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

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.

Application of Phosphorylated Tau for Predicting Outcomes Among Sudden Cardiac Arrest Survivors

BACKGROUND

Phosphorylated Tau (p-Tau), an early biomarker of neuronal damage, has emerged as a promising candidate for predicting neurological outcomes in cardiac arrest (CA) survivors. Despite its potential, the correlation of p-Tau with other clinical indicators remains underexplored. This study assesses the predictive capability of p-Tau and its effectiveness when used in conjunction with other predictors.

METHODS

In this single-center retrospective study, 230 CA survivors had plasma and brain computed tomography scans collected within 24 h after the return of spontaneous circulation (ROSC) from January 2016 to June 2023. The patients with prearrest Cerebral Performance Category scores ≥ 3 were excluded (n = 33). The neurological outcomes at discharge with Cerebral Performance Category scores 1-2 indicated favorable outcomes. Plasma p-Tau levels were measured using an enzyme-linked immunosorbent assay, diastolic blood pressure (DBP) was recorded after ROSC, and the gray-to-white matter ratio (GWR) was calculated from brain computed tomography scans within 24 h after ROSC.

RESULTS

Of 197 patients enrolled in the study, 54 (27.4%) had favorable outcomes. Regression analysis showed that higher p-Tau levels correlated with unfavorable neurological outcomes. The levels of p-Tau were significantly correlated with DBP and GWR. For p-Tau to differentiate between neurological outcomes, an optimal cutoff of 456 pg/mL yielded an area under the receiver operating characteristic curve of 0.71. Combining p-Tau, GWR, and DBP improved predictive accuracy (area under the receiver operating characteristic curve = 0.80 vs. 0.71, p = 0.008).

CONCLUSIONS

Plasma p-Tau levels measured within 24 h following ROSC, particularly when combined with GWR and DBP, may serve as a promising biomarker of neurological outcomes in CA survivors, with higher levels predicting unfavorable outcomes.

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.

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.

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.

Recovery and Survival of Patients After Out-of-Hospital Cardiac Arrest: A Literature Review Showcasing the Big Picture of Intensive Care Unit-Related Factors

As an important public health issue, out-of-hospital cardiac arrest (OHCA) requires several stages of high quality medical care, both on-field and after hospital admission. Post-cardiac arrest shock can lead to severe neurological injury, resulting in poor recovery outcome and increased risk of death. These characteristics make this condition one of the most important issues to deal with in post-OHCA patients hospitalized in intensive care units (ICUs). Also, the majority of initial post-resuscitation survivors have underlying coronary diseases making revascularization procedure another crucial step in early management of these patients. Besides keeping myocardial blood flow at a satisfactory level, other tissues must not be neglected as well, and maintaining mean arterial pressure within optimal range is also preferable. All these procedures can be simplified to a certain level along with using targeted temperature management methods in order to decrease metabolic demands in ICU-hospitalized post-OHCA patients. Additionally, withdrawal of life-sustaining therapy as a controversial ethical topic is under constant re-evaluation due to its possible influence on overall mortality rates in patients initially surviving OHCA. Focusing on all of these important points in process of managing ICU patients is an imperative towards better survival and complete recovery rates.

Diagnostic yield of computed tomography after non-traumatic out-of-hospital cardiac arrest

AIM

Determine the frequency with which computed tomography (CT) after out-of-hospital cardiac arrest (OHCA) identifies clinically important findings.

METHODS

We included non-traumatic OHCA patients treated at a single center from February 2019 to February 2021. Clinical practice was to obtain CT head in comatose patients. Additionally, CT of the cervical spine, chest, abdomen, and pelvis were obtained if clinically indicated. We identified CT imaging obtained within 24 hours of emergency department (ED) arrival and summarized radiology findings. We used descriptive statistics to summarize population characteristics and imaging results, report their frequencies and, post hoc, compared time from ED arrival to catheterization between patients who did and did not undergo CT.

