acceptable.

CONCLUSION

In our cohort, qualitative and quantitative CT methods predicted poor outcome with high specificity and low to moderate sensitivity. Sensitivity increased relevantly after the first 24 hours after CA. Interrater variability poses a problem and indicates the need to standardise brain CT evaluation to increase the methodś safety.

Observational Trends in Publicly Reported Quality Measures of Hospital Outpatient Quality Reporting Program, 2013-2019

In 2011, the Centers for Medicare & Medicaid Services (CMS) implemented the Hospital Outpatient Quality Reporting Program to assess the quality of outpatient imaging efficiency (OIE). In this study, trends in hospital performance on these national hospital OIE measures a year after inception and public reporting were described. An observational trend analysis was conducted using 2013-2019 data from CMS 6 OIE measures. The trend analysis of metric scores indicates year-to-year variability in all 6 OIE variables. The reporting of these measures appears to have effectively improved the efficiency of most of the measures since the inception of the program.

Can Optic Nerve Sheath Images on a Thin-Slice Brain Computed Tomography Reconstruction Predict the Neurological Outcomes in Cardiac Arrest Survivors?

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.

Das ist neu in der Neurointensiv- und Notfallmedizin: die wichtigsten Studien des Jahres im Rück- und Überblick

Die vorliegende Übersichtsarbeit fasst wichtige klinische Studien der neurologischen Notfall- und Intensivmedizin zwischen 2020 und 2021 zusammen zu den Themen: rekanalisierende Therapie beim ischämischen Schlaganfall, Anwendbarkeit und Auswirkung eines zerebralen Sauerstoffgewebemonitorings bei Subarachnoidalblutung, Wirksamkeit induzierter Hypothermie bei Patienten mit „cardiac arrest“ (CA), Wertigkeit früher kranialer Bildgebung nach CA, Relevanz eines schnellen Managements und medikamentöser Therapie beim Status epilepticus sowie Inzidenz von Critical-illness-Polyneuropathie-Myopathie bei intensivpflichtigen COVID-Patienten.

Gray-White Matter Ratio at the Level of the Basal Ganglia as a Predictor of Neurologic Outcomes in Cardiac Arrest Survivors: A Literature Review

Loss of gray-white matter discrimination is the primary early imaging finding within of cranial computed tomography in cardiac arrest survivors, and this has been also regarded as a novel predictor for evaluating neurologic outcome. As displayed clearly on computed tomography and based on sensitivity to hypoxia, the gray-white matter ratio at basal ganglia (GWR-BG) region was frequently detected to assess the neurologic outcome by several studies. The specificity of GWR-BG is 72.4 to 100%, while the sensitivity is significantly different. Herein we review the mechanisms mediating cerebral edema following cardiac arrest, demonstrate the determination procedures with respect to GWR-BG, summarize the related researches regarding GWR-BG in predicting neurologic outcomes within cardiac arrest survivors, and discuss factors associated with predicting the accuracy of this methodology. Finally, we describe the effective measurements to increase the sensitivity of GWR-BG in predicting neurologic outcome.

Brain Injury Biomarkers for Predicting Outcome After Cardiac Arrest

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

Mapping the Unconscious Brain: Insights From Advanced Neuroimaging

SUMMARY

Recent advances in neuroimaging have been a preeminent factor in the scientific effort to unravel mechanisms of conscious awareness and the pathophysiology of disorders of consciousness. In the first part of this review, we selectively discuss operational models of consciousness, the biophysical signal that is measured using different imaging modalities, and knowledge on disorders of consciousness that has been gleaned with each neuroimaging modality. Techniques considered include diffusion-weighted imaging, diffusion tensor imaging, different types of nuclear medicine imaging, functional MRI, magnetoencephalography, and the combined transcranial magnetic stimulation-electroencephalography approach. In the second part of this article, we provide an overview of how advanced neuroimaging can be leveraged to support neurological prognostication, the use of machine learning to process high-dimensional imaging data, potential applications in clinical practice, and future directions.

