Electrophysiology testing for the prediction of poor neurological outcome after return of circulation following pediatric cardiac arrest: PLS 4220.03

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 declared an intellectual conflict of interest, and this was acknowledged and managed by the Task Force Chairs/vice chair and Conflict of Interest committees: Scholefield, B, Topjian A were authors and investigator in pediatric post-cardiac arrest neuro-prognostication studies. Data extraction and risk of bias were conducted by other members of the writing group.

CoSTR Citation

Scholefield B, Tijssen J, Ganesan S, Topjian A, Bittencourt Couto T, Acworth J, Atkins D, Guerguerian A on behalf of the International Liaison Committee on Resuscitation Pediatric Life Support Task Force. Electrophysiology testing for the prediction of survival with poor neurological outcome after return of circulation following pediatric cardiac arrest Consensus on Science with Treatment Recommendations [Internet] Brussels, Belgium: International Liaison Committee on Resuscitation (ILCOR) Pediatric Life Support Task Force, 2025 XXXX. 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 tests for predicting good neurological outcome after pediatric cardiac arrest (Scholefield, 2021, PROSPERO CRD42021279221) conducted by the members of the PLS TF with involvement of clinical content experts. This review has been updated and reevaluated for the current systematic review on poor neurological outcome. Evidence for pediatric literature was sought and considered by the Pediatric Life Support Task Force.

Systematic Review

Scholefield B et al. Electrophysiology testing for the prediction of survival with poor neurological outcome after return of circulation following pediatric cardiac arrest (in preparation)

PICOST

The PICOST

Population: This review is studying children (<18 years) who achieve a return of spontaneous or mechanical circulation (ROC) after resuscitation from in-hospital cardiac arrest (IHCA) and out-of-hospital (OHCA), from any cause.

Studies which include newborn infants or patients in hypoxic coma from causes other than cardiac arrest (e.g., respiratory arrest, toxidromes, drowning, hanging) were excluded, except when a subpopulation of cardiac arrest patients can be evaluated separately.

Intervention: Electrophysiology testing : These included surface bioelectrical recordings from the central nervous system such as electroencephalogram (EEG) and evoked potentials (EPs) (e.g., brainstem auditory evoked potentials, and short-latency somatosensory evoked potentials (SSEPs)). We included studies of the interpretation of raw signals or summary measures derived from processed EEG signals such as amplitude-integrated EEG (aEEG), quantitative EEG (qEEG) or bispectral index (BIS). Index prognostic tests, recorded less than 12 hours, 12 to <24 hours, 24 to <48 hours, 48 to <72 hours, 72 hours to <7 days, and/or 7 to 10 days after cardiac arrest.

Comparators: There was no control group for intervention/exposure. The accuracy of the prognostic index test was assessed by comparing the predicted outcome with the final outcome, which represents the comparator.

Outcomes: Primary outcome of interest is survival with poor neurological outcome. We defined poor neurological outcome as a Pediatric Cerebral Performance Category (PCPC) score of >3, or Vineland Adaptive Behavioural scale-II < 70. PCPC score ranges 1 (normal), 2 (mild disability), 3 (moderate disability), 4 (severe disability), 5 (coma), and 6 (brain death). We separately report studies defining poor neurological outcomes with other assessment tools, or as a PCPC score >2, or change in PCPC score from baseline >2.

Study Designs: RCTs and nonrandomized studies (nonrandomized controlled trials, interrupted time series, controlled before-and-after studies, cohort studies) were eligible for inclusion. Case series were considered if greater than 5 cases were reported. Unpublished studies (eg, conference abstracts, trial protocols) and animal studies were excluded. We selected studies where the sensitivity and FPR of the prognostic (index) test were reported.

Timeframe: All years and all languages were included as long as there was an English abstract. Literature search updated to Aug 24^th 2024.

PROSPERO Registration CRD42021279221

Consensus on Science

Introduction

The systematic review identified studies reporting the presence (or absence) of features on electrophysiology monitoring (eg seizures, status epilepticus, abnormal background EEG patterns, reactivity) for predicting survival with poor neurological outcome following cardiac arrest; however, only some used the accepted American Clinical Neurophysiology Society (ACNS) definition of seizures and EEG indices.

We defined poor neurological outcome prediction as imprecise when the false positive rate (FPR) was >1%. However, there is no universal consensus on what the acceptable limits for imprecision should be in prediction for infants and children after cardiac arrest. We defined the reliability of the evidence as reliable if the FPR was <1% and the upper 95% confidence intervals <10%) and moderately reliable if FPR was <1% without a restriction on width of 95% confidence interval.