RESULTS

We included 597 subjects, of which 491 (82.2%) had a CT obtained. Time to CT was 4.1 hours [2.8-5.7]. Most (n = 480, 80.4%) underwent CT head, of which 36 (7.5%) had intracranial hemorrhage and 161 (33.5%) had cerebral edema. Fewer subjects (230, 38.5%) underwent a cervical spine CT, and 4 (1.7%) had acute vertebral fractures. Most subjects (410, 68.7%) underwent a chest CT, and abdomen and pelvis CT (363, 60.8%). Chest CT abnormalities included rib or sternal fractures (227, 55.4%), pneumothorax (27, 6.6%), aspiration or pneumonia (309, 75.4%), mediastinal hematoma (18, 4.4%) and pulmonary embolism (6, 3.7%). Significant abdomen and pelvis findings were bowel ischemia (24, 6.6%) and solid organ laceration (7, 1.9%). Most subjects that had CT imaging deferred were awake and had shorter time to catheterization.

CONCLUSIONS

CT identifies clinically important pathology after OHCA.

Electrocardiogram monitoring as a predictor of neurological and survival outcomes in patients with out-of-hospital cardiac arrest: a single-center retrospective observational study

Introduction

This study hypothesized that monitoring electrocardiogram (ECG) waveforms in patients with out-of-hospital cardiac arrest (OHCA) could have predictive value for survival or neurological outcomes. We aimed to establish a new prognostication model based on the single variable of monitoring ECG waveforms in patients with OHCA using machine learning (ML) techniques.

Methods

This observational retrospective study included successfully resuscitated patients with OHCA aged ≥ 18 years admitted to an intensive care unit in Japan between April 2010 and April 2020. Waveforms from ECG monitoring for 1 h after admission were obtained from medical records and examined. Based on the open-access PTB-XL dataset, a large publicly available 12-lead ECG waveform dataset, we built an ML-supported premodel that transformed the II-lead waveforms of the monitoring ECG into diagnostic labels. The ECG diagnostic labels of the patients in this study were analyzed for prognosis using another model supported by ML. The endpoints were favorable neurological outcomes (cerebral performance category 1 or 2) and survival to hospital discharge.

Results

In total, 590 patients with OHCA were included in this study and randomly divided into 3 groups (training set, n = 283; validation set, n = 70; and test set, n = 237). In the test set, our ML model predicted neurological and survival outcomes, with the highest areas under the receiver operating characteristic curves of 0.688 (95% CI: 0.682-0.694) and 0.684 (95% CI: 0.680-0.689), respectively.

Conclusion

Our ML predictive model showed that monitoring ECG waveforms soon after resuscitation could predict neurological and survival outcomes in patients with OHCA.

Explainable artificial intelligence-based prediction of poor neurological outcome from head computed tomography in the immediate post-resuscitation phase

Predicting poor neurological outcomes after resuscitation is important for planning treatment strategies. We constructed an explainable artificial intelligence-based prognostic model using head computed tomography (CT) scans taken immediately within 3 h of resuscitation from cardiac arrest and compared its predictive accuracy with that of previous methods using gray-to-white matter ratio (GWR). We included 321 consecutive patients admitted to our institution after resuscitation for out-of-hospital cardiopulmonary arrest with circulation resumption over 6 years. A machine learning model using head CT images with transfer learning was used to predict the neurological outcomes at 1 month. These predictions were compared with the predictions of GWR for multiple regions of interest in head CT using receiver operating characteristic (ROC)-area under curve (AUC) and precision recall (PR)-AUC. The regions of focus were visualized using a heatmap. Both methods had similar ROC-AUCs, but the machine learning model had a higher PR-AUC (0.73 vs. 0.58). The machine learning-focused area of interest for classification was the boundary between gray and white matter, which overlapped with the area of focus when diagnosing hypoxic- ischemic brain injury. The machine learning model for predicting poor outcomes had superior accuracy to conventional methods and could help optimize treatment.

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.

Efficacy of Quantitative Pupillary Light Reflex for Predicting Neurological Outcomes in Patients Treated with Targeted Temperature Management after Cardiac Arrest: A Systematic Review and Meta-Analysis

