A standardised assessment scheme for conventional EEG in preterm infants

Comparative utility of MRI and EEG for early detection of cortical dysmaturation after postnatal systemic inflammation in the neonatal rat

BACKGROUND

Exposure to postnatal systemic inflammation is associated with increased risk of brain injury in preterm infants, leading to impaired maturation of the cerebral cortex and adverse neurodevelopmental outcomes. However, the optimal method for identifying cortical dysmaturation is unclear. Herein, we compared the utility of electroencephalography (EEG), diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI) at different recovery times after systemic inflammation in newborn rats.

METHODS

Sprague Dawley rat pups of both sexes received single-daily lipopolysaccharide (LPS; 0.3 mg/kg i.p.; n = 51) or saline (n = 55) injections on postnatal days (P)1, 2, and 3. A subset of these animals were implanted with EEG electrodes. Cortical EEG was recorded for 30 min from unanesthetized, unrestrained pups at P7, P14, and P21, and in separate groups, brain tissues were collected at these ages for ex-vivo MRI analysis (9.4 T) and Golgi-Cox staining (to assess neuronal morphology) in the motor cortex.

RESULTS

Postnatal inflammation was associated with reduced cortical pyramidal neuron arborization from P7, P14, and P21. These changes were associated with dysmature EEG features (e.g., persistence of delta waveforms, higher EEG amplitude, reduced spectral edge frequency) at P7 and P14, and higher EEG power in the theta and alpha ranges at P21. By contrast, there were no changes in cortical DTI or NODDI in LPS rats at P7 or P14, while there was an increase in cortical fractional anisotropy (FA) and decrease in orientation dispersion index (ODI) at P21.

CONCLUSIONS

EEG may be useful for identifying the early evolution of impaired cortical development after early life postnatal systemic inflammation, while DTI and NODDI seem to be more suited to assessing established cortical changes.

Quantitative analysis of high-frequency activity in neonatal EEG

OBJECTIVE

To determine the presence and potential utility of independent high-frequency activity recorded from scalp electrodes in the electroencephalogram (EEG) of newborns.

METHODS

We compare interburst intervals and continuous activity at different frequencies for EEGs retrospectively recorded at 256 Hz from 4 newborn groups: 1) 36 preterms (<32 weeks' gestational age, GA); 2) 12 preterms (32-37 weeks' GA); 3) 91 healthy full terms; 4) 15 full terms with hypoxic-ischemic encephalopathy (HIE). At 4 standard frequency bands (delta, 0.5-3 Hz; theta, 3-8 Hz; alpha, 8-15 Hz; beta, 15-30 Hz) and 3 higher-frequency bands (gamma1, 30-48 Hz; gamma2, 52-99 Hz; gamma3, 107-127 Hz), we compared power spectral densities (PSDs), quantitative features, and machine learning model performance. Feature selection and further machine learning methods were performed on one cohort.

RESULTS

We found significant (P < 0.01) differences in PSDs, quantitative analysis, and machine learning modelling at the higher-frequency bands. Machine learning models using only high-frequency features performed best in preterm groups 1 and 2 with a median (95% confidence interval, CI) Matthews correlation coefficient (MCC) of 0.71 (0.12-0.88) and 0.66 (0.36-0.76) respectively. Interburst interval-detector models using both high- and standard-bandwidths produced the highest median MCCs in all four groups. High-frequency features were largely independent of standard-bandwidth features, with only 11/84 (13.1%) of correlations statistically significant. Feature selection methods produced 7 to 9 high-frequency features in the top 20 feature set.

CONCLUSIONS

This is the first study to identify independent high-frequency activity in newborn EEG using in-depth quantitative analysis. Expanding the EEG bandwidths of analysis has the potential to improve both quantitative and machine-learning analysis, particularly in preterm EEG.

[A preliminary study on a new method for evaluating brain maturation in preterm infants]

OBJECTIVES

To establish a new method for evaluating the brain maturation of preterm infants based on the features of electroencephalographic activity.

