Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT01774318 |
| Other study ID # |
KKS-01-2012 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
N/A
|
| First received |
January 4, 2013 |
| Last updated |
January 20, 2013 |
| Start date |
February 2012 |
| Est. completion date |
December 2016 |
Study information
| Verified date |
January 2013 |
| Source |
Medical University of Vienna |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
Austria: Ethikkommission |
| Study type |
Interventional
|
Clinical Trial Summary
Due to the development of neonatal intensive care the number of surviving premature infants
increased significantly. The immature brain undergoes a fair amount of external stimuli,
which have a great impact on later cognitive development. Increasingly data show, that a
delayed emergence of sleep-wake-cycling in newborns can be the first sign of brain injury.
Studies have shown that clearly defined sleep states can be identified from 31-32 weeks of
gestation onwards. But a few studies show, that also extremely premature infants already
show cyclical variations of the background pattern within amplitude-integrated EEG (aEEG= a
time-compressed, simplified EEG) and conventional EEG. This might resemble early
sleep-wake-states and their presence correlates to the integrity of the central nervous
system, although no clearly defined "sleep states" according to the classical definition can
be identified. Complex EEG analysis needs the use of automated methods to exclude personal
bias and to ensure gestational age specific data analysis. The newly developed NLEO
algorithm was specially designed for EEG analysis of premature infants. Conventional EEG
within this study will be analyzed visually and with the automated algorithm. In our
research project we will study the emergence of Sleep-wake-cycling in extremely premature
infants and its impact on their neurodevelopmental outcome prospectively. The different
sleep and wake states will be derived from analysis of the conventional Video-EEG, aEEG and
polysomnographic measurements. Visual analysis will include assessment of amplitudes and
frequencies as well as the latencies and durations of EEG-Bursts and Interburst intervals.
The automated NLEO-algorithm will be firstly used for comparison with above described visual
analysis and secondly to find regions of interest involved in the organization of these
early sleep states. The aim of this study is first to understand and analyze in detail the
emergence of sleep-wake cycling including its disturbances in premature infants and to
compare automated NLEO algorithm to conventional visual analysis methods. Secondly to
correlate neurodevelopmental outcome to the emergence of sleep-wake-cycling.
Description:
General description - Aim of the study
Due to development of intensive care the number of surviving premature infants increased
significantly.Delayed emergence of sleep-wake-cycling in newborns can be the first sign of
brain injury. Clearly defined sleep-states can be identified from 32 weeks of gestation
onwards. Few studies show, that extremely premature infants (<26 weeks of gestation) may
already show early sleep-states. In our project we are aiming to study the emergence of
sleep-wake-cycling in extremely premature infants, prospectively collecting
electroencephalographic (EEG) data. Premature infants <29 weeks of gestation will be
included and measured for a 3 hour period every second week with conventional and
amplitude-integrated EEG. New analytical methods (automatic neonatal EEG algorithm) will be
used and compared to conventional visual analysis. In an international and interdisciplinary
cooperation between physicians, electrophysiologist and mathematicians we will be able to
deduct conclusions providing important prognostic information for patient, parents and
physicians.
State of the art and scientific challenge Increasingly data show, that a delayed emergence
of sleep-wake cycling in newborns can be the first sign of brain injury and is associated to
later adverse neurodevelopmental outcome. Due to increasing survival rates among the very
premature population the prevention from later neurological deficit currently becomes even
more important. Rates of cerebral palsy and ouvert cerebral lesions (cystic periventricular
leukomalacia and peri/intraventricular haemorrhage) are decreasing, but the incidence of
neurodevelopmental impairment remains high in preterm infants. This is explained by the
understanding of different mechanisms in brain injury (for example inflammation, oxidative
stress, impaired connectivity) and results in mainly cognitive impairment (1). Therefore
greater attention needs to be directed toward preterm neonatal populations to better
understand brain adaptation both with and without medical complications. Neurophysiologic
surveillance is necessary in these infants to adequately asses cerebral function and is
difficult within this population by clinical aspects only. Conventional EEG is today´s gold
standard for neurophysiologic diagnosis. Nevertheless it is not suitable for continuous
recording since it is producing large data volumes which cannot be assessed directly at the
bedside. In an effort to solve this problem, various methods of reducing and compressing the
EEG signal have been developed, the amplitude-integrated EEG (aEEG), being one of them.
