Automated assessment of early hypoxic brain edema in non-enhanced CT predicts outcome in patients after cardiac arrest

The link between carotid artery stenosis and outcomes in patients with refractory out-of-hospital cardiac arrest

BACKGROUND

Mortality of out-of-hospital cardiac arrest (OHCA) remains high. Extracorporeal cardiopulmonary resuscitation (ECPR) has revolutionized OHCA treatment, but our understanding of the ECPR responder's clinical profile is incomplete. Carotid artery stenosis (CAS) is a well-established cardiovascular disease risk factor. The impact of CAS on OHCA outcomes remains unelucidated.

OBJECTIVE

To assess whether CAS burden affects the outcomes of OHCA patients treated with ECPR.

METHODS

This study included patients with OHCA admitted for ECPR consideration, who had carotid ultrasonography performed. A numeric scale was applied to the plaque to create a CAS burden numeric scale. The primary outcome of the study was survival at discharge, compared among the different degrees of CAS. Neurologically intact survival and surrogate markers of neurologic injury were the secondary study endpoints. To assess the independent effect of CAS burden on survival to hospital discharge, we conducted a logistic regression analysis.

RESULTS

Between 2019 and 2023, carotid ultrasonography was performed on 163 patients who were admitted for refractory OHCA. CAS burden was equally distributed between the right and left carotid arteries. Logistic regression analysis indicated that the CAS burden was significantly associated with both overall and neurologically intact survival at discharge (p = 0.004). A linear relationship between the CAS burden and neuron-specific and S-100 levels was identified. Patients with normal carotids were significantly less likely to have encephalopathy on electroencephalograms.

CONCLUSION

CAS burden independently predicts the risk for worse survival and neurologic outcomes in patients suffering refractory OHCA who are treated with ECPR.

Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest

BACKGROUND AND OBJECTIVES

In light of limited intensive care capacities and a lack of accurate prognostic tools to advise caregivers and family members responsibly, this study aims to determine whether automated cerebral CT (CCT) analysis allows prognostication after out-of-hospital cardiac arrest.

METHODS

In this monocentric, retrospective cohort study, a supervised machine learning classifier based on an elastic net regularized logistic regression model for gray matter alterations on nonenhanced CCT obtained after cardiac arrest was trained using 10-fold cross-validation and tested on a hold-out sample (random split 75%/25%) for outcome prediction. Following the literature, a favorable outcome was defined as a cerebral performance category of 1-2 and a poor outcome of 3-5. The diagnostic accuracy was compared with established and guideline-recommended prognostic measures within the sample, that is, gray matter-white matter ratio (GWR), neuron-specific enolase (NSE), and neurofilament light chain (NfL) in serum.

RESULTS

Of 279 adult patients, 132 who underwent CCT within 14 days of cardiac arrest with good imaging quality were identified. Our approach discriminated between favorable and poor outcomes with an area under the curve (AUC) of 0.73 (95% CI 0.59-0.82). Thus, the prognostic power outperformed the GWR (AUC 0.66, 95% CI 0.56-0.76). The biomarkers NfL, measured at days 1 and 2, and NSE, measured at day 2, exceeded the reliability of the imaging markers derived from CT (AUC NfL day 1: 0.87, 95% CI 0.75-0.99; AUC NfL day 2: 0.90, 95% CI 0.79-1.00; AUC NSE day: 2 0.78, 95% CI 0.62-0.94).

DISCUSSION

Our data show that machine learning-assisted gray matter analysis of CCT images offers prognostic information after out-of-hospital cardiac arrest. Thus, CCT gray matter analysis could become a reliable and time-independent addition to the standard workup with serum biomarkers sampled at predefined time points. Prospective studies are warranted to replicate these 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.

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.

Association between quantitative analysis of cerebral edema using CT imaging and neurological outcomes in cardiac arrest survivors

BACKGROUND

To determine if the density distribution proportion of Hounsfield unit (HUdp) in head computed tomography (HCT) images can be used to quantitatively measure cerebral edema in survivors of out-of-hospital cardiac arrest (OHCA).

