Conflict of Interest Declaration
The ILCOR Continuous Evidence Evaluation process is guided by a rigorous ILCOR Conflict of Interest policy. The following Task Force members and other authors were recused from the discussion as they declared a conflict of interest: none applicable.
The following Task Force members and other authors declared an intellectual conflict of interest, and this was acknowledged and managed by the Task Force Chairs and Conflict of Interest committees: Claudio Sandroni, Karen Hirsch, Jerry Nolan and Jasmeet Soar were coauthors of the systematic review used for adolopment. They did not participate in assessment of the systematic review for quality for adolopment.
Hirsch K, Sandroni C, Skrifvars M, Humaloja J, on behalf of the ILCOR ALS Task Force, EEG for prediction of good neurologic outcome, Consensus on Science with Treatment Recommendations. Brussels, Belgium: International Liaison Committee on Resuscitation (ILCOR) Advanced Life Support Task Force, 2022 December 15. Available from: http://ilcor.org
Methodological Preamble and Link to Published Systematic Review
The continuous evidence evaluation process for the production of Consensus on Science with Treatment Recommendations (CoSTR) started with a systematic review of prognostication after cardiac arrest (PROSPERO: CRD 420 1914 1169). This review was conducted by a systematic review team with the involvement of clinical content experts from the ILCOR ALS Task Force and consisted of two parts. The first part was about prediction of poor neurological outcome and provided evidence for the 2021 ILCOR CoSTR. The second part was about prediction of good neurological outcome and it was completed after the publication of the 2021 ILCOR CoSTR. The two parts of this review have been published separately in 2020 and 2021 respectively (Sandroni C et al, DOIs 10.1007/s00134-020-06198-w and 10.1007/s00134-022-06618-z, respectively). As the systematic review of prognostication of favorable outcome was recent and met ILCOR criteria for being of sufficient quality, the TF deemed it appropriate to use the adolopment process for systematic reviews. Additionally, an updated search including the dates October 31, 2021- May 20, 2022 was conducted to capture any papers published since the search for the original systematic review. Task force members screened and selected all newly identified papers, extracted data and performed bias assessment using the QUIPS tool, which was also used in the original systematic review. The totality of this identified evidence was considered by the Advanced Life Support task force, and used to determine the certainty of evidence and formulate the Consensus on Science and Treatment Recommendations.
For assessment of prognostic accuracy, in the previous 2021 ILCOR CoSTR we considered a test result predicting a poor outcome as a positive result. In the present review, we considered a test result predicting a good outcome as a positive result. Therefore, sensitivity is the proportion of patients with good neurological outcome correctly identified by a positive test result, while specificity is the proportion of patients with poor neurological outcome correctly identified by a negative test result. A high specificity corresponds to a low incidence of falsely optimistic predictions.
The present review aimed to identify patients with the highest likelihood of achieving neurological recovery. Therefore, for predictors whose results are expressed as a continuous variable in a spectrum of values, e.g., the blood values of a biomarker, a positive test result corresponded to test values within the normal range; for predictors whose results are expressed as a categorical variable, e.g., the presence of specific EEG patterns, a positive test result corresponds to the result categories that are closer to normality. We did not include in this review the predictors whose results are dichotomised in only two categories, e.g., present vs. absent pupillary light reflex, because their accuracy for prediction of good outcome corresponds to the inverse of their accuracy for prediction of poor outcome (i.e., the specificity for prediction of good neurological outcome corresponds to the sensitivity for prediction of poor neurological outcome, and vice versa). Therefore, the accuracy of these indices to predict good outcome was already indirectly reported in the previous 2021 ILCOR CoSTR on prediction of poor neurological outcome.
The present chapter of the 2022 CoSTR deals with prediction of good neurological outcome based on electrophysiology and specifically electroencephalogram (EEG) studies.
(Sandroni C, D'Arrigo S, Cacciola S, Hoedemaekers CWE, Westhall E, Kamps MJA, Taccone FS, Poole D, Meijer FJA, Antonelli M, Hirsch KG, Soar J, Nolan JP, Cronberg T. Prediction of good neurological outcome in comatose survivors of cardiac arrest. A systematic review. DOI: 10.1007/s00134-022-06618-z.)