Background and objectives: This study aims to evaluate the usefulness of the quantitative pupillary light reflex as a prognostic tool for neurological outcomes in post-cardiac arrest patients treated with targeted temperature management (TTM). Material and Methods: We systematically searched MEDLINE, EMBASE, and the Cochrane Library (search date: 9 July 2021) for studies on post-cardiac arrest patients treated with TTM that had measured the percent constriction of pupillary light reflex (%PLR) with quantitative pupillometry as well as assessed the neurological outcome. For an assessment of the methodological quality of the included studies, two authors utilized the prognosis study tool independently. Results: A total of 618 patients from four studies were included in this study. Standardized mean differences (SMDs) were calculated to compare patients with good or poor neurological outcomes. A higher %PLR measured at 0–24 h after hospital admission was related to good neurological outcomes at 3 months in post-cardiac arrest patients treated with TTM (SMD 0.87; 95% confidence interval 0.70–1.05; I² = 0%). A higher %PLR amplitude measured at 24–48 h after hospital admission was also associated with a good neurological outcome at 3 months in post-cardiac arrest patients treated with TTM, but with high heterogeneity (standardized mean difference 0.86; 95% confidence interval 0.40–1.32; I² = 70%). The evidence supporting these findings was of poor quality. For poor neurological outcome, the prognosis accuracy of %PLR was 9.19 (pooled diagnostic odds ratio, I² = 0%) and 0.75 (area under the curve). Conclusions: The present meta-analysis could not reveal that change of %PLR was an effective tool in predicting neurological outcomes for post-cardiac arrest patients treated with TTM owing to a paucity of included studies and the poor quality of the evidence.

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.

Optic Nerve Sheath Diameter for Predicting Outcomes in Post-Cardiac Arrest Syndrome: An Updated Systematic Review and Meta-Analysis

We aimed to identify the efficacy of optic nerve sheath diameter (ONSD) in predicting mortality and poor neurological outcomes (PNO) in post-cardiac arrest syndrome (PCAS) by the measurement time of outcomes. We conducted an extensive literature search in EMBASE, MEDLINE, and Cochrane Library, which included studies on the prognostic accuracy of ONSD in predicting PNO and mortality in PCAS by the measured time of outcomes. A total of 791 patients from nine studies were included. Increased ONSD was weakly associated with PNO by a high heterogeneity (standardized mean difference with 95% confidence interval = 0.74 (0.22, 1.27); I² = 87%). The analysis by the measurement time of PNO and mortality for ONSD had no significant difference due to insufficient articles or high heterogeneities. The prognostic accuracy of ONSD was 23.97 (pooled diagnostic odds ratio, I² = 0%) and 0.94 (area under the curve) for short-term PNO. The pooled results showed low or very low quality and very low quality of evidence for PNO and mortality, respectively. ONSD measurement might be an effective predictor for short-term PNO in PCAS. An analysis by measurement time of outcomes showed no significant evidence for ONSD measurement effectiveness in predicting mortality and PNO.

Prognostic Effect of Underlying Chronic Kidney Disease and Renal Replacement Therapy on the Outcome of Patients after Out-of-Hospital Cardiac Arrest: A Nationwide Observational Study

Background and Objectives: This study assessed the prognostic value of underlying chronic kidney disease (CKD) and renal replacement therapy (RRT) on the clinical outcomes from out-of-hospital cardiac arrest (OHCA). Materials and Methods: This retrospective study was conducted utilizing the population-based OHCA data of South Korea between 2008 and 2018. Adult (>18 years) OHCA patients with a medical cause of cardiac arrest were included and classified into three categories based on the underlying CKD and RRT: (1) non-CKD group; (2) CKD without RRT group; and (3) CKD with RRT group. A total of 13,682 eligible patients were included (non-CKD, 9863; CKD without RRT, 1778; CKD with RRT, 2041). From the three comparison subgroups, data with propensity score matching were extracted. The influence of CKD and RRT on patient outcomes was assessed using propensity score matching and multivariate logistic regression analyses. The primary outcome was survival at hospital discharge and the secondary outcome was a good neurological outcome at hospital discharge. Results: The two CKD groups (CKD without RRT and CKD with RRT) showed no significant difference in survival at hospital discharge compared with the non-CKD group (CKD without RRT vs. non-CKD, p > 0.05; CKD with RRT vs. non-CKD, p > 0.05). The non-CKD group had a higher chance of having good neurological outcomes than the CKD groups (non-CKD vs. CKD without RRT, p < 0.05; non-CKD vs. CKD with RRT, p < 0.05) whereas there was no significant difference between the two CKD groups (CKD without RRT vs. CKD with RRT, p > 0.05). Conclusions: Compared with patients without CKD, the underlying cause of CKD—regardless of RRT—may be linked to poor neurological outcomes. Underlying CKD and RRT had no effect on the survival at hospital discharge.