METHODS

This retrospective observational study included adult comatose OHCA survivors who underwent HCT within 6 h (first) and 72-96 h (second), all performed using the same CT scanner. Semi-automated quantitative analysis was used to identify differences in HUdp at specific HU ranges across the intracranial component based on neurological outcome. Cerebral edema was defined as the increased displacement of the sum of HUdp values (ΔHUdp) at a specific range between two HCT scans. Poor neurological outcome was defined as cerebral performance categories 3-5 at 6 months after OHCA.

RESULTS

Twenty-three (42%) out of 55 patients had poor neurological outcome. Significant HUdp differences were observed between good and poor neurological outcomes in the second HCT scan at HU = 1-14, 23-35, and 39-56 (all P < 0.05). Only the ΔHUdp = 23-35 range showed a significant increase and correlation in the poor neurological outcome group (4.90 vs. -0.72, P < 0.001) with the sum of decreases in the other two ranges (r = 0.97, P < 0.001). Multivariate logistic regression analysis demonstrated a significant association between ΔHUdp = 23-35 range and poor neurological outcomes (adjusted OR, 1.12; 95% CI: 1.02-1.24; P = 0.02).

CONCLUSION

In this cohort study, the increased displacement in ΔHUdp = 23-35 range is independently associated with poor neurological outcome and provides a quantitative assessment of cerebral edema formation in OHCA survivors.

Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest

BACKGROUND

Grey-white ratio (GWR) can estimate severity of cytotoxic cerebral edema secondary to hypoxic-ischemic brain injury after cardiac arrest and predict progression to death by neurologic criteria (DNC). Current approaches to calculating GWR are not standardized and have variable interrater reliability. We tested if measures of variance of brain density on early computed tomographic (CT) imaging after cardiac arrest could predict DNC.

METHODS

We performed a retrospective cohort study, identifying post-arrest patients treated between 2011 and 2020 at our single center. We extracted demographic data from our registry and Digital Imaging and Communication in Medicine (DICOM) files for each patient's first brain CT. We analyzed slices 15-20 of each DICOM, corresponding to the level of the basal ganglia while accommodating differences in patient anatomy. We extracted pixel arrays and converted the radiodensities to Hounsfield units (HU). To focus on brain tissue densities, we excluded HU > 60 and < 10. We calculated the variance of each patient's HU distribution and the difference between the means of a two-group Gaussian finite mixture model. We compared these novel metrics to existing measures of cerebral edema, then randomly divided our data into 80% training and 20% test sets and used logistic regression to predict DNC.

RESULTS

Of 1,133 included subjects, 457 (40%) were female, mean (standard deviation) age was 58 (16) years, and 115 (10%) progressed to DNC. CTs were obtained a median [interquartile range] of 4.2 [2.8-5.7] hours post-arrest. Our novel measures correlated weakly with GWR. HU variance, but not difference between mixture model means, differed significantly between subjects with and without sulcal or cistern effacement. GWR outperformed our novel measures in predicting progression to DNC with an area under the receiver operating characteristic curve (AUC) of 0.82, compared to HU variance (AUC = 0.73) and the difference between mixture model means (AUC = 0.56).

CONCLUSION

There are differences in the distribution of HU on post-arrest CT in patients with qualitative measures of cerebral edema. Current methods to quantify cerebral edema outperform simple measures of attenuation variance on early brain CT. Further analyses could investigate if these measures of variance, or other distributional characteristics of brain density, have improved predictive performance on brain CTs obtained later in the clinical course or derived from discrete regions of anatomical interest.

Is gray-white matter ratio in out-of-hospital cardiac arrest patients' really early predictor of neurological outcome?

OBJECTIVE

This study aimed to evaluate the association between neurological outcome and gray-white ratio (GWR) in brain computed tomography (CT) in patients with return of spontaneous circulation (ROSC) who were brought to the emergency department (ED) due to out-of-hospital cardiac arrest (OHCA).