Population: Adults (≥16 y) who are comatose after resuscitation from cardiac arrest (either in-hospital or out-of-hospital), regardless of target temperature.
Intervention: Various EEG modalities assessed within one week from cardiac arrest.
Outcomes: Prediction of good neurological outcome defined as Cerebral Performance Categories (CPC) 1-2 or modified Rankin Score (mRS) 0-3 from ICU discharge to 6 months after cardiac arrest. CPC 1-3 or mRS 1-4 was accepted as an indirect outcome.
Study Designs Prognostic accuracy studies where the 2 x 2 contingency table (i.e., the number of true/false negatives and positives for prediction of poor outcome) was reported, or where those variables could be calculated from reported data, are eligible for inclusion. Unpublished studies, reviews, case reports, case series, studies including less than 10 patients, letters, editorials, conference abstracts, and studies published in abstract form were excluded.
Timeframe: In 2015 and 2020, ILCOR evidence reviews identified four categories of predictors of poor neurological outcome after cardiac arrest, namely clinical examination, biomarkers, electrophysiology, and imaging. However, the prediction of good neurological outcome has never been systematically reviewed to date.
In the systematic review DOI: 10.1007/s00134-022-06618-z we searched studies published from January 1, 2001 to October 13, 2021. We updated this review for this CoSTR. Our last search was on20/05/2022.
Consensus on Science
The original systematic review identified 37 studies on prediction of good neurological outcome, of which 24 investigated EEG and EEG-derived measures such as Bispectral index (BIS). The updated review identified one additional study that investigated EEG reactivity. This study was not included as detailed information about baseline EEG (background, voltage, etc) was not described.
Twenty studies were judged to be at moderate risk of bias and four at high risk of bias.
The overall certainty of evidence for the use of the separate EEG modalities included was very low. Most studies had a moderate risk of bias, due to lack of blinding that may have caused a self-fulfilling prophecy. Imprecision was also an issue, due to the small sample size and the heterogeneity in definitions and timing of assessment, which prevented pooling. None of the included predictors had the maximum 1% rate of falsely optimistic prediction that most clinicians would consider as appropriate based on a survey conducted in 2019 (Steinberg, 2019, 190). However, the panel also considered that achieving a 0% false positive rate with narrow confidence intervals when predicting good outcome is less important than when predicting poor outcome, since good outcome predictors are not used to withdraw life sustaining treatment. Several studies also did not report on use of medications that can impact EEG background continuity and voltage. Finally, there was inconsistency in the definitions of some EEG patterns. A minority of studies did not adopt the American Clinical Neurophysiology Society (ACNS) definitions.
Continuous or nearly continuous background without discharges (ACNS)
Twelve studies (Admiraal, 2019 17; Backman 2018 24; Beretta, 2019 106374; Carrai, 2016; Carrai, 2021; Duez, 2019 145; Hofmeijer, 2015 137; Rossetti, 2017 e674; Scarpino, 2021 162; Sivaraju 2015 1264; Sondag, 2017 111; Westhall, 2016 1482) investigated the ability of a favorable EEG pattern recorded during the first five days after ROSC to predict good outcome. The favorable EEG pattern consisted of a continuous or nearly continuous background without superimposed abundant/generalized periodic discharges or seizures. In all 12 studies, the 2012 American Clinical Neurophysiology Society (ACNS) standardized terminology for use in critical care was adopted, or the pattern definitions were consistent with ACNS.
The criteria for both the superimposed discharges and the background varied slightly across studies.