Neuroprognostic Accuracy of Quantitative Versus Standard Pupillary Light Reflex for Adult Postcardiac Arrest Patients: A Systematic Review and Meta-Analysis

OBJECTIVES

An automated infrared pupillometer measures quantitative pupillary light reflex using a calibrated light stimulus. We examined whether the timing of performing quantitative pupillary light reflex or standard pupillary light reflex may impact its neuroprognostic performance in postcardiac arrest comatose patients and whether quantitative pupillary light reflex may outperform standard pupillary light reflex in early postresuscitation phase.

DATA SOURCES

PubMed and Embase databases from their inception to July 2020.

STUDY SELECTION

We selected studies providing sufficient data of prognostic values of standard pupillary light reflex or quantitative pupillary light reflex to predict neurologic outcomes in adult postcardiac arrest comatose patients.

DATA EXTRACTION

Quantitative data required for building a 2 × 2 contingency table were extracted, and study quality was assessed using standard criteria.

DATA SYNTHESIS

We used the bivariate random-effects model to estimate the pooled sensitivity and specificity of standard pupillary light reflex or quantitative pupillary light reflex in predicting poor neurologic outcome during early (< 72 hr), middle (between 72 and 144 hr), and late (≧ 145 hr) postresuscitation periods, respectively. We included 39 studies involving 17,179 patients. For quantitative pupillary light reflex, the cut off points used in included studies to define absent pupillary light reflex ranged from 0% to 13% (median: 7%) and from zero to 2 (median: 2) for pupillary light reflex amplitude and Neurologic Pupil index, respectively. Late standard pupillary light reflex had the highest area under the receiver operating characteristic curve (0.98, 95% CI [CI], 0.97-0.99). For early standard pupillary light reflex, the area under the receiver operating characteristic curve was 0.80 (95% CI, 0.76-0.83), with a specificity of 0.91 (95% CI, 0.85-0.95). For early quantitative pupillary light reflex, the area under the receiver operating characteristic curve was 0.83 (95% CI, 0.79-0.86), with a specificity of 0.99 (95% CI, 0.91-1.00).

CONCLUSIONS

Timing of pupillary light reflex examination may impact neuroprognostic accuracy. The highest prognostic performance was achieved with late standard pupillary light reflex. Early quantitative pupillary light reflex had a similar specificity to late standard pupillary light reflex and had better specificity than early standard pupillary light reflex. For postresuscitation comatose patients, early quantitative pupillary light reflex may substitute for early standard pupillary light reflex in the neurologic prognostication algorithm.

Prognostic value of changes in the cardiac arrest rhythms from the prehospital stage to the emergency department in out-of-hospital cardiac arrest patients without prehospital returns of spontaneous circulation: A nationwide observational study

Background

This study aimed to assess the prognostic value of the changes in cardiac arrest rhythms from the prehospital stage to the ED (emergency department) in out-of-hospital cardiac arrest (OHCA) patients without prehospital returns of spontaneous circulation (ROSC).

Methods

This retrospective analysis was performed using nationwide population-based OHCA data from South Korea between 2012 and 2016. Patients with OHCA with medical causes and without prehospital ROSC were included and divided into four groups according to the nature of their cardiac arrest rhythms (shockable or non-shockable) in the prehospital stage and in the ED: (1) the shockable and shockable (Shock-Shock) group, (2) the shockable and non-shockable (Shock-NShock) group, (3) the non-shockable and shockable (NShock-Shock) group, and (4) the non-shockable and non-shockable (NShock-NShock) group. The presence of a shockable rhythm was confirmed based on the delivery of an electrical shock. Propensity score matching and multivariate logistic regression analyses were used to assess the effect of changes in the cardiac rhythms on patient outcomes. The primary outcome was sustained ROSC in the ED; the secondary outcomes were survival to hospital discharge and good neurological outcomes at hospital discharge.

