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.

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.

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.

Outcomes After Decompressive Surgery for Severe Cerebral Venous Sinus Thrombosis Associated or Not Associated with Vaccine-Induced Immune Thrombosis with Thrombocytopenia: A Multicenter Cohort Study.

BACKGROUND: Clinical observations indicated that vaccine-induced immune thrombosis with thrombocytopenia (VITT)-associated cerebral venous sinus thrombosis (CVST) often has a space-occupying effect and thus necessitates decompressive surgery (DS). While comparing with non-VITT CVST, this study explored whether VITT-associated CVST exhibits a more fulminant clinical course, different perioperative and intensive care unit management, and worse long-term outcome. METHODS: This multicenter, retrospective cohort study collected patient data from 12 tertiary centers to address priorly formulated hypotheses concerning the clinical course, the perioperative management with related complications, extracerebral complications, and the functional outcome (modified Rankin Scale) in patients with VITT-associated and non-VITT CVST, both with DS. RESULTS: Both groups, each with 16 patients, were balanced regarding demographics, kind of clinical symptoms, and radiological findings at hospital admission. Severity of neurological symptoms, assessed with the National Institute of Health Stroke Scale, was similar between groups at admission and before surgery, whereas more patients with VITT-associated CVST showed a relevant midline shift (≥ 4 mm) before surgery (100% vs. 68.8%, p = 0.043). Patients with VITT-associated CVST tended to undergo DS early, i.e., ≤ 24 h after hospital admission (p = 0.077). Patients with VITT-associated CVST more frequently received platelet transfusion, tranexamic acid, and fibrinogen perioperatively. The postoperative management was comparable, and complications were evenly distributed. More patients with VITT-associated CVST achieved a favorable outcome (modified Rankin Scale ≤ 3) at 3 months (p = 0.043). CONCLUSIONS: Although the prediction of individual courses remains challenging, DS should be considered early in VITT-associated CVST because an overall favorable outcome appears achievable in these patients.

Confounders for prognostic accuracy of neuron-specific enolase after cardiac arrest: A retrospective cohort study.

AIM: To evaluate neuron-specific enolase (NSE) thresholds for prediction of neurological outcome after cardiac arrest and to analyze the influence of hemolysis and confounders. METHODS: Retrospective analysis from a cardiac arrest registry. Determination of NSE serum concentration and hemolysis-index (h-index) 48-96 hours after cardiac arrest. Evaluation of neurological outcome using the Cerebral Performance Category score (CPC) at hospital discharge. Separate analyses considering CPC 1-3 and CPC 1-2 as good neurological outcome. Analysis of specificity and sensitivity for poor and good neurological outcome prediction with and without exclusion of hemolytic samples (h-index larger than 50). RESULTS: Among 356 survivors three days after cardiac arrest, hemolysis was detected in 28 samples (7.9%). At a threshold of 60 µg/L, NSE predicted poor neurological outcome (CPC 4-5) in all samples with a specificity of 92% (86-95%) and sensitivity of 73% (66-79%). In non-hemolytic samples, specificity was 94% (89-97%) and sensitivity 70% (62-76%). At a threshold of 100 µg/L, specificity was 98% (95-100%, all samples) and 99% (95-100%, non-hemolytic samples), and sensitivity 58% (51-65%) and 55% (47-63%), respectively. Possible confounders for elevated NSE in patients with good neurological outcome were ECMO, malignancies, blood transfusions and acute brain diseases. Nine patients with NSE below 17 µg/L had CPC 5, all had plausible death causes other than hypoxic-ischemic encephalopathy. CONCLUSIONS: NSE concentrations higher than 100 µg/L predicted poor neurological outcome with high specificity. An NSE less than 17 µg/L indicated absence of severe hypoxic-ischemic encephalopathy. Hemolysis and other confounders need to be considered. INSTITUTIONAL PROTOCOL NUMBER: The local ethics committee (board name: Ethikkommission der Charité) approved this study by the number: EA2/066/23, approval date: 28th June 2023, study title "'ROSC' - Resuscitation Outcome Study."

Cortical somatosensory evoked potential amplitudes and clinical outcome after cardiac arrest: a retrospective multicenter study.

OBJECTIVE: Bilaterally absent cortical somatosensory evoked potentials (SSEPs) reliably predict poor outcome in comatose cardiac arrest (CA) patients. Cortical SSEP amplitudes are a recent prognostic extension; however, amplitude thresholds, inter-recording, and inter-rater agreement remain uncertain. METHODS: In a retrospective multicenter cohort study, we determined cortical SSEP amplitudes of comatose CA patients using a standardized evaluation pathway. We studied inter-recording agreement in repeated SSEPs and inter-rater agreement by four raters independently determining 100 cortical SSEP amplitudes. Primary outcome was assessed using the cerebral performance category (CPC) upon intensive care unit discharge dichotomized into good (CPC 1-3) and poor outcome (CPC 4-5). RESULTS: Of 706 patients with SSEPs with median 3 days after CA, 277 (39.2%) had good and 429 (60.8%) poor outcome. Of patients with bilaterally absent cortical SSEPs, one (0.8%) survived with CPC 3 and 130 (99.2%) had poor outcome. Otherwise, the lowest cortical SSEP amplitude in good outcome patients was 0.5 µV. 184 (42.9%) of 429 poor outcome patients had lower cortical SSEP amplitudes. In 106 repeated SSEPs, there were 6 (5.7%) with prognostication-relevant changes in SSEP categories. Following a standardized evaluation pathway, inter-rater agreement was almost perfect with a Fleiss' kappa of 0.88. INTERPRETATION: Bilaterally absent and cortical SSEP amplitudes below 0.5 µV predicted poor outcome with high specificity. A standardized evaluation pathway provided high inter-rater and inter-recording agreement. Regain of consciousness in patients with bilaterally absent cortical SSEPs rarely occurs. High-amplitude cortical SSEP amplitudes likely indicate the absence of severe brain injury.

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.

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.

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.

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

AIM: Gray-white-matter ratio (GWR) calculated from head CT is a radiologic index of tissue changes associated with hypoxic-ischemic encephalopathy after cardiac arrest (CA). Evidence from previous studies indicates high specificity for poor outcome prediction at GWR thresholds of 1.10-1.20. We aimed to determine the relationship between accuracy of neurologic prognostication by GWR and timing of CT. METHODS: We included 195 patients admitted to the ICU following CA. GWR was calculated from CT radiologic densities in 16 regions of interest. Outcome was determined upon intensive care unit discharge using the cerebral performance category (CPC). Accuracy of outcome prediction of GWR was compared for 3 epochs (<6, 6-24, and >24 h after CA). RESULTS: 125 (64%) patients had poor (CPC4-5) and 70 (36%) good outcome (CPC1-3). Irrespective of timing, specificity for poor outcome prediction was 100% at a GWR threshold of 1.10. Among 50 patients with both early and late CT, GWR decreased significantly over time (p = 0.002) in patients with poor outcome, sensitivity for poor outcome prediction was 12% (7-20%) with early CTs (<6 h) and 48% (38-58%) for late CTs (>24 h). Across all patients, sensitivity of early and late CT was 17% (9-28%) and 39% (28-51%), respectively. CONCLUSION: A GWR below 1.10 predicts poor outcome (CPC4-5) in patients after CA with high specificity irrespective of time of acquisition of CT. Because GWR decreases over time in patients with severe HIE, sensitivity for prediction of poor outcome is higher for late CTs (>24 h after CA) as compared to early CTs (<6 h after CA).