GFAp and tau protein as predictors of neurological outcome after out-of-hospital cardiac arrest: A post hoc analysis of the COMACARE trial

Aim

To determine the ability of serum glial fibrillary acidic protein (GFAp) and tau protein to predict neurological outcome after out-of-hospital cardiac arrest (OHCA).

Methods

We measured plasma concentrations of GFAp and tau of patients included in the previously published COMACARE trial (NCT02698917) on intensive care unit admission and at 24, 48, and 72 h after OHCA, and compared them to neuron specific enolase (NSE). NSE concentrations were determined already during the original trial. We defined unfavourable outcome as a cerebral performance category (CPC) score of 3–5 six months after OHCA. We determined the prognostic accuracy of GFAp and tau using the receiver operating characteristic curve and area under the curve (AUROC).

Results

Overall, 39/112 (35%) patients had unfavourable outcomes. Over time, both markers were evidently higher in the unfavourable outcome group (p < 0.001). At 48 h, the median (interquartile range) GFAp concentration was 1514 (886–4995) in the unfavourable versus 238 (135–463) pg/ml in the favourable outcome group (p < 0.001). The corresponding tau concentrations were 99.6 (14.5–352) and 3.0 (2.2–4.8) pg/ml (p < 0.001). AUROCs at 48 and 72 h were 0.91 (95% confidence interval 0.85–0.97) and 0.91 (0.85–0.96) for GFAp and 0.93 (0.86–0.99) and 0.95 (0.89–1.00) for tau. Corresponding AUROCs for NSE were 0.86 (0.79–0.94) and 0.90 (0.82–0.97). The difference between the prognostic accuracies of GFAp or tau and NSE were not statistically significant.

Conclusions

At 48 and 72 h, serum both GFAp and tau demonstrated excellent accuracy in predicting outcomes after OHCA but were not superior to NSE.

Clinical trial registration

NCT02698917 (https://www.clinicaltrials.gov/ct2/show/NCT02698917).

Prognostication of neurologic outcome using gray-white-matter-ratio in comatose patients after cardiac arrest

Background

This study aimed to assess the prognostic value regarding neurologic outcome of CT neuroimaging based Gray-White-Matter-Ratio measurement in patients after resuscitation from cardiac arrest.

Methods

We retrospectively evaluated CT neuroimaging studies of 91 comatose patients resuscitated from cardiac arrest and 46 non-comatose controls. We tested the diagnostic performance of Gray-White-Matter-Ratio compared with established morphologic signs of hypoxic-ischaemic brain injury, e. g. loss of distinction between gray and white matter, and laboratory parameters, i. e. neuron-specific enolase, for the prediction of poor neurologic outcomes after resuscitated cardiac arrest. Primary endpoint was neurologic function assessed with cerebral performance category score 30 days after the index event.

Results

Gray-White-Matter-Ratio showed encouraging interobserver variability (ICC 0.670 [95% CI: 0.592–0.741] compared to assessment of established morphologic signs of hypoxic-ischaemic brain injury (Fleiss kappa 0.389 [95% CI: 0.320–0.457]) in CT neuroimaging studies. It correlated with cerebral performance category score with lower Gray-White-Matter-Ratios associated with unfavourable neurologic outcomes. A cut-off of 1.17 derived from the control population predicted unfavourable neurologic outcomes in adult survivors of cardiac arrest with 100% specificity, 50.3% sensitivity, 100% positive predictive value, and 39.3% negative predictive value. Gray-White-Matter-Ratio prognostic power depended on the time interval between circulatory arrest and CT imaging, with increasing sensitivity the later the image acquisition was executed.

Conclusions

A reduced Gray-White-Matter-Ratio is a highly specific prognostic marker of poor neurologic outcomes early after resuscitation from cardiac arrest. Sensitivity seems to be dependent on the time interval between circulatory arrest and image acquisition, with limited value within the first 12 h.

Brain injury after cardiac arrest: pathophysiology, treatment, and prognosis

Post-cardiac arrest brain injury (PCABI) is caused by initial ischaemia and subsequent reperfusion of the brain following resuscitation. In those who are admitted to intensive care unit after cardiac arrest, PCABI manifests as coma, and is the main cause of mortality and long-term disability. This review describes the mechanisms of PCABI, its treatment options, its outcomes, and the suggested strategies for outcome prediction.