Results

After applying the exclusion criteria, 51,060 eligible patients were included in the study (Shock-Shock, 4223; Shock-NShock, 3060; NShock-Shock, 11,509; NShock-NShock, 32,268). The propensity score-matched data were extracted from the six comparative subgroups. For sustained ROSC in the ED, Shock-Shock showed a higher likelihood than Shock-NShock (P <0.01) and NShock-NShock (P <0.01), Shock-NShock showed a lower likelihood than NShock-Shock (P <0.01) and NShock-NShock (P <0.01), NShock-Shock showed a higher likelihood NShock-NShock (P <0.01). For survival to hospital discharge, Shock-Shock showed a higher likelihood than Shock-NShock (P <0.01), NShock-Shock (P <0.01), and NShock-NShock (P <0.01), Shock-NShock showed a higher likelihood than NShock-Shock (P <0.01) and NShock-NShock (P <0.01), of sustained ROSC in the ED. For good neurological outcomes, Shock-Shock showed higher likelihood than Shock-NShock (P <0.01), NShock-Shock (P <0.01), and NShock-NShock (P <0.01), Shock-NShock showed better likelihood than NShock-NShock (P <0.01), NShock-Shock showed a better likelihood than NShock-NShock (P <0.01).

Conclusion

Sustained ROSC in the ED may be expected for patients with shockable rhythms in the ED compared with those with non-shockable rhythms in the ED. For the clinical outcomes, survival to hospital discharge and neurological outcomes, patients with Shock-Shock showed the best outcome, whereas patients with NShock-NShock showed the poorest outcome and Shock-NShock showed a higher likelihood of achieving survival to hospital discharge with no significant differences in the neurological outcomes compared with NShock-Shock.

Outcome and prognostication after cardiac arrest

The outcome after out-of-hospital cardiac arrest has historically been grim at best. The current overall survival rate of patients admitted to a hospital is approximately 10%, making cardiac arrest one of the leading causes of death in the United States. The situation is improving with the incorporation of therapeutic temperature modulation, aggressive prevention of secondary brain injury, and improved access to advanced cardiovascular support, all of which have decreased mortality and allowed for better outcomes. Mortality after cardiac arrest is often the direct result of active withdrawal of life-sustaining therapy based on the perception that neurological recovery is not possible. This reality highlights the importance of providing accurate estimates of neurological prognosis to decision makers when discussing goals of care. The current standard of care for assessing neurological status in patients with hypoxic-ischemic encephalopathy emphasizes a multimodal approach that includes five elements: (1) neurological examination off sedation, (2) continuous electroencephalography, (3) serum neuron-specific enolase levels, (4) magnetic resonance brain imaging, and (5) somatosensory-evoked potential testing. Sophisticated decision support systems that can integrate these clinical, imaging, and biomarker and neurophysiologic data and translate it into meaningful projections of neurological outcome are urgently needed.

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.

Skin sympathetic nerve activity as a biomarker for neurologic recovery during therapeutic hypothermia for cardiac arrest

BACKGROUND

Targeted temperature management (TTM) improves neurologic outcome after cardiac arrest. However, better neurologic prognostication is needed.

OBJECTIVE

The purpose of this study was to test the hypothesis that noninvasive recording of skin sympathetic nerve activity (SKNA) and its association with heart rate (HR) during TTM may serve as a biomarker of neurologic status.

METHODS

SKNA recordings were analyzed from 29 patients undergoing TTM. Patients were grouped based on Clinical Performance Category (CPC) score into group 1 (CPC 1-2) representing a good neurologic outcome and group 2 (CPC 3-5) representing a poor neurologic outcome.

RESULTS

Of the 29 study participants, 18 (62%) were deemed to have poor neurologic outcome. At all timepoints, low average skin sympathetic nerve activity (aSKNA) was associated with poor neurologic outcome (odds ratio 22.69; P = .002) and remained significant (P = .03) even when adjusting for presenting clinical factors. The changes in aSKNA and HR during warming in group 1 were significantly correlated (ρ = 0.49; P <.001), even when adjusting for corresponding temperature and mean arterial pressure measurements (P = .017), whereas this correlation was not observed in group 2. Corresponding to high aSKNA, there was increased nerve burst activity during warming in group 1 compared to group 2 (0.739 ± 0.451 vs 0.176 ± 0.231; P = .013).

CONCLUSION

Neurologic recovery was retrospectively associated with SKNA. Patients undergoing TTM who did not achieve neurologic recovery were associated with low SKNA and lacked a significant correlation between SKNA and HR. These preliminary results indicate that SKNA may potentially be a useful biomarker to predict neurologic status in patients undergoing TTM.

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.

