A Cerebral Recovery Index (CRI) for early prognosis in patients after cardiac arrest

Abstract

Introduction: Electroencephalogram (EEG) monitoring in patients treated with therapeutic hypothermia after cardiac arrest may assist in early outcome prediction. Quantitative EEG (qEEG) analysis can reduce the time needed to review long-term EEG and makes the analysis more objective. In this study, we evaluated the predictive value of qEEG analysis for neurologic outcome in postanoxic patients.

Methods: In total, 109 patients admitted to the ICU for therapeutic hypothermia after cardiac arrest were included, divided over a training and a test set. Continuous EEG was recorded during the first 5 days or until ICU discharge. Neurologic outcomes were based on the best achieved Cerebral Performance Category (CPC) score within 6 months. Of the training set, 27 of 56 patients (48%) and 26 of 53 patients (49%) of the test set achieved good outcome (CPC 1 to 2). In all patients, a 5 minute epoch was selected each hour, and five qEEG features were extracted. We introduced the Cerebral Recovery Index (CRI), which combines these features into a single number.

Results: At 24 hours after cardiac arrest, a CRI <0.29 was always associated with poor neurologic outcome, with a sensitivity of 0.55 (95% confidence interval (CI): 0.32 to 0.76) at a specificity of 1.00 (CI, 0.86 to 1.00) in the test set. This results in a positive predictive value (PPV) of 1.00 (CI, 0.73 to 1.00) and a negative predictive value (NPV) of 0.71 (CI, 0.53 to 0.85). At the same time, a CRI >0.69 predicted good outcome, with a sensitivity of 0.25 (CI, 0.10 to 0.14) at a specificity of 1.00 (CI, 0.85 to 1.00) in the test set, and a corresponding NPV of 1.00 (CI, 0.54 to 1.00) and a PPV of 0.55 (CI, 0.38 to 0.70).

Conclusions: We introduced a combination of qEEG measures expressed in a single number, the CRI, which can assist in prediction of both poor and good outcomes in postanoxic patients, within 24 hours after cardiac arrest.

Figure 1

Example of two signals with different variance in amplitude. The signal in (A) shows two short periods with high amplitude on a zero background; the variance in amplitude in this signal is relatively high, whereas the signal in (B) has a more-regular or constant amplitude. The signal in A can be compared with an EEG showing a burst-suppression pattern, whereas the signal in B can be compared with an EEG with continuous amplitude. This is expressed in the regularity index (compare Equation 2 and Figure 2).

Figure 2

Calculating the regularity of the amplitude (REG) in an EEG showing a burst-suppression pattern (A) and a diffusely slowed pattern (B). In the top graphs, the raw EEG is shown (black), together with the EEG, after squaring and applying a moving average filter (with a window of 0.5 seconds) (blue). In the bottom graphs, the signal q is obtained after sorting this smoothed signal in descending order. The calculated value for the regularity (REG) is the normalized variance of this sorted signal q (compare Equation 2). REG is normalized from 0 to 1, where a higher value corresponds to a signal with a more regular amplitude, as illustrated.

Figure 3

Normalized qEEG scores. All five qEEG values are normalized using a smooth sigmoid function (Equations 3, 4, 5, 6, 7), resulting in score for each feature (annotated with a hat) between 0 and 1. (SD = standard deviation, H_Sh = Shannon entropy, ADR = alpha to delta ratio, REG = regularity, COH = coherence).

Figure 4

Values of the Cerebral Recovery Index (CRI) for the training (A) and test (B) sets. The green and red dots are the median values for patients with good and poor neurologic outcome at each time point; the green and red areas are the corresponding ranges. The grey represents the area where the green and red areas overlap. The fitted recovery functions, R(t) (Equation 9), are given as a solid line. Note that the largest difference between the fitted CRI curves is present between 12 and 24 hours after cardiac arrest.