Emergence of sleep-wake-cycling The concept of state during early brain ontogenesis of the
preterm infant is controversial. It is generally accepted that patterns representing sleep
in preterm infants are highly variable and less organized than patterns described for
full-term infants. Well organized sleep states do not appear before 31 weeks of gestation
and are not well established until 36 weeks postconceptional age. However several
researchers have questioned this assumption based on studies of sleep in preterm infants
(4-7). They support that rudimentary state differentation might be present as early as 26
weeks of gestation. In our study from 2001 we observed cyclical variations of EEG background
activity resembling early sleep-wake cycles as early as at 24/25 weeks of gestation (2).
Neurophysiological methods - amplitude-integrated EEG For early identification of infants at
high risk and to optimize treatment, it is mandatory to have access to a reliable validated
diagnostic method with excellent predictive value for later neurodevelopmental outcome. The
aEEG is a readily available, informative and reliable technique for continuous non-invasive
monitoring of brain activity even in extremely premature infants. Our research group has
more than ten years of experience using of the amplitude-integrated EEG and it is a simple
method for continuous bedside monitoring in the neonatal intensive care unit setting. Our
group has recently shown aEEG has a predictive value for later outcome in preterm infants
and can therefore be used as an early prognostic tool for neurodevelopmental outcome (3).
We have found emerging sleep-wake cycles as early as 24-25 weeks of gestation in
neurologically healthy premature infants. On the contrary premature infants with
intraventricular haemorrhage exhibited a significant delay in emergence of their sleep-wake
cycles, on average with 32 weeks of gestation (8). We know that at this early age the
development of intercellular connections of the brain and synaptic branching is still in
development and that these processes take place mainly during sleep.
Neurophysiological methods - conventional EEG Conventional visual classification of the EEG
signal of different brain regions has been the standard of analysis since the 1960s when
first neonatal recordings were performed. Today more than 80% of extremely premature infants
between 24-28 weeks of gestation survive. Within the analysis of EEG signal there has been a
growing need for more reliable automatic methods, being suitable for this specific
population. New nomenclature has emerged specifically for the premature population such as
spontaneous activity transients (SATs), which constitute the most salient feature on EEG
during the preterm period (9-11).These spontaneous bursts of activity, which are related to
the excitatory role of GABAergic transmission during early development not only characterize
the premature EEG, but have been linked to the development of intracortical connections and
neuronal wiring. SATs constitute of a very slow activity (0,1-0,5 Hz), with nesting activity
at several higher frequencies. This activity represents the organization and development of
thalamo-cortical connections, when neurons migrate from the subplate into the cortical plate
in the primary sensory cortices. The cooperation with a finnish expert on the field within
this study, who is experienced with automated EEG algorithm analysis will allow even further
analysis of the emergence of early sleep-wake-cycling as allows only conventional, visual
analysis of the EEG.
As far as we know this would be the first study to evaluate in detail the emergence of
sleep-wake-cycles in preterm infants using different methods and the first study, trying to
identify the role of SATs in the development of sleep organization in extremely premature
infants.
Research questions/ Objectives/ Hypotheses We plan to conduct a prospective single-center
cohort trial with an international cooperation in order to analyze the emergence of
sleep-wake rhythm in very premature infants in detail using conventional video-EEG and
amplitude-integrated EEG monitoring.
First objective of the study will be a detailed description of early sleep states, to
analyze which cortical regions and deeper structures are responsible and involved in their
development and describe the sequence of their emergence and study these EEG features in a
prospective "healthy" cohort with no drug bias or pathology. (Infants should show no
neurological disease and not use any neurologically active medication during analysis)
Second objective of the study will be to analyze the feasibility of automated conventional
EEG analysis using the NLEO-based algorithm (nonlinear energy operator) designed for the
automated detection of SATs in premature infants and to correlate the results with the
different sleep-wake-states.
Third aim of the study is to compare the two methods (automated versus visual analysis) in
order to develop evidence based analysis for early sleep-stage development.
The fourth aim of the study is to correlate sleep organisation to later neuromotor and
cognitive outcome.
Hypotheses:
1. Video-EEG Polysomnography in combination with aEEG information is a reliable tool to
identify sleep development even in extremely premature infants.
2. Early sleep-wake cycles appear from early gestation onwards in healthy premature
infants. They can be distinguished visually as well as automatically by NLEO algorithm.