Targeted temperature management and early neuro-prognostication after cardiac arrest

Targeted temperature management (TTM) is a recommended neuroprotective intervention for coma after out-of-hospital cardiac arrest (OHCA). However, controversies exist concerning the proper implementation and overall efficacy of post-CA TTM, particularly related to optimal timing and depth of TTM and cooling methods. A review of the literature finds that optimizing and individualizing TTM remains an open question requiring further clinical investigation. This paper will summarize the preclinical and clinical trial data to-date, current recommendations, and future directions of this therapy, including new cooling methods under investigation. For now, early induction, maintenance for at least 24 hours, and slow rewarming utilizing endovascular methods may be preferred. Moreover, timely and accurate neuro-prognostication is valuable for guiding ethical and cost-effective management of post-CA coma. Current evidence for early neuro-prognostication after TTM suggests that a combination of initial prediction models, biomarkers, neuroimaging, and electrophysiological methods is the optimal strategy in predicting neurological functional outcomes.

Utility of brain parenchyma density measurement and computed tomography perfusion imaging in predicting brain death

Purpose

To assess the utility of brain parenchyma density measurement on unenhanced computed tomography (CT) in predicting brain death (BD), in order to evaluate the added value of CT perfusion (CTP).

Material and methods

A total of 77 patients who were clinically diagnosed as BD and had both CT angiography (CTA) and CTP imaging in the same session were retrospectively reviewed. On unenhanced phase of CTA, density measurement was performed from 23 regions of interests (ROIs) which were located in the following areas: level of basal ganglia (caudate nucleus, putamen, corpus callosum, posterior limb of internal capsule), level of brainstem, grey- white matters on levels of centrum semiovale (CS), high convexity (HC), and cerebellum. CTP images were evaluated qualitatively and independently. Grey matter (GM), white matter (WM), density, and GM/WM density ratio of BD patients were compared with control subjects.

Results

Comparing with the normal control group, the GM and WM density at each level and GM/WM density ratio of CS, HC, and cerebellum level were significantly lower in brain-dead patients (p = 0.019 for HC-WM, p < 0.001 for other areas). Using ROC analysis, the highest value of area under curve (AUC) for the GM/WM density ratio was found at the HC level (AUC = 0.907). The sensitivity of the GM/WM density ratio at the HC level was found to be 90% when the cut-off value of 1.25 was identified. Evaluating the GM/WM density ratio together with the CTP results increased the sensitivity further to 98%.

Conclusions

The GM/WM density ratio at the HC level on unenhanced CT may be a useful finding to predict BD. Also, the addition of CTP increases the sensitivity of this method.

Neurologic prognostication after resuscitation from cardiac arrest

Out-of-hospital cardiac arrest remains a leading cause of mortality in the United States, and the majority of patients who die after achieving return of spontaneous circulation die from withdrawal of care due to a perceived poor neurologic prognosis. Unfortunately, withdrawal of care often occurs during the first day of admission and research suggests this early withdrawal of care may be premature and result in unnecessary deaths for patients who would have made a full neurologic recovery. In this review, we explore the evidence for neurologic prognostication in the emergency department for patients who achieve return of spontaneous circulation after an out-of-hospital cardiac arrest.

Neuron-specific enolase and neuroimaging for prognostication after cardiac arrest treated with targeted temperature management

BACKGROUND

Prognostication after cardiac arrest (CA) needs a multimodal approach, but the optimal method is not known. We tested the hypothesis that the combination of neuron-specific enolase (NSE) and neuroimaging could improve outcome prediction after CA treated with targeted temperature management (TTM).

METHODS

A retrospective observational cohort study was performed on patients who underwent at least one NSE measurement between 48 and 72 hr; received both a brain computed tomography (CT) scan within 24 hr and diffusion-weighted magnetic resonance imaging (DW-MRI) within 7 days after return of spontaneous circulation (ROSC); and were treated with TTM after out-of-hospital CA between 2009 and 2017 at the Seoul St. Mary's Hospital in Korea. The primary outcome was a poor neurological outcome at 6 months after CA, defined as a cerebral performance category of 3-5.

