Assessing brain injury after cardiac arrest, towards a quantitative approach:

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.

Predicting early recovery of consciousness after cardiac arrest supported by quantitative electroencephalography

OBJECTIVE

To determine the ability of quantitative electroencephalography (QEEG) to improve the accuracy of predicting recovery of consciousness by post-cardiac arrest day 10.

METHODS

Unconscious survivors of cardiac arrest undergoing daily clinical and EEG assessments through post-cardiac arrest day 10 were studied in a prospective observational cohort study. Power spectral density, local coherence, and permutation entropy were calculated from daily EEG clips following a painful stimulus. Recovery of consciousness was defined as following at least simple commands by day 10. We determined the impact of EEG metrics to predict recovery when analyzed with established predictors of recovery using partial least squares regression models. Explained variance analysis identified which features contributed most to the predictive model.

RESULTS

367 EEG epochs from 98 subjects were analyzed in conjunction with clinical measures. Highest prediction accuracy was achieved when adding QEEG features from post-arrest days 4-6 to established predictors (area under the receiver operating curve improved from 0.81 ± 0.04 to 0.86 ± 0.05). Prediction accuracy decreased from 0.84 ± 0.04 to 0.79 ± 0.04 when adding QEEG features from post-arrest days 1-3. Patients with recovery of command-following by day 10 showed higher coherence across the frequency spectrum and higher centro-occipital delta-frequency spectral power by days 4-6, and globally-higher theta range permutation entropy by days 7-10.

CONCLUSIONS

Adding quantitative EEG metrics to established predictors of recovery allows modest improvement of prediction accuracy for recovery of consciousness, when obtained within a week of cardiac arrest. Further research is needed to determine the best strategy for integration of QEEG data into prognostic models in this patient population.

Update on Cardiopulmonary Resuscitation in Small Animals

Cardiopulmonary arrest (CPA), the acute cessation of ventilation and systemic perfusion, leads to discontinuation of tissue oxygen delivery and death if not quickly reversed. Reported resuscitation rates suggest that the heart can be restarted in 40% to 50% of dogs and cats treated with cardiopulmonary resuscitation (CPR). However, approximately 80% of these animals do not survive to hospital discharge. To minimize mortality due to CPA a broad strategy is required including preparedness and prevention measures, basic and advanced life support as well as post-cardiac arrest care. This article summarizes the current guidelines on the treatment of small animals with CPA..

Cerebrospinal fluid lactate dehydrogenase as a potential predictor of neurologic outcomes in cardiac arrest survivors who underwent target temperature management

PURPOSE

Cerebrospinal fluid (CSF) lactate dehydrogenase (LDH) levels increase in patients with brain injury. We investigated neurologic outcomes associated with CSF LDH levels in out-of-hospital cardiac arrest (OHCA) survivors who underwent target temperature management (TTM).

MATERIALS AND METHODS

This was a prospective single-centre observational study from April 2018 to May 2019 on a cohort of 41 patients. CSF and serum LDH samples were obtained immediately (LDH₀) and at 24 (LDH₂₄), 48 (LDH₄₈), and 72 h (LDH₇₂) after return of spontaneous circulation (ROSC). Neurologic outcomes were assessed at 3 months after ROSC using the Cerebral Performance Category scale.

RESULTS

Twenty-one patients had a poor neurologic outcome. CSF LDH levels were significantly higher in the poor neurologic outcome group at each time point. The area under the curve (AUC) of CSF LDH₄₈ was 0.941 (95% confidence interval [CI], 0.806-0.992). With a cut off value of 250 U/L, CSF LDH₄₈ had a high sensitivity (94.1%; 95% CI, 71.3-99.9) at 100% specificity.

CONCLUSIONS

CSF LDH level at 48 h was a highly specific and sensitive marker for 3-month poor neurologic outcome. This may constitute a useful predictive marker for neurologic outcome in OHCA survivors treated with TTM.

Standardized EEG analysis to reduce the uncertainty of outcome prognostication after cardiac arrest

PURPOSE

Post-resuscitation guidelines recommend a multimodal algorithm for outcome prediction after cardiac arrest (CA). We aimed at evaluating the prevalence of indeterminate prognosis after application of this algorithm and providing a strategy for improving prognostication in this population.

METHODS

We examined a prospective cohort of comatose CA patients (n = 485) in whom the ERC/ESICM algorithm was applied. In patients with an indeterminate outcome, prognostication was investigated using standardized EEG classification (benign, malignant, highly malignant) and serum neuron-specific enolase (NSE). Neurological recovery at 3 months was dichotomized as good (Cerebral Performance Categories [CPC] 1-2) vs. poor (CPC 3-5).

RESULTS

Using the ERC/ESICM algorithm, 155 (32%) patients were prognosticated with poor outcome; all died at 3 months. Among the remaining 330 (68%) patients with an indeterminate outcome, the majority (212/330; 64%) showed good recovery. In this patient subgroup, absence of a highly malignant EEG by day 3 had 99.5 [97.4-99.9] % sensitivity for good recovery, which was superior to NSE < 33 μg/L (84.9 [79.3-89.4] % when used alone; 84.4 [78.8-89] % when combined with EEG, both p < 0.001). Highly malignant EEG had equal specificity (99.5 [97.4-99.9] %) but higher sensitivity than NSE for poor recovery. Further analysis of the discriminative power of outcome predictors revealed limited value of NSE over EEG.

CONCLUSIONS

In the majority of comatose CA patients, the outcome remains indeterminate after application of ERC/ESICM prognostication algorithm. Standardized EEG background analysis enables accurate prediction of both good and poor recovery, thereby greatly reducing uncertainty about coma prognostication in this patient population.