3. The onset of sleep-wake cycles and their regular cyclicity in extremely premature
infants are a good indicator of normal brain function and intact brain development,
reflecting maturing neural networks.
4. Early sleep-wake cycles and their development do have an impact on later
neurodevelopmental and cognitive outcome in premature infants.
Methodological approaches
Patients During a study period of 36 months consecutively all infants born below 29 weeks of
gestation who are admitted to our neonatal intensive care unit (NICU) will be enrolled in
this study. Approvement of the local ethics committee has already be obtained (EK-Nr.
67/2008) and written parental consent will be acquired for each patient. At least 3 hours of
sleep monitoring will be performed every second week until 36 weeks of gestation using
conventional video-EEG and aEEG. Thus there will be six different timepoints of data
acquisition: 24-25; 26-27; 28-29; 30-31; 32-33 and 34-35 weeks of gestation. The first
measurement will be performed during the first week of life after stabilization of clinical
state.
Amplitude-integrated EEG (aEEG) The aEEG is recorded as a single channel EEG from biparietal
surface disk electrodes using a CFM 6000 (Olympic Medical, USA). The obtained signal is
filtered, rectified, smoothed and amplitude-integrated before it is written out or digitally
available on the monitor at a slow speed (6 cm/h), directly at the bed side.
Tracings are evaluated visually and classified according to the method previously described
by Hellström-Westas et al. (12) Descriptive analysis of the background activity of the aEEG
tracings will be done by dividing each trace in 10-minute epochs. These 10-minute epochs wil
be classified into five pattern categories ("continuous pattern", "discontinuous pattern",
"burst suppression pattern", "low voltage activity" and "flat trace").
The presence of sleep-wake-cycles and seizure activity will be described seperately.
The percentage of the different patterns and the length of quiet-sleep and active
sleep/wakefulness will be calculated for the entire aEEG trace.
Conventional EEG and Video-Polysomnography For the assessment of conventional EEG we will
use the Micromed System-Plus program. The Video-EEG will be evaluated according to the
methods previously published by Ludington-Hoe/Scher (4,6). EEG signals are registered from
electrodes located on Fp1, C3, T3, O1, Fp2, C4, T4, O2 according to the International 10-20
System of electrode placement adapted for recording of neonates.
"Quiet Sleep" is defined as a discontinuous pattern in all channels, where low amplitude
(<20µV) bursts (bursts are defined as a distinct occurrence of cerebral activity with a slow
component and associated faster activity) are often present and they are approximately 2-10s
long (=Interburst Interval).
The beginning and the end of such a "discontinuous EEG-Segment" is to be marked and the
Bursts and Interburst-intervals and its amplitudes and frequencies will be described in
detail. They will be measured 20x in a 10min representative EEG epoch and data will be
averaged. Also clinical data like body movements, eye movements, heart rate and respiratory
rate will be measured 20 x in a 10min representative EEG epoch and data will be averaged.
"Active sleep" is defined as a continuous EEG-activity lasting longer than 60s. Similarly
amplitudes and frequencies of the bursts and interburst intervals will be measured as well
as the above mentioned clinical parameters.
"Wakefulness" is defined electrophysiologically identical to the definition of "active
sleep", differences can only be determined according to the behaviour analysis noted on the
video recordings.
"Indeterminate sleep": EEG-segments, which do not fully meet the definitons above and last
longer than >30s will be classified into "indeterminate sleep".
"Arousals" are defined as sudden asynchronuous changes within the EEG-pattern during a sleep
state, with associated body movements, muscle activity and eye-openings which last shorter
than 30s.
Behavioral states will be classified by Videomonitoring according to Holditch-Davies (5).
Quiet waking state is defined by eyes being open or opening, low motor activity and even
respiration.
Active waking is defined by eyes being open, crying, fussing and generalized motor activity.
Active sleep is defined as eyes being closed, uneven respiration and intermittent rapid eye
movements (REM).