METHODS

A prospective study was conducted on the video electroencephalography (vEEG) and amplitude-integrated electroencephalography (aEEG) recordings within 7 days after birth of preterm infants who had a postmenstrual age (PMA) of 25-36 weeks and met the inclusion criteria. The background activity of aEEG+conventional electroencephalography (cEEG) was scored according to the features of brain maturation as a new evaluation system and was compared with the aEEG evaluation system. The correlations of the evaluation results of the two methods with gestational age (GA), PMA, and head circumference were evaluated. The intervals of the total scores of aEEG+cEEG and aEEG were calculated for preterm infants with different PMAs and were compared between groups. The consistency of the new scoring system was evaluated among different raters.

RESULTS

A total of 52 preterm infants were included. The total scores of aEEG+cEEG and aEEG were positively correlated with GA, PMA, and head circumference (P<0.05), and the correlation coefficient between the total scores of the two systems and PMA and GA was >0.9. The normal score intervals for aEEG+cEEG and aEEG scoring systems were determined in preterm infants with different PMAs as follows: infants with a PMA of less than 28 weeks had scores of 13.0 (11.0, 14.0) points for aEEG+cEEG and 6.0 (4.0, 7.0) points for aEEG; infants with a PMA between 28 and 29+6 weeks had scores of 16.0 (14.5, 17.0) points for aEEG+cEEG and 8.0 (6.0, 8.0) points for aEEG; infants with a PMA between 30 and 31+6 weeks had scores of 18.0 (17.0, 21.0) points for aEEG+cEEG and 9.0 (8.0, 10.0) points for aEEG; infants with between 32 and 33+6 weeks had scores of 22.0 (20.0, 24.5) points for aEEG+cEEG and 10.0 (10.0, 10.8) points for aEEG; infants with a PMA between 34 and 36 weeks had scores of 26.0 (24.5, 27.5) points for aEEG+cEEG and 11.0 (10.0, 12.0) points for aEEG. There were significant differences in the total scores of aEEG+cEEG and aEEG among the different PMA groups (P<0.05). There was a high consistency between different raters when using the scoring system to evaluate the brain maturation of preterm infants (κ=0.86).

CONCLUSIONS

The aEEG+cEEG scoring system established in this study can quantitatively reflect the brain maturation of preterm infants, with a good discriminatory ability between preterm infants with different PMAs and high consistency between different raters.

Acute Diffusion-Weighted Imaging Signaling Severe Periventricular Leukomalacia in Preterm Infants: Case Report and Review of Literature

Introduction: Periventricular leukomalacia occurs in up to 25% of very preterm infants resulting in adverse neurodevelopmental outcomes. In its acute phase, periventricular leukomalacia is clinically silent. Although ultrasonography is widely available, its sensitivity in the early detection of periventricular leukomalacia is low. Case Report and Published Literature: We identified a preterm infant with early diffusion-weighted imaging changes that later evolved to periventricular leukomalacia. Thirty-two cases of abnormal diffusion-weighted imaging reliably heralding severe periventricular leukomalacia in the preterm infant have been published in the literature. Notable features include the following: (1) infants were more mature preterm infants (29-36 weeks' gestation); (2) findings were often serendipitous with benign clinical courses; (3) diffusion-weighted imaging changes only were evident in the first weeks of life with later evolution to more classical abnormalities on conventional magnetic resonance imaging (MRI) or ultrasonography. Conclusion: Diffusion-weighted imaging in the first week of life may be a reliable early marker of severe periventricular leukomalacia injury in more mature preterm infants.

Predicting the Neurodevelopmental Outcome in Extremely Preterm Newborns Using a Multimodal Prognostic Model Including Brain Function Information

IMPORTANCE

Early assessment of the prognosis of preterm newborns is crucial for accurately informing parents and making treatment decisions. The currently available prognostic models rarely incorporate functional brain information from conventional electroencephalography (cEEG).