Automated Assessment of Brain CT After Cardiac Arrest-An Observational Derivation/Validation Cohort Study

Objectives

Prognostication of outcome is an essential step in defining therapeutic goals after cardiac arrest. Gray-white-matter ratio obtained from brain CT can predict poor outcome. However, manual placement of regions of interest is a potential source of error and interrater variability. Our objective was to assess the performance of poor outcome prediction by automated quantification of changes in brain CTs after cardiac arrest.

Design

Observational, derivation/validation cohort study design. Outcome was determined using the Cerebral Performance Category upon hospital discharge. Poor outcome was defined as death or unresponsive wakefulness syndrome/coma. CTs were automatically decomposed using coregistration with a brain atlas.

Setting

ICUs at a large, academic hospital with circulatory arrest center.

Patients

We identified 433 cardiac arrest patients from a large previously established database with brain CTs within 10 days after cardiac arrest.

Interventions

None.

Measurements and Main Results

Five hundred sixteen brain CTs were evaluated (derivation cohort n = 309, validation cohort n = 207). Patients with poor outcome had significantly lower radiodensities in gray matter regions. Automated GWR_si (putamen/posterior limb of internal capsule) was performed with an area under the curve of 0.86 (95%-CI: 0.80-0.93) for CTs taken later than 24 hours after cardiac arrest (similar performance in the validation cohort). Poor outcome (Cerebral Performance Category 4-5) was predicted with a specificity of 100% (95% CI, 87-100%, derivation; 88-100%, validation) at a threshold of less than 1.10 and a sensitivity of 49% (95% CI, 36-58%, derivation) and 38% (95% CI, 27-50%, validation) for CTs later than 24 hours after cardiac arrest. Sensitivity and area under the curve were lower for CTs performed within 24 hours after cardiac arrest.

Conclusions

Automated gray-white-matter ratio from brain CT is a promising tool for prediction of poor neurologic outcome after cardiac arrest with high specificity and low-to-moderate sensitivity. Prediction by gray-white-matter ratio at the basal ganglia level performed best. Sensitivity increased considerably for CTs performed later than 24 hours after cardiac arrest.

Hypoxic-Ischemic Encephalopathy Evaluated by Brain Autopsy and Neuroprognostication After Cardiac Arrest

Importance

Neuroprognostication studies are potentially susceptible to a self-fulfilling prophecy as investigated prognostic parameters may affect withdrawal of life-sustaining therapy.

Objective

To compare the results of prognostic parameters after cardiac arrest (CA) with the histopathologically determined severity of hypoxic-ischemic encephalopathy (HIE) obtained from autopsy results.

Design, Setting, and Participants

In a retrospective, 3-center cohort study of all patients who died following cardiac arrest during their intensive care unit stay and underwent autopsy between 2003 and 2015, postmortem brain histopathologic findings were compared with post-CA brain computed tomographic imaging, electroencephalographic (EEG) findings, somatosensory-evoked potentials, and serum neuron-specific enolase levels obtained during the intensive care unit stay. Data analysis was conducted from 2015 to 2020.

Main Outcomes and Measures

The severity of HIE was evaluated according to the selective eosinophilic neuronal death (SEND) classification and patients were dichotomized into categories of histopathologically severe and no/mild HIE.

Results

Of 187 included patients, 117 were men (63%) and median age was 65 (interquartile range, 58-74) years. Severe HIE was found in 114 patients (61%) and no/mild HIE was identified in 73 patients (39%). Severe HIE was found in all 21 patients with bilaterally absent somatosensory-evoked potentials, all 15 patients with gray-white matter ratio less than 1.10 on brain computed tomographic imaging, all 9 patients with suppressed EEG, 15 of 16 patients with burst-suppression EEG, and all 29 patients with neuron-specific enolase levels greater than 67 μg/L more than 48 hours after CA without confounders. Three of 7 patients with generalized periodic discharges on suppressed background and 1 patient with burst-suppression EEG had a SEND 1 score (<30% dead neurons) in the cerebral cortex, but higher SEND scores (>30% dead neurons) in other oxygen-sensitive brain regions.