A low false positive rate means that a low proportion of patients, predicted to have a poor outcome will have a falsely pessimistic prediction (test predicted a poor outcome, but patient went on to have a good outcome). The task force felt that when focused on accuracy of predicting a poor outcome - a low false positive rate (e.g. <1%) is more desirable to avoid falsely pessimistic prediction than a high sensitivity. The cut off value of <1% FPR (equivalent to 99% specificity) was chosen as the consequences of false pessimism are substantial. False pessimism may result in discontinuation of life sustaining therapy in a patient who will eventually have a good outcome.

Results

The overall quality of evidence was rated as very low for all outcomes primarily due to a very serious risk of bias, assessed using the QUIPS tool. The individual studies were all at a moderate to high risk of bias due to confounding. Because of this and a high degree of heterogeneity, no meta-analyses could be performed.

Presence of clinical or electrographic seizure

Fourteen studies reported the relationship between presence of clinical and/or electrographic seizures in children post-cardiac arrest and poor neurological outcomes at PICU/hospital discharge, 6 months and 12 month.(Bach 2024 e209134, Brooks 2018 324-329, Ducharme-Crevier 2017 452-460, Fung 2019 349-357, Kirschen 2021 e719-e731, Lin 2019 534, Mazzio 2024 110271, Meert 2019 393-402, Moler 2017 318-329, Moler 2015 1898-1908, Ostendorf 2016 667-676, Smith 2022 91-100, Topjian 2016 547-557, Yang 2019 223) These studies included 1165 children, of which 6/12 studies reported using the ACNS criteria.(Bach 2024 e209134, Ducharme-Crevier 2017 452-460, Fung 2019 349-357, Mazzio 2024 110271, Ostendorf 2016 667-676, Yang 2019 223)

Presence of seizures between 4-6 hours and 24 hours post-ROC was reported in 10 studies and had a FPR of 0-20% and a sensitivity of 2-38% for predicting poor neurological outcome. Three studies had a FPR <1% but with wide 95%CI.(Fung 2019 349-357, Mazzio 2024 110271, Ostendorf 2016 667-676) At 48 hours and onwards only 3/11 studies reported a FPR for predicting poor outcome of <10%,(Kirschen 2021 e719-e731, Meert 2019 393-402, Ostendorf 2016 667-676) the majority reported an imprecise FPR 19-50%. Overall presence of seizures was not a reliable prognostic test for poor outcome prediction; although early (≤24hours) had improved accuracy compared to ≥48hours.

Presence of status epilepticus on EEG

Presence of status epilepticus was reported in five studies including 299 children. (Fung 2019 349-357, Oualha 2013 1306-1312, Smith 2022 91-100, Topjian 2016 547-557, Yang 2019 223) Poor neurological outcome at PICU/hospital discharge were predicted with a low FPR of 0-5% (upper limit of 95%CI ranged 13-41%) and sensitivity was 9-25%. Presence of status epilepticus had moderate reliability as a prognostic test.

Presence of myoclonic status epilepticus on EEG

In two studies, including 61 patients, myoclonic status epilepticus was identified in 8 patients. Presence of myoclonic status epilepticus on EEG predicted poor neurological outcomes with a FPR 0% (95% CI 0-34%) and sensitivity of 17-21% at PICU/hospital discharge.(Brooks 2018 324-329, Ostendorf 2016 667-676) Status myoclonus on EEG had moderate reliability as a prognostic test although there was a very small sample size.

Absence of continuous or normal background EEG

The absence of a normal/continuous EEG background pattern (defined as normal, continuous and reactive, continuous and unreactive, and nearly continuous by ACNS definitions(Hirsch 2021 1-29)) was reported in 14 studies at 6 different time points, and included 563 patients.(Bach 2024 e209134, Brooks 2018 324-329, Ducharme-Crevier 2017 452-460, Fink 2014 664-674, Fung 2019 349-357, Kessler 2011 37-43, Kirschen 2021 e719-e731, Mazzio 2024 110271, Ostendorf 2016 667-676, Oualha 2013 1306-1312, Smith 2022 91-100, Topjian 2016 547-557, Topjian 2021 282-288, Yang 2019 223) There was a wide variability of FPR and sensitivity reported across all timepoints for predicting poor neurological outcome. Only 4/14 studies identified a FPR <10%. The range of FPR across studies was 0-90%. Sensitivity ranged 7 to 96% with 4 studies having a sensitivity >90%. Overall, absence of a continuous EEG was an inaccurate and unreliable method for predicting poor neurological outcome.