OBJECTIVE

To examine the performance of a multimodal model combining (1) brain function information with (2) brain structure information (cranial ultrasonography), and (3) perinatal and (4) postnatal risk factors for the prediction of death or neurodevelopmental impairment (NDI) in extremely preterm infants.

DESIGN, SETTING, AND PARTICIPANTS

Preterm newborns (23-28 weeks' gestational age) admitted to the neonatal intensive care unit at Amiens-Picardie University Hospital were retrospectively included (January 1, 2013, to January 1, 2018). Risk factors from the 4 categories were collected during the first 2 weeks post delivery. Neurodevelopmental impairment was assessed at age 2 years with the Denver Developmental Screening Test II. No or moderate NDI was considered a favorable outcome. Death or severe NDI was considered an adverse outcome. Data analysis was performed from August 26, 2021, to March 31, 2022.

MAIN OUTCOMES AND MEASURES

After the selection of variables significantly associated with outcome, 4 unimodal prognostic models (considering each category of variable independently) and 1 multimodal model (considering all variables simultaneously) were developed. After a multivariate analysis for models built with several variables, decision-tree algorithms were run on each model. The areas under the curve for decision-tree classifications of adverse vs favorable outcomes were determined for each model, compared using bootstrap tests, and corrected for type I errors.

RESULTS

A total of 109 newborns (58 [53.2% male]) born at a mean (SD) gestational age of 26.3 (1.1) weeks were included. Among them, 52 (47.7%) had a favorable outcome at age 2 years. The multimodal model area under the curve (91.7%; 95% CI, 86.4%-97.0%) was significantly higher than those of the unimodal models (P < .003): perinatal model (80.6%; 95% CI, 72.5%-88.7%), postnatal model (81.0%; 95% CI, 72.6%-89.4%), brain structure model (cranial ultrasonography) (76.6%; 95% CI, 67.8%-85.3%), and brain function model (cEEG) (78.8%; 95% CI, 69.9%-87.7%).

CONCLUSIONS AND RELEVANCE

In this prognostic study of preterm newborns, the inclusion of brain information in a multimodal model was associated with significant improvement in the outcome prediction, which may have resulted from the complementarity of the risk factors and reflected the complexity of the mechanisms that interfered with brain maturation and led to death or NDI.

Developmental characteristics of early electroencephalography in preterm neonates: Differences between twins and singletons

BACKGROUND

To analyze the early electroencephalography (EEG) development of twins and singleton preterm neonates using 5 measurement indicators.

METHODS

On the 1st and 7th days after birth, EEG monitoring was performed on 102 preterm neonates (62 males, median gestational age 33.15 weeks, IQR 31.00-35.75). The minimum amplitude, maximum amplitude, maximum interburst intervals (IBI), total duration of trace discontinue (TD), maximum duration of single TD, and the Burdjalov score of amplitude-integrated electroencephalography (aEEG) were used to evaluate EEG recordings.

RESULTS

The minimum amplitude of EEG increases with gestational age (GA), while the maximum amplitude decreases, the maximum IBI decreases, and the total duration of TD and the maximum duration of single TD decrease (all p < 0.05). Burdjalov score did not differ significantly between the 1st and 7th days (p = 0.075). There is no significant difference between twins and singleton preterm infants in the five EEG measurement indicators (p > 0.05 for all).

CONCLUSION

The five EEG measurement indicators can better reflect preterm infants' brain maturation than the Burdjalov score in aEEG. There were no statistical differences in brain maturation between twin and singleton preterm infants.

CARFS7: A guide and proforma for reading a preterm neonate's EEG

OBJECTIVES

The important role of the EEG in preterm and term babies in investigating brain function and seizures, predicting outcomes, evaluating therapeutic interventions and decision-making is being increasingly acknowledged. Development of the brain in the last trimester of pregnancy results in rapid changes in the EEG patterns in this period. Acquiring and interpreting the EEG of a preterm baby can be challenging. The aim of this study was to develop a proforma titled CARFS⁷ (Continuity, Amplitude, Reactivity, Frequency, Synchrony, Symmetry, Sleep, Sharps, Shapes, Size and Seizures) to enable neurologists to read EEGs of premature babies with greater confidence, ease and accuracy and produce a report more easily repeatable and homogenous among operators.