Conclusions and Relevance

In this study, histopathologic findings suggested severe HIE after cardiac arrest in patients with bilaterally absent cortical somatosensory-evoked potentials, gray-white matter ratio less than 1.10, highly malignant EEG, and serum neuron-specific enolase concentration greater than 67 μg/L.

Early head CT in post-cardiac arrest patients: A helpful tool or contributor to self-fulfilling prophecy?

OBJECTIVE

Neuroprognostication guidelines suggest that early head computed tomography (HCT) might be useful in the evaluation of cardiac arrest (CA) patients following return of spontaneous circulation. We aimed to determine the impact of early HCT, performed within the first 6 h following CA, on decision-making following resuscitation.

METHODS

We identified a cohort of initially unconscious post-CA patients at a tertiary care academic medical center from 2012 to 2017. Variables pertaining to demographics, CA details, post-CA care, including neuroimaging and neurophysiologic testing, were abstracted retrospectively from the electronic medical records. Changes in management resulting from HCT findings were recorded. Blinded board-certified neurointensivists adjudicated HCT findings related to hypoxic-ischemic brain injury (HIBI) burden. The gray-white matter ratio (GWR) was also calculated.

RESULTS

Of 302 patients, 182 (60.2%) underwent HCT within six hours of CA (early HCT group). Approximately 1 in 4 early HCTs were abnormal (most commonly HIBI changes; 78.7%, n = 37), which resulted in a change in management in nearly half of cases (46.8%, n = 22). The most common changes in management were de-escalation in care [including transition to do not resuscitate status), withholding targeted temperature management, and withdrawal of life sustaining therapy (WLST)]. In cases with radiographic HIBI, mean [standard deviation] GWR was lower (1.20 [0.10] vs 1.30 [0.09], P < 0.001) and progression to brain death was higher (44.4% vs 2.9%; P < 0.001). The inter-rater reliability (IRR) of early HCT to determine presence of HIBI between radiology and three neurointensivists had a wide range (κ 0.13-0.66).

CONCLUSION

Early HCT identified abnormalities in 25% of cases and frequently influenced therapeutic decisions. Neuroimaging interpretation discrepancies between radiology and neurointensivists are common and agreement on severity of HIBI on early HCT is poor (k 0.11).

Predictive values of early head computed tomography for survival outcome after cardiac arrest in childhood: a pilot study

Predicting outcomes of children after cardiac arrest (CA) remains challenging. To identify useful prognostic markers for pediatric CA, we retrospectively analyzed the early findings of head computed tomography (CT) of patients. Subjects were non-traumatic, out-of-hospital CA patients < 16 years of age who underwent the first head CT within 24 h in our institute from 2006 to 2018 (n = 70, median age: 4 months, range 0–163). Of the 24 patients with return of spontaneous circulation, 14 survived up to 30 days after CA. The degree of brain damage was quantitatively measured with modified methods of the Alberta Stroke Program Early CT Score (mASPECTS) and simplified gray-matter-attenuation-to-white-matter-attenuation ratio (sGWR). The 14 survivors showed higher mASPECTS values than the 56 non-survivors (p = 0.035). All 3 patients with mASPECTS scores ≥ 20 survived, while an sGWR ≥ 1.14 indicated a higher chance of survival than an sGWR < 1.14 (54.5% vs. 13.6%). Follow-up magnetic resonance imaging for survivors validated the correlation of the mASPECTS < 15 with severe brain damage. Thus, low mASPECTS scores were associated with unfavorable neurological outcomes on the Pediatric Cerebral Performance Category scale. A quantitative analysis of early head CT findings might provide clues for predicting survival of pediatric CA.

The first national survey on practices of neurological prognostication after cardiac arrest in China, still a lot to do

AIMS

To investigate current awareness and practices of neurological prognostication in comatose cardiac arrest (CA) patients.

METHODS

An anonymous questionnaire was distributed to 1600 emergency physicians in 75 hospitals which were selected randomly from China between January and July 2018.

