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Objectives:To define the frequency and characteristics of acute neurologic complications in children hospitalised with infective endocarditis and to identify risk factors for neurologic complications.Study Design:Retrospective cohort study of children aged 0–18 years hospitalised at a tertiary children’s hospital from 1 January, 2008 to 31 December, 2017 with infective endocarditis.Results:Sixty-eight children met Duke criteria for infective endocarditis (43 definite and 25 possible). Twenty-three (34%) had identified neurologic complications, including intracranial haemorrhage (25%, 17/68) and ischaemic stroke (25%, 17/68). Neurologic symptoms began a median of 4.5 days after infective endocarditis symptom onset (interquartile range 1, 25 days), though five children were asymptomatic and diagnosed on screening neuroimaging only. Overall, only 56% (38/68) underwent neuroimaging during acute hospitalisation, so additional asymptomatic neurologic complications may have been missed. Children with identified neurologic complications compared to those without were older (48 versus 22% ≥ 13 years old, p = 0.031), more often had definite rather than possible infective endocarditis (96 versus 47%, p < 0.001), mobile vegetations >10mm (30 versus 11%, p = 0.048), and vegetations with the potential for systemic embolisation (65 versus 29%, p = 0.004). Six children died (9%), all of whom had neurologic complications.Conclusions:Neurologic complications of infective endocarditis were common (34%) and associated with mortality. The true frequency of neurologic complications was likely higher because asymptomatic cases may have been missed without screening neuroimaging. Moving forward, we advocate that all children with infective endocarditis have neurologic consultation, examination, and screening neuroimaging. Additional prospective studies are needed to determine whether early identification of neurologic abnormalities may direct management and ultimately reduce neurologic morbidity and overall mortality.

OBJECTIVE: Cardiac surgery utilizing circulatory arrest is most commonly performed under deep hypothermia (~18°C) to suppress tissue oxygen demand and provide neuroprotection during operative circulatory arrest. Studies investigating the effects of deep hypothermic circulatory arrest (DHCA) on neurodevelopmental outcomes of patients with congenital heart disease give conflicting results. Here, we address these issues by quantifying changes in cerebral oxygen saturation, blood flow, and oxygen metabolism in neonates during DHCA and investigating the association of these changes with post-operative brain injury. METHODS: Neonates with critical congenital heart disease undergoing DHCA were recruited for continuous intraoperative monitoring of cerebral oxygen saturation (ScO(2)) and an index of cerebral blood flow (CBF(i)) using two non-invasive optical techniques, diffuse optical spectroscopy (DOS) and diffuse correlation spectroscopy (DCS). Pre- and post-operative brain magnetic resonance imaging (MRI) was performed to detect white matter injury (WMI). RESULTS: Fifteen neonates were studied, and 11/15 underwent brain MRI. During DHCA, ScO(2) decreased exponentially in time with a median decay rate of −0.04 min(−1). This decay rate was highly variable between subjects. Subjects who had larger decreases in ScO(2) during DHCA were more likely to have post-operative WMI (p=0.02). CONCLUSIONS: Cerebral oxygen extraction persists during DHCA and varies widely from patient-to-patient. Patients with a higher degree of oxygen extraction during DHCA were more likely to show new WMI in post-operative MRI. These findings suggest cerebral oxygen extraction should be monitored during DHCA to identify patients at risk for hypoxic-ischemic injury, and that current commercial cerebral oximeters may underestimate cerebral oxygen extraction.

Objective To determine whether machine learning techniques would enhance our ability to incorporate key variables into a parsimonious model with optimized prediction performance for electroencephalographic seizure (ES) prediction in critically ill children. Methods We analyzed data from a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy who underwent clinically-indicated continuous EEG monitoring (CEEG). We implemented and compared three state-of-the-art machine learning methods for ES prediction: (1) random forest; (2) Least Absolute Shrinkage and Selection Operator (LASSO); and (3) Deep Learning Important FeaTures (DeepLIFT). We developed a ranking algorithm based on the relative importance of each variable derived from the machine learning methods. Results Based on our ranking algorithm, the top five variables for ES prediction were: (1) epileptiform discharges in the initial 30 minutes, (2) clinical seizures prior to CEEG initiation, (3) sex, (4) age dichotomized at 1 year, and (5) epileptic encephalopathy. Compared to the stepwise selection-based approach in logistic regression, the top variables selected by our ranking algorithm were more informative as models utilizing the top variables achieved better prediction performance evaluated by prediction accuracy, AUROC and F1 score. Adding additional variables did not improve and sometimes worsened model performance. Conclusion The ranking algorithm was helpful in deriving a parsimonious model for ES prediction with optimal performance. However, application of state-of-the-art machine learning models did not substantially improve model performance compared to prior logistic regression models. Thus, to further improve the ES prediction, we may need to collect more samples and variables that provide additional information.