METHODS

The CARFS⁷proforma was developed based on a literature review and the personal experience of the authors. The parameters of the EEG evaluated and scored in the proforma are Continuity, Amplitude, Reactivity/Variability, Frequency, Synchrony, Symmetry, Sleep, Sharps, Shapes/Patterns, Size and Seizures. We also assessed the interrater reliability of the proposed scoring system incorporated in the proforma.

RESULTS

CARFS⁷ proforma incorporates a number of parameters that help evaluate the preterm EEG. The interrater reliability of the proposed scoring system in the CARFS⁷proforma was high.

CONCLUSIONS

CARFS⁷ is a user friendly proforma for reading EEGs in the preterm infant. Interrater reliability using Cohen's k shows high agreement between two child neurologists who independently rated the EEGs of 25 premature babies using this proforma. CARFS⁷ has the potential to provide, accurate, reproducible and valuable information on brain function in the preterm infant in clinical practice.

The impact of perinatal inflammation on the electroencephalogram in preterm infants: a systematic review

BACKGROUND

To summarise the association between perinatal inflammation (PI) exposure and electroencephalography (EEG) features in preterm infants.

METHODS

This systematic review included clinical studies of preterm infants born <37 weeks of gestational age (GA), who had both a PI exposure and an EEG assessment performed during the neonatal period. Studies were identified from Medline and Embase databases on the 15th of September 2021. PI was defined by histological chorioamnionitis, clinical chorioamnionitis, or early-onset neonatal infection (EONI). The risk of bias in included studies was assessed using the Joanna Briggs Institute (JBI) appraisal tool. A narrative approach was used to synthesise results. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement.

RESULTS

Two cross-sectional studies enrolling 130 preterm children born <32 weeks of GA assessed with one-channel amplitude-integrated EEG (aEEG) during the first four days of life were included. A PI exposure was described in 39 (30%) infants and was associated with a decrease in amplitude and a reduced incidence of sleep-wake cycling patterns.

CONCLUSION

These results should be interpreted with caution because of the small number of included studies and their heterogeneity. Further clinical studies evaluating the association of PI with EEG findings are needed.

IMPACT

A method to assess developmental trajectories following perinatal inflammation is required. Insufficient data exist to determine EEG features associated with perinatal inflammation. Further clinical studies evaluating this association are needed.

Can EEG accurately predict 2-year neurodevelopmental outcome for preterm infants?

OBJECTIVE

Establish if serial, multichannel video electroencephalography (EEG) in preterm infants can accurately predict 2-year neurodevelopmental outcome.

DESIGN AND PATIENTS

EEGs were recorded at three time points over the neonatal course for infants <32 weeks' gestational age (GA). Monitoring commenced soon after birth and continued over the first 3 days. EEGs were repeated at approximately 32 and 35 weeks' postmenstrual age (PMA). EEG scores were based on an age-specific grading scheme. Clinical score of neonatal morbidity risk and cranial ultrasound imaging were completed.

SETTING

Neonatal intensive care unit at Cork University Maternity Hospital, Ireland.

MAIN OUTCOME MEASURES

Bayley Scales of Infant Development III at 2 years' corrected age.

RESULTS

Sixty-seven infants were prospectively enrolled in the study and 57 had follow-up available (median GA 28.9 weeks (IQR 26.5-30.4)). Forty had normal outcome, 17 had abnormal outcome/died. All EEG time points were individually predictive of abnormal outcome; however, the 35-week EEG performed best. The area under the receiver operating characteristic curve (AUC) for this time point was 0.91 (95% CI 0.83 to 1), p<0.001. Comparatively, the clinical course AUC was 0.68 (95% CI 0.54 to 0.80, p=0.015), while abnormal cranial ultrasound was 0.58 (95% CI 0.41 to 0.75, p=0.342).

