Alpha-power in electroencephalography as good outcome predictor for out-of-hospital cardiac arrest survivors

A nomogram based on quantitative EEG to predict the prognosis of nontraumatic coma patients in the neuro-intensive care unit

OBJECTIVE

We aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work.

METHODS

This retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model.

RESULTS

After excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244-1074.9000)], sleep cycle [OR: 0.006 (0.0002-0.1808)], and RAV [0.068 (0.0049-0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899-0.979).

CONCLUSION

A lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge.

CLINICAL IMPLICATION

This study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice.

Normal value of neuron-specific enolase for predicting good neurological outcomes in comatose out-of-hospital cardiac arrest survivors

Research on prognostic factors for good outcomes in out-of-hospital cardiac arrest (OHCA) survivors is lacking. We assessed whether normal levels of normal neuron-specific enolase (NSE) value would be useful for predicting good neurological outcomes in comatose OHCA survivors treated with targeted temperature management (TTM). This registry-based observational study with consecutive adult (≥18 years) OHCA survivors with TTM who underwent NSE measurement 48 hours after cardiac arrest was conducted from October 2015 to November 2022. Normal NSE values defined as the upper limit of the normal range by the manufacturer (NSE <16.3 μg/L) and guideline-suggested (NSE < 60 μg/L) were examined for good neurologic outcomes, defined as Cerebral Performance Categories ≤2, at 6 months post-survival. Among 226 OHCA survivors with TTM, 200 patients who underwent NSE measurement were enrolled. The manufacturer-suggested normal NSE values (<16.3 μg/L) had a specificity of 99.17% for good neurological outcomes with a very low sensitivity of 12.66%. NSE <60 μg/L predicted good outcomes with a sensitivity of 87.34% and specificity of 72.73%. However, excluding 14 poor-outcome patients who died from multi-organ dysfunction excluding hypoxic brain injury, the sensitivity and specificity of normal NSE values were 12.66% and 99.07% of NSE < 16.3 μg/L, and 87.34% and 82.24% of NSE < 60 μg/L. The manufacturer-suggested normal NSE had high specificity with low sensitivity, but the guideline-suggested normal NSE value had a comparatively low specificity for good outcome prediction in OHCA survivors. Our data demonstrate normal NSE levels can be useful as a tool for multimodal appropriation of good outcome prediction.