Post-operative oral feeding difficulties in neonates and infants with CHD is common. While pre-operative oral feeding may be normal, oral feeding challenges manifest in the post-operative period without a clearly defined aetiology. The objective of this scoping review was to examine post-operative oral feeding in full-term neonates and infants with a CHD. Electronic databases query (1 January 1975–31 May 2021), hand-search of the reference lists of included studies, contact with experts, and review of relevant conferences were performed to identify quantitative studies evaluating post-operative oral feeding in full-term neonates and infants with a CHD. Associations with additional quantitative variables in these studies were also examined. Twenty-five studies met inclusion criteria. Eighty per cent were cohort studies that utilised retrospective chart review from a single institution. The primary variable of interest in all studies was oral feeding status upon discharge from neonatal hospitalisation. The most common risk factors evaluated with poor feeding at time of discharge were birth weight (36% of included studies), gestational age (44%), duration of post-operative intubation (48%), cardiac diagnosis (40%), and presence of genetic syndrome or chromosomal anomaly (36%). The most common health-related outcomes evaluated were length of hospital stay (40%) and length of ICU stay (16%). Only the health-related outcomes of length of hospital stay and length of ICU stay were consistently significantly associated with poor post-operative oral feeding across studies in this review. A clear aetiology of poor post-operative oral feeding remains unknown.

Continuous EEG monitoring (CEEG) to identify electrographic seizures (ES) in critically ill children is resource intense. Targeted strategies could enhance implementation feasibility. We aimed to validate previously published findings regarding the optimal CEEG duration to identify ES in critically ill children.This was a prospective observational study of 1,399 consecutive critically ill children with encephalopathy. We validated the findings of a multistate survival model generated in a published cohort (N = 719) in a new validation cohort (N = 680). The model aimed to determine the CEEG duration at which there was15%,10%,5%, or2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors.A model aiming to determine the CEEG duration at which a patient had10% risk of ES if CEEG were continued longer showed similar performance in the generation and validation cohorts. Patients without emergent EEG risk factors would undergo 7 hours of CEEG in both cohorts, whereas patients with emergent EEG risk factors would undergo 44 and 36 hours of CEEG in the generation and validation cohorts, respectively. The10% risk of ES model would yield a 28% or 64% reduction in CEEG hours compared with guidelines recommending CEEG for 24 or 48 hours, respectively.This model enables implementation of a data-driven strategy that targets CEEG duration based on readily available clinical and EEG variables. This approach could identify most critically ill children experiencing ES while optimizing CEEG use.

Objective Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but identification requires extensive resources for continuous electroencephalographic monitoring (CEEG). In a previous study, we developed a clinical prediction rule using three clinical variables (age, acute encephalopathy category, clinically evident seizure[s] prior to CEEG initiation) and two electroencephalographic (EEG) variables (EEG background category and interictal discharges within the first 30 minutes of EEG) to identify patients at high risk for ESs for whom CEEG might be essential. In the current study, we aimed to validate the ES prediction model using an independent cohort. Methods The prospectively acquired validation cohort consisted of 314 consecutive critically ill children treated in the Pediatric Intensive Care Unit of a quaternary care referral hospital with acute encephalopathy undergoing clinically indicated CEEG. We calculated test characteristics using the previously developed prediction model in the validation cohort. As in the generation cohort study, we selected a 0.10 cutpoint to emphasize sensitivity. Results The incidence of ESs in the validation cohort was 22%. The generation and validation cohorts were alike in most clinical and EEG characteristics. The ES prediction model was well calibrated and well discriminating in the validation cohort. The model had a sensitivity of 90%, specificity of 37%, positive predictive value of 28%, and negative predictive value of 93%. If applied, the model would limit 31% of patients from undergoing CEEG while failing to identify 10% of patients with ESs. The model had similar performance characteristics in the generation and validation cohorts. Significance A model employing five readily available clinical and EEG variables performed well when validated in a new consecutive cohort. Implementation would substantially reduce CEEG utilization, although some patients with ESs would not be identified. This model may serve a critical role in targeting limited CEEG resources to critically ill children at highest risk for ESs.