METHODS

This study has a retrospective design. Patients with ROSC who were brought to the ED due to OHCA and who underwent brain CT in the first 24 h were included in the study. Demographic data, brain CT results (intensities of gray matter and white matter in Hounsfield units and calculated GWR), and hospital outcome were recorded. The cerebral Performance Categories (CPC) score was used as the outcome of the study.

RESULTS

A total of 160 patients were included in the study. 55% of the patients were male and the median age was 75.5. The median brain CT time of the patients was 120 min. 16.3% of the patients were in the good neurological outcome group. When attenuation values and GWRs of the patients were compared according to CPC of patients (good-poor), no statistically significant difference was detected in any parameter except MC2 attenuation (P > 0.05 for all values). The patients were separated into groups geriatric and nongeriatric and GWRs were compared. GWRs were lower in the geriatric groups (P < 0.05 for all values).

CONCLUSION

Although it is emphasized in the literature that detection of low GWR in brain CT can help the clinical decision process in patients surviving comatose arrest, we think that it is not valid for especially in geriatric patients and in patients who underwent early brain CT after ROSC.

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.

Early risk stratification for progression to death by neurological criteria following out-of-hospital cardiac arrest

BACKGROUND

Some patients resuscitated from out-of-hospital cardiac arrest (OHCA) progress to death by neurological criteria (DNC). We hypothesized that initial brain imaging, electroencephalography (EEG), and arrest characteristics predict progression to DNC.

METHODS

We identified comatose OHCA patients from January 2010 to February 2020 treated at a single quaternary care facility in Western Pennsylvania. We abstracted demographics and arrest characteristics; Pittsburgh Cardiac Arrest Category, initial motor exam and pupillary light reflex; initial brain computed tomography (CT) grey-to-white ratio (GWR), sulcal or basal cistern effacement; initial EEG background and suppression ratio. We used two modeling approaches: fast and frugal tree (FFT) analysis to create an interpretable clinical risk stratification tool and ridge regression for comparison. We used bootstrapping to randomly partition cases into 80% training and 20% test sets and evaluated test set sensitivity and specificity.

RESULTS

We included 1,569 patients, of whom 147 (9%) had diagnosed DNC. Across bootstrap samples, >99% of FFTs included three predictors: sulcal effacement, and in cases without sulcal effacement, the combination of EEG background suppression and GWR ≤ 1.23. This tree had mean sensitivity and specificity of 87% and 81%. Ridge regression with all available predictors had mean sensitivity 91 % and mean specificity 83%. Subjects falsely predicted as likely to progress to DNC generally died of rearrest or withdrawal of life sustaining therapies due to poor neurological prognosis. Two of these cases awakened from coma during the index hospitalization.

CONCLUSIONS

Sulcal effacement on presenting brain CT or EEG suppression with GWR ≤ 1.23 predict progression to DNC after OHCA.

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.

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.

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.

Head CT: Toward Making Full Use of the Information the X-Rays Give

Although clinical head CT images are typically interpreted qualitatively, automated methods applied to routine clinical head CTs enable quantitative assessment of brain volume, brain parenchymal fraction, brain radiodensity, and brain radiomass. These metrics gain clinical meaning when viewed relative to a reference database and expressed as quantile regression values. Quantitative imaging data can aid in objective reporting and in the identification of outliers, with possible diagnostic implications. The comparison to a reference database necessitates standardization of head CT imaging parameters and protocols. Future research is needed to learn the effects of virtual monochromatic imaging on the quantitative characteristics of head CT images.

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.

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.

Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study

Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models. Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI. Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1-3 vs. 4-5). In-hospital mortality, length of stay (>1 week), and need for surgery were further evaluated as alternative TBI outcome measures. Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72-0.94), in-hospital mortality = 0.91 (95% CI: 0.82-1.00), length of stay = 0.83 (95% CI: 0.72-0.94), and need for surgery = 0.71 (95% CI: 0.56-0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model. Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.