Absence of attenuated, isoelectric or flat EEG background

The absence of an attenuated, isoelectric, or flat EEG was reported in 12 studies including up to 526 patients (although there was a risk of some patients appearing in multiple studies).(Bach 2024 e209134, Brooks 2018 324-329, Ducharme-Crevier 2017 452-460, Fink 2014 664-674, Fung 2019 349-357, Kessler 2011 37-43, Kirschen 2021 e719-e731, Mazzio 2024 110271, Ostendorf 2016 667-676, Oualha 2013 1306-1312, Smith 2022 91-100, Topjian 2016 547-557, Topjian 2021 282-288, Yang 2019 223) In 7/9 studies, which reported prediction of poor neurological at 24 hours to 6 days, there was a FPR <10% (95%CI upper limit ranges 4-52%) and sensitivity of 18-58%.(Bach 2024 e209134, Brooks 2018 324-329, Ducharme-Crevier 2017 452-460, Kessler 2011 37-43, Kirschen 2021 e719-e731, Mazzio 2024 110271, Ostendorf 2016 667-676) In 4/9 studies, the FPR was <1% (95%CI upper limit ranges 4-52%).(Brooks 2018 324-329, Ducharme-Crevier 2017 452-460, Mazzio 2024 110271, Ostendorf 2016 667-676) At time points earlier than 24 hours, FPR was much higher (ranged 10-90%).(Fung 2019 349-357, Kessler 2011 37-43, Topjian 2016 547-557, Topjian 2021 282-288) Therefore, the absence of an attenuated, isoelectric, or flat EEG FPR was imprecise (at the FPR<1% cut off) in more than 50% of included studies to predict a poor neurological outcome.

Presence of burst suppression, burst attenuation or generalized periodic epileptiform discharges (GPEDS) on EEG background

Presence of burst suppression, burst attenuation or GPEDS was reported in 7 studies including 395 patients.(Bach 2024 e209134, Brooks 2018 324-329, Fung 2019 349-357, Ostendorf 2016 667-676, Topjian 2016 547-557, Topjian 2021 282-288, Yang 2019 223) Prior to 24 hours, in 4 studies, the FPR ranged 0-19% and sensitivity 9-30%. From 24 hours onwards, the accuracy improved. A FPR <1% (95%CI upper limit range 16-54%) was reported in 3 of 4 studies at 24, 48 and 72 hours with a sensitivity of 0-67%.(Brooks 2018 324-329, Oualha 2013 1306-1312, Yang 2019 223) Therefore, prediction of poor neurological outcome was moderately reliable from 24 to 72 hours.

Absence of reactivity, sleep II architecture or sleep spindles, or variability on EEG

The absence of reactivity within an EEG trace was reported in 3 studies,(Ostendorf 2016 667-676, Topjian 2016 547-557, Yang 2019 223) absence of sleep II architecture in 2 studies,(Ducharme-Crevier 2017 452-460, Topjian 2021 282-288) and absence of variability in 2 studies.(Ostendorf 2016 667-676, Topjian 2016 547-557) No test had a prediction accuracy with a FPR <1%. Absence of reactivity had a FPR 0-93%, and sensitivity 36-100%; absence of sleep II architecture had a FPR 20-43%, and sensitivity 84-92%; absence of variability in EEG had FPR 0-80% and sensitivity 21 to 82% for poor neurological outcome prediction. These were inaccurate and unreliable tests for poor outcome prediction.

Quantitative EEG scoring

A composite score assessing EEG background from a 24-hour monitoring period, obtained from quantitative EEG using the amplitude integrated EEG trace, was assessed in only one study which included 30 patients.(Bourgoin 2020 248-255) A score of >15 had a predicted FPR of 6% (95%CI 0-27%) and sensitivity of 33% for poor neurological outcome.

Somatosensory evoked potential (SSEPs)

SSEPs, evaluating bilateral absence of N20 waves, were reported in only one study, with a small sample size (n=12), reporting poor neurological outcome (PCPC >3) at 24, 48 and 72 hours.(McDevitt 2021 30-35) Clinicians were blinded to test results and the SSEP assessor was blinded to outcome. The predicted FPR was 0% (95%CI 0-52%) at 24 and 48 hours and 17% at 72 hours, with a sensitivity of 100% (95%CI 29-100) at all time points. Absence of N20 response on SSEP was moderately reliable to predict poor neurological outcome, but only assessed in one small study.

Treatment Recommendations

• We recommend that no single electrophysiology test be used in isolation to predict poor neurological outcome in children after cardiac arrest at any time point (strong recommendation, very-low certainty evidence).

• Clinicians should consider using multiple tests in combination for poor neurological outcome prediction (good practice statement).