ObjectivesTo determine the optimal duration of continuous EEG monitoring (CEEG) for electrographic seizure (ES) identification in critically ill children.MethodsWe performed a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy. We evaluated baseline clinical risk factors (age and prior clinically evident seizures) and emergent CEEG risk factors (epileptiform discharges and ictal-interictal continuum patterns) using a multistate survival model. For each subgroup, we determined the CEEG duration for which the risk of ES was ResultsES occurred in 184 children (26%). Patients achieved ConclusionsA model derived from 2 baseline clinical risk factors and emergent EEG risk factors would allow clinicians to implement personalized strategies that optimally target limited CEEG resources. This would enable more widespread use of CEEG-guided management as a potential neuroprotective strategy.ClinicalTrials.gov identifierNCT03419260.

Background Extra-corporeal membrane oxygenation (ECMO) is a life-saving intervention for severe respiratory and cardiac diseases. However, 50% of survivors have abnormal neurologic exams. Current ECMO management is guided by systemic metrics, which may poorly predict cerebral perfusion. Continuous optical monitoring of cerebral hemodynamics during ECMO holds potential to detect risk factors of brain injury such as impaired cerebrovascular autoregulation (CA). Methods We conducted daily measurements of microvascular cerebral blood flow (CBF), oxygen saturation, and total hemoglobin concentration using diffuse correlation spectroscopy (DCS) and frequency-domain diffuse optical spectroscopy in nine neonates. We characterize CA utilizing the correlation coefficient (DCSx) between CBF and mean arterial blood pressure (MAP) during ECMO pump flow changes. Results Average MAP and pump flow levels were weakly correlated with CBF and were not correlated with cerebral oxygen saturation. CA integrity varied between individuals and with time. Systemic measurements of MAP, pulse pressure, and left cardiac dysfunction were not predictive of impaired CA. Conclusions Our pilot results suggest that systemic measures alone cannot distinguish impaired CA from intact CA during ECMO. Furthermore, optical neuromonitoring could help determine patient-specific ECMO pump flows for optimal CA integrity, thereby reducing risk of secondary brain injury. Impact Cerebral blood flow and oxygenation are not well predicted by systemic proxies such as ECMO pump flow or blood pressure. Continuous, quantitative, bedside monitoring of cerebral blood flow and oxygenation with optical tools enables new insight into the adequacy of cerebral perfusion during ECMO. A demonstration of hybrid diffuse optical and correlation spectroscopies to continuously measure cerebral blood oxygen saturation and flow in patients on ECMO, enabling assessment of cerebral autoregulation. An observation of poor correlation of cerebral blood flow and oxygenation with systemic mean arterial pressure and ECMO pump flow, suggesting that clinical decision making guided by target values for these surrogates may not be neuroprotective. ~50% of ECMO survivors have long-term neurological deficiencies; continuous monitoring of brain health throughout therapy may reduce these tragically common sequelae through brain-focused adjustment of ECMO parameters.

OBJECTIVE Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone. METHODS We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG. RESULTS The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P

Background and ObjectivesFew data are available regarding the use of anesthetic infusions for refractory status epilepticus (RSE) in children and neonates, and ketamine use is increasing despite limited data. We aimed to describe the impact of ketamine for RSE in children and neonates.MethodsRetrospective single-center cohort study of consecutive patients admitted to the intensive care units of a quaternary care children's hospital treated with ketamine infusion for RSE.ResultsSixty-nine patients were treated with a ketamine infusion for RSE. The median age at onset of RSE was 0.7 years (interquartile range 0.15–7.2), and the cohort included 13 (19%) neonates. Three patients (4%) had adverse events requiring intervention during or within 12 hours of ketamine administration, including hypertension in 2 patients and delirium in 1 patient. Ketamine infusion was followed by seizure termination in 32 patients (46%), seizure reduction in 19 patients (28%), and no change in 18 patients (26%).DiscussionKetamine administration was associated with few adverse events, and seizures often terminated or improved after ketamine administration. Further data are needed comparing first-line and subsequent anesthetic medications for treatment of pediatric and neonatal RSE.Classification of EvidenceThis study provides Class IV evidence on the therapeutic utility of ketamine for treatment of RSE in children and neonates.