RESULTS

A total of 109 subjects underwent all three tests and were ultimately included in this study. Thirty-four subjects (31.2%) experienced good neurological outcomes at 6 months after CA. The gray matter to white matter attenuation ratio (GWR) was weakly correlated with the mean apparent diffusion coefficient (ADC), PV400 and NSE (Spearman's rho: 0.359, -0.362 and -0.263, respectively). NSE was strongly correlated with the mean ADC and PV400 (Spearman's rho: -0.623 and 0.666, respectively). Serum NSE had the highest predictive value among the single parameters (area under the curve (AUC) 0.912, sensitivity 70.7% for maintaining 100% specificity). The combination of a DWI parameter (mean ADC or PV400) and NSE had better prognostic performance than the combination of the CT parameter (GWR) and NSE. The addition of the GWR to a DWI parameter and NSE did not improve the prediction of neurological outcomes.

CONCLUSION

The GWR (≤ 24 hr) is weakly correlated with the mean ADC (≤ 7 days) and NSE (highest between 48 and 72 hr). The combination of a DWI parameter and NSE has better prognostic performance than the combination of the GWR and NSE. The addition of the GWR to a DWI parameter and NSE does not improve the prediction of neurological outcomes after CA treatment with TTM.

Early risk stratification after resuscitation from cardiac arrest

Emergency clinicians often resuscitate cardiac arrest patients, and after acute resuscitation, clinicians face multiple decisions regarding disposition. Recent evidence suggests that out-of-hospital cardiac arrest patients with return of spontaneous circulation have higher odds of survival to hospital discharge, long-term survival, and improved functional outcomes when treated at centers that can provide advanced multidisciplinary care. For community clinicians, a high volume cardiac arrest center may be hours away. While current guidelines recommend against neurological prognostication in the first hours or days after return of spontaneous circulation, there are early findings suggestive of irrecoverable brain injury in which the patient would receive no benefit from transfer. In this Concepts article, we describe a simplified approach to quickly evaluate neurological status in cardiac arrest patients and identify findings concerning for irrecoverable brain injury. Characteristics of the arrest and resuscitation, initial neurological assessment, and brain computed tomography together can identify patients with high likelihood of irrecoverable anoxic injury. Patients who may benefit from centers with access to continuous electroencephalography are discussed. This approach can be used to identify patients who may benefit from rapid transfer to cardiac arrest centers versus those who may benefit from care close to home. Risk stratification also can provide realistic expectations for recovery to families.

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.

Prognostic factors for neurological outcomes in Korean targeted temperature management recipients with return of spontaneous circulation after out-of-hospital cardiac arrests: A nationwide observational study

Targeted temperature management (TTM) is recommended for comatose patients after out-of-hospital cardiac arrests (OHCAs). Even after successful TTM, several factors could influence the neuroprotective effect of TTM. The aim of this study is to identify prognostic factors associated with good neurological outcomes in TTM recipients.This study used nationwide data during 2012 to 2016 to investigate prognostic factors associated with good neurological outcomes in patients who received TTM after the return of spontaneous circulation (ROSC). Multivariate logistic regression analysis was conducted to analyse the factors that may affect the neurological outcomes in the TTM recipients.The study included 1578 eligible patients, comprising 767 with good and 811 with poor neurological outcomes. Multivariable analyses showed that OHCA in public places (OR, 1.599; 95% CI, 1.100-2.323, P = .014), initial shockable rhythms (OR, 1.721; 95% CI, 1.191-2.486, P = .004), pre-hospital ROSCs (OR, 6.748; 95% CI, 4.703-9.682, P < .001), bystander cardiopulmonary resuscitation (CPR) (OR, 1.715; 95% CI, 1.200-2.450, P = .003), and primary coronary interventions (PCIs) (OR, 2.488; 95% CI, 1.639-3.778, P < .001) were statistically significantly associated with good neurological outcomes. Whereas, increase of age (OR, 0.962; 95% CI, 0.950-0.974, P < .001) and conventional cooling (OR, 0.478; 95% CI, 0.255-0.895, P = .021) were statistically significantly associated with poor neurological outcome.This study suggests that being younger, experiencing OHCA in public places, having initial shockable rhythm, pre-hospital ROSC, and bystander CPR, implementing PCIs and applying intravascular or surface cooling devices compared to conventional cooling method could predict good neurological outcomes in post-cardiac arrest patients who received TTM.

Cardiac arrest: An interdisciplinary review of the literature from 2018

OBJECTIVES

The Interdisciplinary Cardiac Arrest Research Review (ICARE) group was formed in 2018 to conduct a systematic annual search of peer-reviewed literature relevant to cardiac arrest (CA). The goals of the review are to illustrate best practices and help reduce knowledge silos by disseminating clinically relevant advances in the field of CA across disciplines.