The association between post-cardiac arrest cerebral oxygenation and survival with favorable neurological outcomes: A multicenter study

OBJECTIVE

Cerebral oximetry is a non-invasive system that uses near infrared spectroscopy to measure regional cerebral oxygenation (rSO₂) in the frontal lobe of the brain. Post-cardiac arrest rSO₂ may be associated with survival and neurological outcomes in out-of-hospital cardiac arrest patients; however, no studies have examined relationships between rSO₂ and neurological outcomes following in-hospital cardiac arrest (IHCA). We tested the hypothesis that rSO₂ following IHCA is associated with survival and favorable neurological outcomes.

DESIGN

Prospective study from nine acute care hospital in the United States and United Kingdom.

PATIENTS

Convenience sample of IHCA patients admitted to the intensive care unit with post-cardiac arrest syndrome.

INTERVENTIONS

Cerebral oximetry monitoring (Equanox 7600, Nonin Medical, MN, USA) during the first 48 h after IHCA.

MEASUREMENTS AND MAIN RESULTS

Subject's rSO₂ was calculated as the mean of collected data at different time intervals: hourly between 1-6 h, 6-12 h, 12-18 h, 18-24 h and 24-48 h. Demographic data pertaining to possible confounding variables for rSO₂ and primary outcome were collected. The primary outcome was survival with favorable neurological outcomes (cerebral performance scale [CPC] 1-2) vs severe neurological injury or death (CPC 3-5) at hospital discharge. Univariate and multivariate statistical analyses were performed to correlate cerebral oximetry values and other variables with the primary outcome. Among 87 studied patients, 26 (29.9%) achieved CPC 1-2. A significant difference in mean rSO₂ was observed during hours 1-2 after IHCA in CPC 1-2 vs CPC 3-5 (73.08 vs. 66.59, p = 0.031) but not at other time intervals. There were no differences in age, Charlson comorbidity index, APACHE II scores, CPR duration, mean arterial pressure, PaO₂, PaCO₂, and hemoglobin levels between two groups.

CONCLUSIONS

There may be a significant physiological difference in rSO₂ in the first two hours after ROSC in IHCA patients who achieve favorable neurological outcomes, however, this difference may not be clinically significant.

The Use of Gray-White-Matter Ratios May Help Predict Survival and Neurological Outcomes in Patients Resuscitated From Out-of-Hospital Cardiac Arrest

BACKGROUND

The gray-white-matter ratio (GWR) measured on brain computed tomography (CT) following return of spontaneous circulation (ROSC) has been reported to be helpful in the prognostication of mortality or comatose status of cardiac arrest victims. However, whether the use of GWR in predicting the outcomes in out-of-hospital cardiac arrest (OHCA) survivors in Taiwan population remains uninvestigated.

METHODS

This retrospective observational study conducted in a single tertiary medical center in Taiwan enrolled all the non-traumatic OHCA adults (> 18 years old) with sustained ROSC (≥ 20 minutes) during the period from 2006 to 2014. Patients with following exclusion criteria were further excluded: no brain CT within 24 hours following ROSC; the presence of intracranial hemorrhage, severe old insult, brain tumor, ventriculoperitoneal shunt, and severe image artifact. The GWR values were obtained from the density measurement of bilateral putamen, caudate nuclei, posterior limbs of internal capsule, corpus callosum, medial cortex and medial white matter of cerebrum in Hounsfield unit with region of interest of 0.11 cm2, and further compared between the patients who survived to hospital discharge or not and the patients with and without good neurological outcome (good: cerebral performance category [CPC] of 1-2, poor: CPC of 3-5), respectively.

RESULTS

A total of 228 patients were included in the final analysis with 59.2% in male gender and mean age of 65.8-year-old. There were 106 patients (46.5%) survived to hospital discharge and 40 patients (17.5%) discharged with good neurological outcomes. The GWR values of patients who survived to hospital discharge was significantly higher than ones of those who failed (e.g. basal ganglion: 1.239 vs. 1.199, p < 0.001). Patients with good neurological outcome also had higher GWR values than those with poor outcome (e.g. basal ganglion: 1.243 vs. 1.208, p = 0.010). The Area Under Curve of Receiver of Characteristic curve demonstrated fair predicting ability of GWR for survival and neurological outcomes.