• The presence of status epilepticus between 24-72 hours after ROC, presence of burst suppression, burst attenuation or GPEDs between 24-72 hours after ROC, all had moderate reliability and may be considered as part of multi-modal testing to predict poor neurological outcome in children after cardiac arrest (good practice statement).

• We suggest against using the following EEG features for predicting poor neurological outcome: presence of clinical or electrographic seizures; absence of sleep spindle and sleep II architecture on EEG, continuous or normal background EEG, EEG reactivity and EEG variability, at any time point (weak recommendation, very-low-certainty evidence).

• There was insufficient evidence to make a recommendation for or against the use of presence of attenuated, isoelectric or flat EEG, absence of N20 response on SSEPs, presence of myoclonic status epilepticus, or quantitative EEG score to predict poor neurological outcome in children after cardiac arrest at any time point.

Justification and Evidence to Decision Framework Highlights

• The available scientific evidence had a high risk of bias based on high heterogeneity across studies, small number of studies and small number of patients included in addition to lack of blinding, variation in test assessment and performance, and variability in outcome measurement. Therefore, no meta-analysis was performed. Overall assessment of test performance was based on visual assessment of forest plots.

• In addition to providing prognostic information, electrophysiology monitoring may allow identification of reversible events e.g. seizures. Treatment of seizures may prevent additional secondary injury following a hypoxic-ischemic insult. The role of electrophysiology monitoring was not assessed for this purpose.

• If only one study was available (with small patient sample size) then a suggestion or recommendation could not be made.

• There was limited or no context of when tests were undertaken in relation to concurrent pharmacological exposure, sedation and ongoing treatment (e.g., TTM) in patients following cardiac arrest.

• American Clinical Neurophysiology Society (ACNS) definitions for seizures and EEG indices were followed in some studies. EEG and SSEP prognostic criteria require clear and reproducible definitions and require validation in the pediatric ICU environment.

• The complex interpretation of normality in background EEG patterns in preterm and term infants, and the impact of brain maturation on EEG patterns in infancy and childhood, requires expert neurophysiology input. Studies reported limited information on handling of this area and further refinement of definitions and application of recommendation are required.

• SSEPs have a high level of precision in adult studies of neuro-prognostication in comatose patients after cardiac arrest.(Sandroni 2020 1803-1851) The Task Force recognizes the lack of available data in children and strongly encourages further multi-centre evaluation.

Knowledge Gaps

• Electrophysiology tests for prognostication after cardiac arrest appear promising but more research is required in infants and children.

• More research is required on type of monitoring, intermittent or continuous EEG, use of reduced channel monitoring, quantitative EEG systems, duration and timing of prognostic assessment.

• ● Validation of ACNS or other international definitions of EEG indices within the pediatric ICU environment for infants and children after cardiac arrest.

• Further work on multi-modal prognostication, timing, definitions of testing, accurate outcome timing and definition.

• We encourage wider research and consultation with patients, children, parents, guardians and caregivers, health care professionals and members of the wider society on understanding survivorship after pediatric cardiac arrest to inform correct definitions and framework of good neurological outcome for prediction research.

ETD summary table: PLS 4220 03 Posr ROC Poor EEG background pattern ETD; PLS 4220 03 Posr ROC Poor EEG reactive variable sleep q EEG SSEP ETD; PLS 4220 03 Posr ROC Poor EEG seizures status epilepcticus status myoclonus ETD

References

Bach AM, Kirschen MP, Fung FW, Abend NS, Ampah S, Mondal A, et al. Association of EEG Background With Diffusion-Weighted Magnetic Resonance Neuroimaging and Short-Term Outcomes After Pediatric Cardiac Arrest. Neurology. 2024;102(5)e209134.

Bourgoin P, Barrault V, Joram N, Leclair Visonneau L, Toulgoat F, Anthoine E, et al. The prognostic value of early amplitude-integrated electroencephalography monitoring after pediatric cardiac arrest. Pediatric Critical Care Medicine. 2020248-255.

Brooks GA, Park JT. Clinical and Electroencephalographic Correlates in Pediatric Cardiac Arrest: Experience at a Tertiary Care Center. Neuropediatrics. 2018;49(5)324-329.

Ducharme-Crevier L, Press CA, Kurz JE, Mills MG, Goldstein JL, Wainwright MS. Early presence of sleep spindles on electroencephalography is associated with good outcome after pediatric cardiac arrest. Pediatric Critical Care Medicine. 2017;18(5)452-460.