METHODS

An electronic search of PubMed using keywords related to CA was conducted. Title and abstracts retrieved by these searches were screened for relevancy, separated by article type (original research or review), and sorted into 7 categories. Screened manuscripts underwent standardized scoring of overall methodological quality and importance. Articles scoring higher than 99 percentiles by category-type were selected for full critique. Systematic differences between editors and reviewer scores were assessed using Wilcoxon signed-rank test.

RESULTS

A total of 9119 articles were identified on initial search; of these, 1214 were scored after screening for relevance and deduplication, and 80 underwent full critique. Prognostication & Outcomes category comprised 25% and Epidemiology & Public Health 17.5% of fully reviewed articles. There were no differences between editor and reviewer scoring.

CONCLUSIONS

The total number of articles demonstrates the need for an accessible source summarizing high-quality research findings to serve as a high-yield reference for clinicians and scientists seeking to absorb the ever-growing body of CA-related literature. This may promote further development of the unique and interdisciplinary field of CA medicine.

A comparison of intravascular and surface cooling devices for targeted temperature management after out-of-hospital cardiac arrest: A nationwide observational study

This study aimed to compare prognostic difference between intravascular cooling devices (ICDs) and surface cooling devices (SCDs) in targeted temperature management (TTM) recipients.Adult TTM recipients using ICD or SCD during 2012 to 2016 were included in this nationwide observational study. The outcome was survival to hospital discharge and good neurological outcome at hospital discharge.Among 142,905 out-of-hospital cardiac arrest patients, 1159 patients (SCD, n = 998; ICD, n = 161) were investigated. After propensity score matching for all patients, 161 matched pairs of patients were available for analysis (SCD, n = 161; ICD, n = 161). We observed no significant differences in the survival to hospital discharge (SCD, n = 144 [89.4%] vs ICD, n = 150 [93.2%], P = .32) and the good neurological outcomes (SCD, n = 86 [53.4%] vs ICD, n = 91 [56.5%], P = .65). TTM recipients were categorized by age groups (elderly [age >65 years] vs nonelderly [age ≤65 years]) to compare prognostic difference between ICD and SCD according to the age groups. In the nonelderly group, the use of ICD or SCD was not a significant factor for survival to hospital discharge or good neurologic outcome. Whereas, the use of ICD was significantly associated with good neurological outcome (odds ratio, 3.97; 95% confidence interval, 1.19 - 13.23, P = .02) compared with SCD in the elderly group.There were no significant differences in the survival to hospital discharge and the good neurological outcomes between SCD and ICD recipients. However, the use of ICD might be more beneficial than SCD in elderly patients.

Grey-white matter ratio measured using early unenhanced brain computed tomography shows no correlation with neurological outcomes in patients undergoing targeted temperature management after cardiac arrest

AIM

This study evaluated whether the grey-white matter ratio (GWR) assessed via early brain computed tomography (CT) within 2 h after the return of spontaneous circulation (ROSC) following cardiac arrest is associated with poor neurological outcomes after 6 months in post-cardiac arrest patients treated with targeted temperature management (TTM).

METHODS

This study used data from the Korean Hypothermia Network prospective registry obtained from November 2015 to October 2017 to assess patients with out-of-hospital cardiac arrest (OHCA) who underwent brain CT within 2 h following the ROSC. The primary endpoint was the neurological outcome 6 months post-cardiac arrest (cerebral performance category; CPC). The GWR was measured using early brain CT images. The subgroup analysis examined the difference in GWRs obtained from early and repeated brain CT.

RESULTS

Five-hundred-twelve patients were enrolled. Good (CPC 1-2) and poor (CPC 3-5) neurological outcomes were observed in 162 (31.6%) and 350 (68.4%) patients, respectively. The multivariate logistic regression analysis revealed that the GWR measured using early brain CT was a statistically nonsignificant predictor of poor neurologic outcomes (p = 0.727). In patients with poor outcomes, the mean GWR obtained from early and repeated CT images were 1.171 ± 0.058 and 1.091 ± 0.133, respectively (p < 0.001); there was no statistically significant difference between the GWRs in patients with good outcomes.

CONCLUSION

The GWR assessed via early brain CT alone is not an independent factor predictive of poor neurologic outcomes but could be useful when used with repeated CT data.