CONCLUSION

The use of GWR measured on bran CT within 24 hours following ROSC can help in predicting survival-to-hospital discharge and neurological outcome in OHCA survivors.

How can neurological outcomes be predicted in comatose pediatric patients after out-of-hospital cardiac arrest?

The prognosis of patients who are comatose after resuscitation remains uncertain. The accurate prediction of neurological outcome is important for management decisions and counseling. A neurological examination is an important factor for prognostication, but widely used sedatives alter the neurological examination and delay the response recovery. Additional studies including electroencephalography, somatosensory-evoked potentials, brain imaging, and blood biomarkers are useful for evaluating the extent of brain injury. This review aimed to assess the usefulness of and provide practical prognostic strategy for pediatric postresuscitation patients. The principles of prognostication are that the assessment should be delayed until at least 72 hours after cardiac arrest and the assessment should be multimodal. Furthermore, multiple factors including unmeasured confounders in individual patients should be considered when applying the prognostication strategy.

Imaging for Neuroprognostication After Cardiac Arrest: Systematic Review and Meta-analysis

BACKGROUND

Predicting neurological outcome in comatose survivors of cardiac arrest relies on clinical findings, radiological and neurophysiological test results. To evaluate the predictive accuracy of brain computed tomography (CT) and magnetic resonance imaging (MRI) for prognostication of neurological outcomes after cardiac arrest.

METHODS

We searched MEDLINE (database inception to August 2018) and included all observational cohort studies or randomized controlled trials including adult (16 years or older) survivors of cardiac arrest which evaluated the diagnostic accuracy of CT or MRI for predicting neurologic outcome or mortality. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. All review stages were conducted independently by 2 reviewers, and where possible data were pooled using bivariate meta-analysis. The main outcome was to evaluate the of accuracy of CT and MRI in neuroprognostication of patients after cardiac arrest.

RESULTS

We included 44 studies that examined brain CT (n = 24) or MRI (n = 21) in 4008 (n per study, 9-398) patients. Decreased grey to white matter ratio on CT (20 studies) was useful for predicting poor neurological outcome (sensitivity 0.44, 95% CI 0.29-0.60; specificity 0.97, 95% CI 0.93-0.99; positive likelihood ratio [LR+] 13.8, 95% CI 6.9-27.7). Similarly, diffusion-weighted imaging (DWI) on MRI (16 studies; sensitivity 0.77, 95% CI 0.65-0.85; specificity 0.92, 95% CI 0.85-0.96; LR+ 9.2, 95% CI 5.2-16.4) and DWI and fluid-attenuated inversion recovery (FLAIR) MRI (4 studies, sensitivity 0.70, 95% CI 0.43-0.88; specificity 0.95, 95% CI 0.79-0.99; LR+ 13.4, 95% CI 3.5-51.2) were useful for predicting poor neurological outcomes. We found marked heterogeneity in timing of radiological examinations and neurological assessments relative to the cardiac arrest.

CONCLUSION

Decreased grey to white matter ratio on CT and DWI or DWI and FLAIR on MRI are useful adjuncts for predicting poor early neurological outcome after cardiac arrest.

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).

The aquaporin-4 inhibitor AER-271 blocks acute cerebral edema and improves early outcome in a pediatric model of asphyxial cardiac arrest

BACKGROUND

Cerebral edema after cardiac arrest (CA) is associated with increased mortality and unfavorable outcome in children and adults. Aquaporin-4 mediates cerebral water movement and its absence in models of ischemia improves outcome. We investigated early and selective pharmacologic inhibition of aquaporin-4 in a clinically relevant asphyxial CA model in immature rats in a threshold CA insult that produces primarily cytotoxic edema in the absence of blood-brain barrier permeability.

METHODS

Postnatal day 16-18 Sprague-Dawley rats were studied in our established 9-min asphyxial CA model. Rats were randomized to aquaporin-4 inhibitor (AER-271) vs vehicle treatment, initiated at return of spontaneous circulation. Cerebral edema (% brain water) was the primary outcome with secondary assessments of the Neurologic Deficit Score (NDS), hippocampal neuronal death, and neuroinflammation.