RESULTS

92.1% respondents fulfilled the survey. The predictive value of brain stem reflex, motor response and myoclonus was confirmed by 63.5%, 44.6% and 31.7% respondents, respectively. Only 30.7% knew that GWR value < 1.1 indicated poor prognosis and only 8.1% know the most commonly used SSEP N20. Status epilepticus, burst suppression and suppression were considered to predict poor outcome by only 35.0%, 27.4% and 20.9% respondents, respectively. Only 46.7% knew NSE and only 24.7% knew S-100. Only a few respondents knew that neurological prognostication should be performed later than 72 hours from CA either in TTM or non-TTM patients. In practice, the most commonly used method was clinical examination (85.4%). Only 67.9% had used brain CT for prognosis and 18.4% for MRI. NSE (39.6%) was a little more widely used than S-100β (18.0%). However, SSEP (4.4%) and EEG (11.4%) were occasionally performed.

CONCLUSIONS

Neurological prognostication in CA survivors had not been well understood and performed by emergency physicians in China. They were more likely to use clinical examination rather than objective tools, especially SSEP and EEG, which also illustrated that multimodal approach was not well performed in practice.

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

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 (I² = 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.

Utility of Abnormal Head Computed Tomography in Predicting Outcome in Out-of-Hospital Cardiac Arrest Victims

Head computed tomography (HCT) is often performed postcardiac arrest to assess for hypoxic-ischemic brain injury. Our primary objective was to assess whether cerebral edema (CE) on early HCT is associated with poor survival and neurologic outcome after out-of-hospital cardiac arrest (OHCA).We included subjects from a prospectively collected database of OHCA adults who received targeted temperature management at two hospitals from July 2009 to July 2018. We included cases if an emergency department (ED) HCT was performed. Patient demographics and cardiac arrest variables were collected. HCT results were abstracted from radiology reports. HCT findings were categorized as no acute disease, evidence of CE, or excluded (bleed, tumor, and stroke). Outcomes were survival to discharge or dichotomized discharge cerebral performance category (CPC) of 1-2 (good neurologic outcome) versus 3-5 (poor neurologic outcome). Univariate and multivariate analyses were performed. There were 425 OHCA, of which 315 had ED HCT with 277 cases included. Patients were predominately male (65.0%), average age of 60.9 years and average body mass index of 30.5. Of all cases, 44 (15.9%) showed CE on computed tomography. Univariate analysis demonstrated that CE was associated with 9.2-fold greater odds of poor outcome (odds ratio [OR]: 9.23; 95% confidence interval [CI] 1.73-49.2) and 9.1-fold greater odds of death (OR: 9.09, 95% CI 2.4-33.9). In adjusted analysis, CE was associated with a poor CPC outcome (adjusted odds ratios [AOR]: 14.9, 95% CI 2.49-88.4), and death (AOR: 13.7, 95% CI 3.26-57.4). Adjusted survival analysis demonstrated that patients with CE on HCT had 3.6-fold greater hazard of death than those without CE (hazard ratios 3.56, 95% CI 2.34-5.41). The results identify that CE on HCTs early in the postarrest period in OHCA patients is strongly associated with poor rates of survival and neurologic outcome. Prospective work is needed to further define the role of early HCT in postarrest neuroprognostication.

Postcardiac arrest neurological prognostication with quantitative regional cerebral densitometry

PURPOSE

To quantitatively assess the severity of anoxic-ischemic brain injury early after cardiac arrest (CA) using a novel automated method applied to head computed tomography (HCT).

METHODS

Adult patients who were comatose and underwent HCT < 24 h after arrest were included in a retrospective analysis. Principal endpoint was unfavorable outcome (UO) defined as Cerebral Performance Category (CPC) of 3-5 at hospital discharge. We developed an automated processing algorithm for HCT images to be registered, atlas-segmented in 181 regions, and region-specific radiologic densities determined in Hounsfield Units. This approach was compared with an established manual method evaluating grey-white matter ratios (GWR). We tested univariable and multivariable prognostic models which integrated clinical and HCT features including densities in lobes and in nodes of cerebral networks linked to CA recovery.