CONCLUSION

Multichannel EEG is a strong predictor of 2-year outcome in preterm infants particularly when recorded around 35 weeks' PMA. Infants at high risk of brain injury may benefit from early postnatal EEG recording which, if normal, is reassuring. Postnatal clinical complications can contribute to poor outcome; therefore, we state that a later EEG around 35 weeks has a role to play in prognostication.

Early Diagnosis of Brain Injury in Premature Infants Based on Amplitude-Integrated EEG Scoring System

Analyzing and discussing the relationship between brain injury in preterm infants and related risk factors can provide evidence for perinatal prevention and early intervention of brain injury in preterm infants, thereby improving the quality of life of preterm infants. This paper selects term preterm infants diagnosed with preterm infant asphyxia in the NICU of a university's First Affiliated Hospital from January 2018 to February 2019 as the research object. In addition, healthy term infants born at the same time in the obstetric department of this hospital are selected as the control group. Both groups of premature infants were monitored for brain function within 6 hours after birth. The aEEG results range from background activity (continuous normal voltage, discontinuous normal voltage, burst suppression, continuous low voltage, and plateau) and sleep-wake cycle (no sleep-wake cycle, immature, and mature sleep-wake cycle) to epileptic activity (single seizures, recurrent seizures, and status epilepticus), three aspects to judge. Statistical analysis uses SPSS 17.0 software. Amplitude-integrated EEG is a simplified form of continuous EEG recording. The trace of the trace represents the voltage change signal of the entire EEG background activity, which can reflect the EEG amplitude, frequency, burst-inhibition, and other pieces of information. aEEG can reflect the degree of HIE lesions in premature infants and the long-term prognosis. It is easy to operate and effective in diagnosis and can be continuously monitored. It is worthy of clinical popularization. There is a good correlation between the expression of EEG and biomarkers. Combining multiple methods can diagnose HIE earlier and evaluate the prognosis.

Multichannel EEG abnormalities during the first 6 hours in infants with mild hypoxic-ischaemic encephalopathy

BACKGROUND

Infants with mild HIE are at risk of significant disability at follow-up. In the pre-therapeutic hypothermia (TH) era, electroencephalography (EEG) within 6 hours of birth was most predictive of outcome. This study aims to identify and describe features of early EEG and heart rate variability (HRV) (<6 hours of age) in infants with mild HIE compared to healthy term infants.

METHODS

Infants >36 weeks with mild HIE, not undergoing TH, with EEG before 6 hours of age were identified from 4 prospective cohort studies conducted in the Cork University Maternity Services, Ireland (2003-2019). Control infants were taken from a contemporaneous study examining brain activity in healthy term infants. EEGs were qualitatively analysed by two neonatal neurophysiologists and quantitatively assessed using multiple features of amplitude, spectral shape and inter-hemispheric connectivity. Quantitative features of HRV were assessed in both the groups.

RESULTS

Fifty-eight infants with mild HIE and sixteen healthy term infants were included. Seventy-two percent of infants with mild HIE had at least one abnormal EEG feature on qualitative analysis and quantitative EEG analysis revealed significant differences in spectral features between the two groups. HRV analysis did not differentiate between the groups.

CONCLUSIONS

Qualitative and quantitative analysis of the EEG before 6 hours of age identified abnormal EEG features in mild HIE, which could aid in the objective identification of cases for future TH trials in mild HIE.

IMPACT

Infants with mild HIE currently do not meet selection criteria for TH yet may be at risk of significant disability at follow-up. In the pre-TH era, EEG within 6 hours of birth was most predictive of outcome; however, TH has delayed this predictive value. 72% of infants with mild HIE had at least one abnormal EEG feature in the first 6 hours on qualitative assessment. Quantitative EEG analysis revealed significant differences in spectral features between infants with mild HIE and healthy term infants. Quantitative EEG features may aid in the objective identification of cases for future TH trials in mild HIE.