Fink EL, Berger RP, Clark RSB, Watson RS, Angus DC, Richichi R, et al. Serum biomarkers of brain injury to classify outcome after pediatric Cardiac Arrest*. Critical Care Medicine. 2014;42(3)664-674.

Fung FW, Topjian AA, Xiao R, Abend NS. Early EEG Features for Outcome Prediction After Cardiac Arrest in Children. Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society. 2019;36(5)349-357.

Hirsch LJ, Fong MWK, Leitinger M, LaRoche SM, Beniczky S, Abend NS, et al. American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2021 Version. J Clin Neurophysiol. 2021;38(1)1-29.

Kessler SK, Topjian AA, Gutierrez-Colina AM, Ichord RN, Donnelly M, Nadkarni VM, et al. Short-term outcome prediction by electroencephalographic features in children treated with therapeutic hypothermia after cardiac arrest. Neurocrit Care. 2011;14(1)37-43.

Kirschen MP, Licht DJ, Faerber J, Mondal A, Graham K, Winters M, et al. Association of MRI brain injury with outcome after pediatric out-of-hospital cardiac arrest. Neurology. 2021;96e719-e731.

Lin JJ, Lin YJ, Hsia SH, Kuo HC, Wang HS, Hsu MH, et al. Early Clinical Predictors of Neurological Outcome in Children With Asphyxial Out-of-Hospital Cardiac Arrest Treated With Therapeutic Hypothermia. Front Pediatr. 2019;7534.

Mazzio EL, Topjian AA, Reeder RW, Sutton RM, Morgan RW, Berg RA, et al. Association of EEG characteristics with outcomes following pediatric ICU cardiac arrest: A secondary analysis of the ICU-RESUScitation trial. Resuscitation. 2024;201110271.

McDevitt WM, Rowberry TA, Davies P, Bill PR, Notghi LM, Morris KP, et al. The Prognostic Value of Somatosensory Evoked Potentials in Children After Cardiac Arrest: The SEPIA Study. J Clin Neurophysiol. 2021;38(1)30-35.

Meert KL, Guerguerian AM, Barbaro R, Slomine BS, Christensen JR, Berger J, et al. Extracorporeal Cardiopulmonary Resuscitation: One-Year Survival and Neurobehavioral Outcome among Infants and Children with In-Hospital Cardiac Arrest*. Critical Care Medicine. 2019;47(3)393-402.

Moler FW, Silverstein FS, Holubkov R, Slomine BS, Christensen JR, Nadkarni VM, et al. Therapeutic Hypothermia after In-Hospital Cardiac Arrest in Children. New England Journal of Medicine. 2017;376(4)318-329.

Moler FW, Silverstein FS, Holubkov R, Slomine BS, Christensen JR, Nadkarni VM, et al. Therapeutic hypothermia after out-of-hospital cardiac arrest in children. New England Journal of Medicine. 2015;372(20)1898-1908.

Ostendorf AP, Hartman ME, Friess SH. Early electroencephalographic findings correlate with neurologic outcome in children following cardiac arrest. Pediatric Critical Care Medicine. 2016;17(7)667-676.

Oualha M, Gatterre P, Boddaert N, Dupic L, De Saint Blanquat L, Hubert P, et al. Early diffusion-weighted magnetic resonance imaging in children after cardiac arrest may provide valuable prognostic information on clinical outcome. Intensive Care Medicine. 2013;39(7)1306-1312.

Sandroni C, D'Arrigo S, Cacciola S, Hoedemaekers CWE, Kamps MJA, Oddo M, et al. Prediction of poor neurological outcome in comatose survivors of cardiac arrest: a systematic review. Intensive Care Med. 2020;46(10)1803-1851.

Smith AE, Ganninger AP, Mian AY, Friess SH, Guerriero RM, Guilliams KP. Magnetic resonance imaging adds prognostic value to EEG after pediatric cardiac arrest. Resuscitation. 2022;17391-100.

Topjian AA, Sanchez SM, Shults J, Berg RA, Dlugos DJ, Abend NS. Early Electroencephalographic Background Features Predict Outcomes in Children Resuscitated from Cardiac Arrest. Pediatric Critical Care Medicine. 2016;17(6)547-557.

Topjian AA, Zhang B, Xiao R, Fung FW, Berg RA, Graham K, et al. Multimodal monitoring including early EEG improves stratification of brain injury severity after pediatric cardiac arrest. Resuscitation. 2021;167282-288.

Yang D, Ryoo E, Kim HJ. Combination of early EEG, brain CT, and ammonia level is useful to predict neurologic outcome in children resuscitated from cardiac arrest. Frontiers in Pediatrics. 2019;7223.