In six studies (Admiraal, 2019 17; Backman, 2018 24; Duez, 2019 145; Hofmeijer, 2015 137; Sondag, 2017 111; Westhall, 2016 1482), the definition of background was consistent (continuous or nearly continuous, normal voltage), with minor variations (see below), while criteria for superimposed discharges were different: four of these studies (Admiraal 2019 17; Backman 2018 24; Duez 2019 145; Westhall 2016 1482) used the absence of abundant (> 50% of the record) periodic discharges or abundant spike-wave as a criterion (definition A1a in the systematic review), while two studies (Duez, 2019 145; Hofmeijer 2015 137; Sondag, 2017 111) used the absence of generalized periodic discharges as a criterion (definition A1b in the systematic review). One study (Duez, 2019 145) assessed the predictive value of both criteria.
Two of the four studies using definition A1a (Backman 2018 24; Westhall 2016 1482) used an additional criterion (normal anteroposterior EEG gradient) to define a favorable pattern. Two of these four studies (Westhall, 2016 1482; Duez, 2019 145) also investigated a more restrictive definition by further adding reactivity.
Concerning background, besides the continuous or nearly continuous normal voltage, four studies (Carrai, 2016 940; Carrai, 2021 133; Rossetti 2017 e674; Scarpino 2021 162) included a low-voltage background among favorable EEGs (definition A2 in the systematic review). In one of these studies [Rossetti, 2017 e674], reactivity was required to define EEG as favorable. Two studies (Beretta 2019 106374; Sivaraju 2015 1264), included a discontinuous background (definition A3 in the systematic review), provided that the voltage was normal [Sivaraju 2015 1264] or that the background was reactive (Beretta 2019 106374).
In the six studies using the A1a and A1b definitions (continuous or nearly continuous, normal-voltage background with no abundant/generalized periodic discharges or seizures), sensitivity and specificity for good outcome prediction ranged from 51 to 63% and from 82% to 88% at 12h, respectively. At 24h, these were 39–78% and 67–100%. The highest specificities for good outcome (90-100%) were observed in studies using the most restrictive definition of favorable EEG (A1a, reactive, normal gradient).
In studies assessing the EEG at multiple time points (Admiraal, 2019 17; Duez, 2019 145; Hofmeijer 2015 137) the specificities decreased, and the sensitivities increased over time.
In the four studies using the A2 definition [Carrai, 2021, 133; Carrai, 2016, 940; Scarpino 2021, 162; Rossetti, 2017, e674], at an early time window (<6 hours to 24 h after ROSC), specificities ranged between 87% to 98% and sensitivities ranged between 57% to 100% [Carrai, 2021, 133; Carrai 2016, 940; Scarpino, 2021, 162] At a later time window (48–72 h after ROSC) in two studies (Carrai 2016 940; Rossetti 2017 e674) specificities were 83% and sensitivities 91% and 100%.
In one of the two studies [Beretta, 2019, 106374] using the A3 definition the specificity to predict good outcome was 77% (sensitivity 77%) at 0-5 days from ROSC. In the other study [Sivaraju, 2015, 1264] the specificity for good outcome was 97% (sensitivity 72) within 72 hours after ROSC. This specificity decreased remarkably (84%) if the EEG record included discharges.
Other EEG patterns or grading scales
A heterogeneous group of EEG patterns was described in three studies [Alvarez, 2015 128; Lamartine, 2016 153; Leao, 2015 322] that did not use the ACNS terminology. In these studies, the favorable EEG was described as continuous or nearly continuous background with excessive slow (theta and delta) activity with spontaneous variability and/or reactivity [Lamartine 2016 153], or theta activity [Leao 2015 322] or continuous background, not further defined [Alvarez 2015 128]. None of these studies excluded EEGs with superimposed discharges from favorable patterns. All three studies assessed EEG within approximately 24–48 h after CA, and the specificities to predict good outcome ranged between 68% and 91% (sensitivities from 75% to 96%). Specificity was lower for later assessments.
EEG – Continuous background assessed via reduced montage and/or amplitude integrated EEG
Five studies [Wennervirta, 2009 2427; Jang, 2019 142; Oh, 2013 200; Rundgren, 2010 1838; Eertmans, 2019 139] investigated the predictive value of a continuous normal-voltage background defined from quantitative trend analysis using amplitude-integrated EEG (aEEG) [Jang, 2019 142; Oh, 2013 200] or original EEG with reduced electrode montages [Rundgren, 2010 1838; Wennervirta, 2009 2427] at a time ranging from 6 to 72 h after ROSC.