Quiet sleep is defined as eyes beingt closed and even respiration
Automated EEG analysis - NLEO algorithm The NLEO-based algorithm for automated detection of
spontaneous activity transients (SATs; also called bursts) will also be used to analyze the
8 channel EEG data. The algorithm consists of feature extraction and a classification
algorithm, with the idea that every sample of the EEG will be automatically characterized as
either SAT or inter-SAT based on a detection that uses Nonlinear Energy Operator
transformation (NLEO). This methodology has been implemented for adults previously, but it
was recently adapted further for preterm EEG signals by the Vanhatalo group in Helsinki
(9-11). The newly revised algorithm was shown to agree well with an expert visual
classification. SAT detection can be used to calculate cumulative (or time-varying)
percentage of SATs, the length of the inter-SAT interval and the number of SATs per minute.
In the context of the present study, this approach has offered a possibility to design
algorithms for an automated and objective assessment of SWC. Our core interest is the
endogenous cyclicity of the EEG pattern, which will be further analyzed with the automated
software, where SATs, their spatial characteristics and their regulation will be
characterized. The NLEO enables to analyze the spatial characteristics of these oscillations
in different sleep-stages in the developing brain of the premature infant.
Cranial Ultrasound All infants will be routinely assessed using cranial ultrasound
examinations every week until the 32th week of gestation and after that every second week.
Pathologies such as periventricular leukomalacia (PVL) and intraventricular haemorrhage
(IVH), or other appearing abnormalities will be documented and followed up.
Neurodevelopmental follow up All study patients will be involved in our neonatal follow up
program to assess their neurodevelopmental outcome. Neurodevelopmental outcome will be
assessed at 1,2 and 3,5 years of age by assessment of the Bayley Scales of Infant
Development II and at the age of 5,5 years by Kaufmann´s Assessment Battery for Children
(K-ABC) and Beery-Buktenica Developmental Test of Visual-Motor Integration (VMI) done by an
experienced staff (developmental psychologist and pediatrician).
The Bayley Scales will be classified as normal when psychomotor (PDI) and mental
developmental index (MDI) scores were > 85; K-ABC and VMI will also be considered normal
when > 85 (within 2 standard deviations of reference values) and severely impaired when < 70
(below 3 standard deviation variance Cerebral Palsy will be defined as a nonprogressive
central nervous system disorder characterized by abnormal muscle tone in at least one
extremity and abnormal control of movement or posture and was defined due to location as
hemiplegia, diplegia and tetraplegia.
Other included outcome variables will be visual and acoustic impairment, where any form of
abnormality will be included (need of glasses/hearing aid, as well as blindness/deafness).
Also detailed information regarding environmental and social and perinatal risk factors will
be collected.
Statistical Analysis Occurrence and duration of different amplitude-integrated EEG pattern
(=continuous, discontinuous patterns) will be given as percentages and descriptively
compared to already published reference values. Occurrence and duration of above described
conventional EEG features (=quiet sleep, active sleep and indetermined sleep) and its
established detailed components (duration and amplitudes and frequencies of bursts and
interburst intervals) will be given as means per 10 minute epochs. In another step EEG
activity will be correlated to neurodevelopmental outcome by Pearson´s correlation.
The effect of the following factors: "percentage of continuous pattern", "percentage of
discontinuous pattern, "percentage of burst suppression pattern", , "occurrence of
sleep-wake-cycling at aEEG", occurrence of seizure activity", "mean amplitude of burst",
"mean amplitude of interburst interval ", "mean frequency of burst", "mean frequency of
interburst interval", "appearance of Delta Brush" ,"appearance of Theta Bursts", "mean heart
rate", "mean respiratory rate", "occurrence of rapid eye movements" and "mean of body
movements" on neurodevelopmental outcome will be estimated in a multinomial regression model
and ANOVA in SPSS Statistics Version 17.0 for Windows.
P-values lower than 5% will be considered as indicating significance. Classification of the
conventional EEG and video-polysomnography will be done by two of the authors (K.K and Z.R)
and interrater reliability (Cohen´s Kappa) will be determined.
Detection of SAT epochs by NLEO-based detector will be assessed, by using sample by sample
method. This aims to verify the comparability to prior studies (10) that used EEG signals
from EEG amplifiers with different specifications. In the next phase, the NLEO-based indexes
will be compared to visual classification (either raw EEG or aEEG trend) by using an
epoch-based comparison and by using time series methods in cases where both (the index and
visual classification) output time series with comparable features.
Expected results/deliverables Project success will be measured by inclusion of 60-80
patients, measuring their aEEG and Polysomnography as described above and their
neurodevelopmental follow-up at least at the age of two years corrected age, allowing
statistical analysis as described above.