RESULTS

Treatment with AER-271 ameliorated early cerebral edema measured at 3 h after CA vs vehicle treated rats. This treatment also attenuated early NDS. In contrast to rats treated with vehicle after CA, rats treated with AER-271 did not develop significant neuronal death or neuroinflammation as compared to sham.

CONCLUSION

Early post-resuscitation aquaporin-4 inhibition blocks the development of early cerebral edema, reduces early neurologic deficit, and blunts neuronal death and neuroinflammation post-CA.

Gray matter to white matter ratio for predicting neurological outcomes in patients treated with target temperature management after cardiac arrest: A systematic review and meta-analysis

AIMS

This study aimed to evaluate the prognostic accuracy of the gray matter to white matter ratio (GWR) in predicting neurological outcomes in post-cardiac arrest patients treated with target temperature management.

DATA SOURCES

We systematically searched MEDLINE and EMBASE (Search date: 09/13/2017). Included studies were those evaluating neurological outcomes using the cerebral performance categories scale. We performed a subgroup analysis based on the location of the measurement. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess the risk of bias.

RESULTS

In total, 1150 patients from 10 observational studies were included. GWR of the basal ganglia (BG) average showed the highest value (area under the curve [AUC] 0.96, SE 0.02, Q 0.90) compared with the putamen/posterior limb of internal capsule (AUC 0.93, SE 0.05, Q 0.87), overall average (AUC 0.91, SE 0.02, Q 0.85), and cerebrum (AUC 0.89, SE 0.05, Q 0.82) for prognostic accuracy. Furthermore, the highest pooled diagnostic odd ratio of GWR for predicting poor neurological outcomes was shown for the BG average (21.00, 95% CI 6.85-64.40) followed by the overall average (20.71, 95% CI 9.53-44.98), putamen/posterior limb of internal capsule (16.08, 95% CI 4.36-59.23), and cerebrum (13.96, 95% CI 4.26-45.76).

CONCLUSIONS

GWR in the early cranial computed tomography scan had high prognostic value in predicting poor neurological outcomes in post-cardiac arrest patients. The BG GWR had the highest prognostic accuracy when compared to other locations of the brain.

Head computed tomography for prognostication of poor outcome in comatose patients after cardiac arrest and targeted temperature management

INTRODUCTION

A multimodal approach to prognostication of outcome after cardiac arrest (CA) is recommended. Evidence for combinations of methods is low. In this post-hoc analysis we described findings on head computed tomography (CT) after CA. We also examined whether generalised oedema on CT alone or together with the biomarker Neuron-specific enolase (NSE) could predict poor outcome.

METHODS

Patients participating in the Target Temperature Management after out-of-hospital-cardiac-arrest-trial underwent CT based on clinical indications. Findings were divided into pre-specified categories according to local radiologists descriptions. Generalised oedema alone and in combination with peak NSE at either 48h or 72h was correlated with poor outcome at 6 months follow-up using the Cerebral Performance Category (CPC 3-5).

RESULTS

356/939 (37.9%) of patients underwent head CT. Initial CT≤24h after CA was normal in 174/218 (79.8%), whilst generalised oedema was diagnosed in 21/218 (9.6%). Between days 1-7, generalised oedema was seen in 65/143 (45.5%), acute/subacute infarction in 27/143 (18.9%) and bleeding in 9/143 (6.3%). Overall, generalised oedema predicted poor outcome with 33.6% sensitivity (95%CI:28.1-39.5) and 98.4% specificity (95%CI:94.3-99.6), whilst peak NSE demonstrated sensitivities of 61.5-64.8% and specificity 95.7% (95%CI:89.5-98.4). The combination of peak NSE>38ng/l and generalised oedema on CT predicted poor outcome with 46.0% sensitivity (95%CI:36.5-55.8) with no false positives. NSE was significantly higher in patients with generalised oedema.

CONCLUSION

In this study, generalised oedema was more common >24h≤7d after CA. The combination of CT and NSE improved sensitivity and specificity compared to CT alone, with no false positives in this limited population.