Two studies [Rundgren, 2010 1838; Wennervirta, 2009 2427] assessed reduced-montage EEG at two time-windows (within 24h and between 24 and 48h after ROSC). Favorable EEG patterns predicted good outcome at earlier time window with specificity 56–96% (sensitivities 53–67%) and at later time-window with specificity of 67–79% (sensitivity 95%).
Two studies [Jang, 2019 142; Oh, 2013 200] investigated aEEG within 72h after ROSC and specificity to predict good outcome on hospital discharge [Oh, 2013 200] was 96% (sensitivity 57%) and at 6 months [Jang, 2019 142] 85% (sensitivity 100%).
One study [Eertmans, 2019 139] analyzed the original EEG tracing of a bispectral index (BIS) monitor recorded between 6 and 48 h from ROSC from four frontotemporal channels. A slow diffuse theta and/or delta activity, as opposed to epileptiform, burst-suppression, or suppression (<5 μV), predicted good neurological outcome with 79% specificity at all time points, with 55%-86% sensitivity.
aEEG results report voltage and continuity but do not directly enable a morphological assessment of the original EEG signals, making identifying superimposed activity difficult unless the original EEG channels are also displayed/reviewed. In one study [Oh, 2013 200] original EEG was reviewed to exclude discharges.
One study [Tjepkema-Cloostermans, 2013, R252] investigated the ability of cerebral recovery index (CRI) to predict good outcome at 6 months after CA. In that study, a CRI above 0.57 at 18 h or 0.69 at 24 h predicted good neurological outcome with 100% specificity (sensitivities 65% [44.3–82.8] and 26% [11.1–46.3] respectively).
Three studies [Park, 2018 59; Seder, 2010 281; Leary, 2010 1133]. the predictive value of bispectral index (BIS). In two studies, a BIS value greater than 21 at 1–3 h [Park, 2018] after ROSC or 24 at 3–6 h after ROSC [Seder, 2010 281] predicted good neurological outcome with 94% [79.8–99.3] and 86% [73.3–94.2] specificity, respectively (sensitivities 88% [61.7–98.4] and 94%, [83.1–98.7] respectively). In one study [Leary, 2010 1133], the ability of BIS to predict good neurological outcome at 24 h from ROSC was assessed at different BIS thresholds. Specificity increased from 41% [25.6–56.7] at BIS 30 to 92.9% [80.5–98.5]at BIS 60. Sensitivities decreased from 95% [75.1–99.9] to 20% 20 [5.7–43.7], respectively.
We suggest using a continuous or nearly continuous normal voltage EEG background without periodic discharges or seizures within 72h from ROSC in combination with other indices to predict good outcome in patients who are comatose after cardiac arrest (weak recommendation, very low-certainty evidence).
There is insufficient evidence to recommend for or against using a low voltage or a discontinuous EEG background on days 0-5 from ROSC to predict good neurological outcome after cardiac arrest (weak recommendation, very low-certainty evidence).
We suggest against using heterogeneous, non-ACNS-defined favorable EEG patterns to predict good neurological outcome after cardiac arrest (weak recommendation, very low-certainty evidence).
We suggest against the use of other EEG metrics, including reduced montage or amplitude integrated EEG, BIS, or EEG-derived indices, to predict good outcome in patients who are comatose after cardiac arrest (weak recommendation, very low-certainty evidence).
We suggest that the American Clinical Electrophysiology Society (ACNS) terminology be used to classify the EEG patterns used for prognostication (good practice statement)
Justification and Evidence to Decision Framework Highlights
In making their recommendation in favor of a continuous or nearly continuous, normal-voltage EEG background without seizures or abundant/generalized periodic discharges as a predictor of good neurological outcome in patients who are comatose after cardiac arrest, the TF members considered the consistency of the evidence (12 studies, mostly with >80% specificity and >50% sensitivity) and the consistency of the definition, made using an ACNS or ACNS-compatible terminology.