We aimed to determine the incidence of periodic and rhythmic patterns (PRP), assess the interrater agreement between electroencephalographers scoring PRP using standardized terminology, and analyze associations between PRP and electrographic seizures (ES) in critically ill children.This was a prospective observational study of consecutive critically ill children undergoing continuous electroencephalographic monitoring (CEEG). PRP were identified by one electroencephalographer, and then two pediatric electroencephalographers independently scored the first 1-h epoch that contained PRP using standardized terminology. We determined the incidence of PRPs, evaluated interrater agreement between electroencephalographers scoring PRP, and evaluated associations between PRP and ES.One thousand three hundred ninety-nine patients underwent CEEG. ES occurred in 345 (25%) subjects. PRP, ES + PRP, and ictal-interictal continuum (IIC) patterns occurred in 142 (10%), 81 (6%), and 93 (7%) subjects, respectively. The most common PRP were generalized periodic discharges (GPD; 43, 30%), lateralized periodic discharges (LPD; 34, 24%), generalized rhythmic delta activity (GRDA; 34, 24%), bilateral independent periodic discharges (BIPD; 14, 10%), and lateralized rhythmic delta activity (LRDA; 11, 8%). ES risk varied by PRP type (p .01). ES occurrence was associated with GPD (odds ratio [OR] = 6.35, p .01), LPD (OR = 10.45, p .01), BIPD (OR = 6.77, p .01), and LRDA (OR = 6.58, p .01). Some modifying features increased the risk of ES for each of those PRP. GRDA was not significantly associated with ES (OR = 1.34, p = .44). Each of the IIC patterns was associated with ES (OR = 6.83-8.81, p .01). ES and PRP occurred within 6 h (before or after) in 45 (56%) subjects.PRP occurred in 10% of critically ill children who underwent CEEG. The most common patterns were GPD, LPD, GRDA, BIPD, and LRDA. The GPD, LPD, BIPD, LRDA, and IIC patterns were associated with ES. GRDA was not associated with ES.

ObjectiveTo determine the association between electroencephalographic seizure (ES) and electroencephalographic status epilepticus (ESE) exposure and unfavorable neurobehavioral outcomes in critically ill children with acute encephalopathy.MethodsThis was a prospective cohort study of acutely encephalopathic critically ill children undergoing continuous EEG monitoring (CEEG). ES exposure was assessed as (1) no ES/ESE, (2) ES, or (3) ESE. Outcomes assessed at discharge included the Glasgow Outcome Scale–Extended Pediatric Version (GOS-E-Peds), Pediatric Cerebral Performance Category (PCPC), and mortality. Unfavorable outcome was defined as a reduction in GOS-E-Peds or PCPC score from preadmission to discharge. Stepwise selection was used to generate multivariate logistic regression models that assessed associations between ES exposure and outcomes while adjusting for multiple other variables.ResultsAmong 719 consecutive critically ill patients, there was no evidence of ES in 535 patients (74.4%), ES occurred in 140 patients (19.5%), and ESE in 44 patients (6.1%). The final multivariable logistic regression analyses included ES exposure, age dichotomized at 1 year, acute encephalopathy category, initial EEG background category, comatose at CEEG initiation, and Pediatric Index of Mortality 2 score. There was an association between ESE and unfavorable GOS-E-Peds (odds ratio 2.21, 95% confidence interval 1.07–4.54) and PCPC (odds ratio 2.17, 95% confidence interval 1.05–4.51) but not mortality. There was no association between ES and unfavorable outcome or mortality.ConclusionsAmong acutely encephalopathic critically ill children, there was an association between ESE and unfavorable neurobehavioral outcomes, but no association between ESE and mortality. ES exposure was not associated with unfavorable neurobehavioral outcomes or mortality.

Objective Guidelines recommend that encephalopathic critically ill children undergo continuous electroencephalographic (CEEG) monitoring for electrographic seizure (ES) identification and management. However, limited data exist on antiseizure medication (ASM) safety for ES treatment in critically ill children. Methods We performed a single-center prospective observational study of encephalopathic critically ill children undergoing CEEG. Clinical and EEG features and ASM utilization patterns were evaluated. We determined the incidence, types, and risk factors for adverse events associated with ASM administration. Results A total of 472 consecutive critically ill children undergoing CEEG were enrolled. ES occurred in 131 children (28%). Clinicians administered ASM to 108 children with ES (82%). ES terminated after the initial ASM in 38% of patients who received one ASM, after the second ASM in 35% of patients who received two ASMs, after the third ASM in 50% of patients who received three ASMs, and after the fourth ASM in 53% of patients who received four ASMs. Thirty patients (28%) received anesthetic infusions for ES management. Adverse events occurred in 18 patients (17%). Adverse effects were expected and resolved in all patients, and they were generally serious (in 15 patients) and definitely related (in 12 patients). Adverse events were rare in patients with acute symptomatic seizures requiring only one to two ASMs for treatment, but were more common in children with epilepsy, ictal-interictal continuum EEG patterns, or patients requiring more extensive ASM management. Significance ES ceased after one ASM in only 38% of critically ill children but ceased after two ASMs in 73% of critically ill children. Thus, ES management was often accomplished with readily available medications, but optimization of multistep ES management strategies might be beneficial. Adverse events were rare and manageable in children with acute symptomatic seizures requiring only one to two ASMs for treatment. Future studies are needed to determine whether management of acute symptomatic ES improves neurobehavioral outcomes.