RESULTS

Ninety-one patients were enrolled among whom 66 (73%) had an UO. HCTs were interpreted as normal or without acute abnormality by a neuroradiologist in 77 cases (85%). Compared to the favorable outcome group, UO patients had significantly lower densities in all lobes and in nodes of cerebral networks. A model combining clinical variables with the automated method applied to cerebral network nodes had the highest prognostic performance although not significantly different than the combined clinical-GWR method (AUC [95% CI] 0.94 [0.86-1.00] and 0.92 [0.83-1.00] respectively).

CONCLUSION

In comatose survivors of CA, automated quantitative analysis of HCT revealed very early multifocal changes in brain tissue density which are mostly overlooked on conventional neuroradiologic interpretation and are associated with neurological outcome.

Duration and clinical features of cardiac arrest predict early severe cerebral edema

BACKGROUND

Severe brain edema appears early after cardiopulmonary resuscitation (CPR) in a subset of patients and portends a poor prognosis. We tested whether clinical features of patients or resuscitation during out-of-hospital cardiac arrest (OHCA) are associated with early, severe cerebral edema.

METHOD/RESEARCH DESIGN

We reviewed pre-hospital and hospital records for comatose patients surviving to hospital admission after OHCA who had computed tomography (CT) of brain at the time of hospital admission available for inspection. We measured the gray-white ratio (GWR) of X-ray attenuation between the caudate nucleus and posterior limb of the internal capsule, defining severe cerebral edema as GWR < 1.20. We calculated associations between severe cerebral edema and patient or resuscitation variables.

RESULTS

Between 2010 and 2019, 1340 subjects were admitted of whom 296 (22%) showed severe cerebral edema on initial CT. Subjects with severe edema had lower survival (5/296, 2% vs. 377/1044, 36%). Severe edema was independently associated with total CPR duration, total dose of epinephrine, younger age, non-shockable arrest rhythms, fewer total number of rescue shocks, rearrest after initial return of pulses, and non-cardiac arrest etiology. Prevalence of severe cerebral edema increased from 2% among subjects with 0-10 min of CPR to 31% among subjects with >40 min of CPR.

CONCLUSION

CPR duration along with easily measurable clinical and resuscitation characteristics predict early severe cerebral edema after OHCA. Future interventional trials should consider targeting or preventing cerebral edema after prolonged hypoxic-ischemic brain injury especially in patients with high risk clinical features.

Performance of a guideline-recommended algorithm for prognostication of poor neurological outcome after cardiac arrest

Purpose

To assess the performance of a 4-step algorithm for neurological prognostication after cardiac arrest recommended by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM).

Methods

Retrospective descriptive analysis with data from the Target Temperature Management (TTM) Trial. Associations between predicted and actual neurological outcome were investigated for each step of the algorithm with results from clinical neurological examinations, neuroradiology (CT or MRI), neurophysiology (EEG and SSEP) and serum neuron-specific enolase. Patients examined with Glasgow Coma Scale Motor Score (GCS-M) on day 4 (72–96 h) post-arrest and available 6-month outcome were included. Poor outcome was defined as Cerebral Performance Category 3–5. Variations of the ERC/ESICM algorithm were explored within the same cohort.

Results

The ERC/ESICM algorithm identified poor outcome patients with 38.7% sensitivity (95% CI 33.1–44.7) and 100% specificity (95% CI 98.8–100) in a cohort of 585 patients. An alternative cut-off for serum neuron-specific enolase, an alternative EEG-classification and variations of the GCS-M had minor effects on the sensitivity without causing false positive predictions. The highest overall sensitivity, 42.5% (95% CI 36.7–48.5), was achieved when prognosticating patients irrespective of GCS-M score, with 100% specificity (95% CI 98.8–100) remaining.

Conclusion

The ERC/ESICM algorithm and all exploratory multimodal variations thereof investigated in this study predicted poor outcome without false positive predictions and with sensitivities 34.6–42.5%. Our results should be validated prospectively, preferably in patients where withdrawal of life-sustaining therapy is uncommon to exclude any confounding from self-fulfilling prophecies.

Electronic supplementary material

The online version of this article (10.1007/s00134-020-06080-9) contains supplementary material, which is available to authorized users.