The background definition was consistent in six of these studies. Although the criteria for periodic discharges varied slightly within this subgroup, this did not affect the prediction accuracy.
Evidence from the remaining six studies confirmed the ability of a continuous or nearly continuous, normal-voltage EEG background without seizures or discharges to predict good neurological outcome. These studies also included a low-voltage or discontinuous EEG background among the ‘favorable’ EEG patterns. These patterns are farther from normal than a continuous or nearly continuous background, and their accuracy could not be assessed separately. The ILCOR TF considered the evidence supporting these patterns insufficient for recommending their use.
The remaining studies on EEG used definitions of favorable patterns that did not comply with the ACNS terminology. These definitions were highly heterogeneous, preventing the panel from making a recommendation.
In recommending against using non-ACNS-defined favorable EEG patterns to predict good neurological outcome after cardiac arrest, the panel considered the limited evidence and the heterogeneity of pattern definitions.
In recommending against using amplitude-integrated EEG or EEG-derived indices, such as BIS or CRI, the panel considered that these techniques do not allow or allow only a limited morphological assessment of the original EEG signal. Moreover, the evidence was limited to few studies (only one study for CRI).
- ● The effects of sedation and systemic organ dysfunction on the predictive value of the EEG background should be investigated.
- ● The value of low-voltage background and discontinuous reactive/normal voltage background should be investigated
- ● The value of EEG reactivity for predicting good outcome deserves further investigation using standardized stimulation and assessment.
- ● It is not clear which aspect of periodic discharges (ie distribution, morphology, prevalence, etc.) has greatest importance in affecting the prognosis of a favorable EEG pattern.
- ● The value of dominant EEG rhythms (e.g. theta) in prognostication after cardiac arrest deserves investigation.
- ● The predictive value of favorable EEG patterns defined according the 2021 ACNS definitions deserves investigation, albeit the differences vs the 2012 definitions regarding the features used for predicting a good outcome are minimal.
Admiraal MM, van Rootselaar AF, Hofmeijer J, et al. (2019) Electro- encephalographic reactivity as predictor of neurological outcome in postanoxic coma: a multicenter prospective cohort study. Ann Neurol 86:17–27.
Alvarez V, Reinsberger C, Scirica B, O’Brien MH, Avery KR, Henderson G, Lee JW (2015) Continuous electrodermal activity as a potential novel neurophysiological biomarker of prognosis after cardiac arrest—a pilot study. Resuscitation 93:128–135
Backman S, Cronberg T, Friberg H, et al. (2018) Highly malignant routine EEG predicts poor prognosis after cardiac arrest in the target temperature management trial. Resuscitation 131:24–28
Beretta S, Coppo A, Bianchi E, et al. (2019) Neurological outcome of postanoxic refractory status epilepticus after aggressive treatment. Epilepsy Behav E&B 101:106374
Carrai R, Spalletti M, Scarpino M, et al. (2021) Are neurophysiologic tests reliable, ultra-early prognostic indices after cardiac arrest? Neurophysiol Clin (Clinical neurophysiology) 51:133–144
Carrai R, Grippo A, Scarpino M, et al. (2016) Time- dependent and independent neurophysiological indicators of prognosis in post-anoxic coma subjects treated by therapeutic hypothermia. Minerva Anestesiol 82:940–949
Duez CHV, Johnsen B, Ebbesen MQ, Kvaloy MB, Grejs AM, Jeppesen AN, Soreide E, Nielsen JF, Kirkegaard H (2019) Post resuscitation prognostica- tion by EEG in 24 vs 48h of targeted temperature management. Resusci- tation 135:145–152
Eertmans W, Genbrugge C, Haesen J, Drieskens C, Demeestere J, Vander Laenen M, Boer W, Mesotten D, Dens J, Ernon L, Jans F, De Deyne C (2019) The prognostic value of simplified EEG in out-of-hospital cardiac arrest patients. Neurocrit Care 30:139–148
Hofmeijer J, Beernink TM, Bosch FH, et al., (2015) Early EEG contributes to multimodal outcome prediction of postanoxic coma. Neurology 85:137–143
Jang J, Oh SH, Nam Y, Lee K, Choi HS, Jung SL, Ahn KJ, Park KN, Kim BS (2019) Prognostic value of phase information of 2D T2*-weighted gradient echo brain imaging in cardiac arrest survivors: A preliminary study. Resuscitation 140:142–149
Lamartine Monteiro M, Taccone FS, Depondt C, Lamanna I, Gaspard N, Ligot N, Mavroudakis N, Naeije G, Vincent JL, Legros B (2016) The prognostic value of 48-h continuous EEG during therapeutic hypothermia after cardiac arrest. Neurocrit Care 24:153–162
Leao RN, Avila P, Cavaco R, Germano N, Bento L (2015) Therapeutic hypothermia after cardiac arrest: outcome predictors. Revista Brasileira de terapia intensiva 27:322–332
Leary M, Fried DA, Gaieski DF, Merchant RM, Fuchs BD, Kolansky DM, Edelson DP, Abella BS (2010) Neurologic prognostication and bispectral index monitoring after resuscitation from cardiac arrest. Resuscitation 81:1133–1137
Oh SH, Park KN, Kim YM, Kim HJ, Youn CS, Kim SH, Choi SP, Kim SC, Shon YM (2013) The prognostic value of continuous amplitude-integrated electroencephalogram applied immediately after return of spontaneous circulation in therapeutic hypothermia-treated cardiac arrest patients. Resuscitation 84:200–205
Park JH, Oh JH, Choi SP, Wee JH (2018) Neurologic outcome after out-of- hospital cardiac arrest could be predicted with the help of bispectral- index during early targeted temperature management. Scand J Trauma Resusc Emerg Med 26:59
Rossetti AO, Tovar Quiroga DF, Juan E, et al. (2017) Electroencephalography pre- dicts poor and good outcomes after cardiac arrest: a two-center study. Crit Care Med 45:e674–e682
Rundgren M, Westhall E, Cronberg T, Rosen I, Friberg H (2010) Continuous amplitude-integrated electroencephalogram predicts outcome in hypothermia-treated cardiac arrest patients. Crit Care Med 38:1838–1844
Scarpino M, Lolli F, Lanzo G, et al. (2021) SSEP amplitude accurately predicts both good and poor neurological outcome early after cardiac arrest; a post-hoc analysis of the ProNeCA multicentre study. Resuscitation 163:162–171
Sivaraju A, Gilmore EJ, Wira CR, et al. (2015) Prognostication of post-cardiac arrescoma: early clinical and electroencephalographic predictors of outcome.Intensive Care Med 41:1264–1272
Seder DB, Fraser GL, Robbins T, Libby L, Riker RR (2010) The bispectral index and suppression ratio are very early predictors of neurological outcome during therapeutic hypothermia after cardiac arrest. Intensive Care Med 36:281–288
Sondag L, Ruijter BJ, Tjepkema-Cloostermans MC, et al. (2017) Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis. Crit Care 21:111
Tjepkema-Cloostermans MC, van Meulen FB, Meinsma G, van Putten MJ (2013) A Cerebral Recovery Index (CRI) for early prognosis in patients after cardiac arrest. Crit Care 17:R252
Wennervirta JE, Ermes MJ, Tiainen SM, Salmi TK, Hynninen MS, Sarkela MO, Hynynen MJ, Stenman UH, Viertio-Oja HE, Saastamoinen KP, Pettila VY, Vakkuri AP (2009) Hypothermia-treated cardiac arrest patients with good neurological outcome differ early in quantitative variables of EEG suppression and epileptiform activity. Crit Care Med 37:2427–2435
Westhall E, Rossetti AO, van Rootselaar AF, Wesenberg Kjaer T, et al. (2016) Standardized EEG inter- pretation accurately predicts prognosis after cardiac arrest. Neurology 86:1482–1490