Validation of functional Fetal Autonomic Brain Age Score ...€¦ · Validation of functional Fetal...
Transcript of Validation of functional Fetal Autonomic Brain Age Score ...€¦ · Validation of functional Fetal...
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Validation of functional Fetal Autonomic Brain Age Score
fABAS
developed using magnetocardiography for applicability in
cardiotocography
1Hoyer Dirk, 1,2Pytlik Adelina, 3Gonçalves Hernâni, 3,4,5Amorim-Costa Célia, 3,4Bernardes
João, 3,4,5Ayres-de-Campos Diogo, 1Witte Otto W, 2Schleussner Ekkehard, 2Schneider Uwe
1 Jena University Hospital, Biomagnetic Center, Hans Berger Department of Neurology,
2 Jena University Hospital, Department of Obstetrics, Jena, Germany,
3 CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal,
4 Department of Obstetrics and Gynecology, Faculty of Medicine, University of Porto;
Department of Obstetrics and Gynecology, São João Hospital, Portugal;
5 INEB — Institute of Biomedical Engineering, Portugal
Hans Berger Department of Neurology, Biomagnetic Center
Dept. of Obstetrics, Div. of Prenatal Diagnostics and Fetal Physiology
Study Group ‚Prenatal Monitoring of Fetal Maturation‘
ESGCO 2016, Lancaster
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Autonomic Nervous System
prenatal birth postnatal
Assessment of autonomic nervous function
heart rate patterns !
Development of autonomic nervous system and control function
prenatal influences
- Stress
- Physical
- Psychosocial
- Nutrition
- Illness
- Drugs
time
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Formation of fetal behavioural states and heart rate patterns
Pillai and James
Obstet Gynecol 1990
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1F quiet sleep (HRP A)
2F active sleep (HRP B)
4F active awakeness (HRP D)
Nijhuis et al. 1982:
Fetal behavioural states classification based on body
movements, eye movements, heart rate patterns,
>32 weeks gestational age
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HRV Parameter Calculation Interpretation
Fluctuation amplitude amplitude 20-95 inter-quantile distance of
detrended NN interval series
Fluctuation range of heart beat intervals
above an approximated baseline
Complexity gMSE(3) Generalized Mutual Information at
coarse graining level 3 of NN series
Complexity of sympatho-vagal modulation
Pattern formation skewness Skewness of NN interval series Asymmetry, decline of deceleration and
formation of acceleration patterns
pNN5
Percentage of differences between
adjacent NN intervals > 5 ms
Formation of vagal modulations
VLF/LF Ratio between VLF (0.02-0.08 Hz)
and LF (0.08-0.2 Hz) power
Baseline fluctuation in relation to
sympatho-vagal modulations 4
Fundamental of functional fetal Autonomic Brain Age Score fABAS:
Universal indices of evolution and development in nature
• Increasing pattern amplitude
• Increasing complexity
• Pattern formation Hoyer et al. PlosONE 2013
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Autonomic Brain Age Score versus chronological age (MCG recordings)
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fABAS:
fetal Autonomic Brain Age Score
30 min recordings
indetermined state dynamics
10 min sections
□ active sleep (2F, HRP B)
○ quiet sleep (1F, HRP A)
WGA: Week Gestational Age
fABAS (HRV indices that are related to universal developmental indices)
are associated with functional fetal maturation age Hoyer et al. PlosONE 2013
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Functional fetal autonomic brain age score (fABAS)
IUGR ● versus normal ○
Active sleep (2F), 10 min sections
from 30 min recordings identified
Jena data base, Hoyer et al. 2013
State distribution normal
HRP A HRP B (2F) HRP D
113 286 29
26.4 % 66.8 % 6.8 %
State distribution IUGR
HRP A HRP B (2F) HRP D
4 11 5
29 % 55 % 25 %
IntraUterine Growth Restriction – fetal HRV - state depended
IUGR: sonographically estimated weight
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Undetermined state,
5 min recordings
Bochum data base, Hoyer et al. 2015
Active sleep (2F), 10 min sections
from 30 min recordings identified
Jena data base, Hoyer et al. 2013
IntraUterine Growth Restriction – fetal HRV – vs. State indifferent 5 min recordings
Functional fetal autonomic brain age score (fABAS)
IUGR ● versus normal ○ Validation study, state unclear
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4F ?
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Problems for clinical application
Appearance of both behavioural states requires recordings over up to 1 hour and
longer (FIGO recommendation 60 min )
- In shorter investigations valuable diagnostics information might be lost
Recording technology:
- Magnetocardiography (e.g. 1 kHz sampling rate)
- precise RR intervals, low artifact rate, reliable, high quality, but expensive
- Abdominal Electrocardiography (e.g. 1 kHz sampling rate)
- precise RR intervals, high artifact rate, applications not always reliable,
limitations due to physiological conduction/isolation conditions
- Cardiotocography (CTG, ultrasound based)
- Individual heart beats are not detected, correlation based heart rate (from
1.2 sec. segments), 5/2 ms resampling/interpolation, recordings not
continiuously reliable.
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Objective
Validation of functional Fetal Autonomic Brain Age Score fABAS
developed using magnetocardiography (MCG)
for applicability in cardiotocography (CTG)
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Material and Methods
Jena Fetal Monitoring Data Base:
- 390 MCG recordings over 30 min sampled at 1024 Hz (normal fetuses)
- Investigation of NN interval series and their time series resampled at 4 Hz
- Only normal beat intervals (NN) were considered,
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Learn set Validation (test) set
SE R2 SE R2
30min MCGNN 2.68 0.498 MCGres 2.724 0.481
MCGres 2.647 0.510 CTG 3.719 0.345
CTG 3.587 0.391 MCGres 2.724 0.481
Results
Standard error (SE) and coefficient of determinism (R2) of
fitted models (learn sets) and the application of this models to test sets
MCGNN MCGres : Resampling of MCG does not remove relevant information (? ANS controlled modulation maintained ? )
MCGres CTG : Relevant information is lacking in CTG (? recording quality + lacking individual beat detection ? )
CTG MCGres : CTG fitted models effect higher goodness in MCGres application (? wide spread dispersion around “correct mean model” in CTG group ? )
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Standard error (SE) and coefficient of determinism (R2) of
fitted models (learn sets) and the application of this models to test sets
Similar relationships in all data sets:
MCGNN MCGres : Resampling of MCG does not remove relevant information
MCGres CTG : Relevant information is lacking in CTG
CTG MCGres : CTG fitted models effect higher goodness in MCGres application
Learn set Validation (test) set
SE R2 SE R2
30min MCGNN 2.68 0.498 MCGres 2.724 0.481
MCGres 2.647 0.510 CTG 3.719 0.345
CTG 3.587 0.391 MCGres 2.724 0.481
1F MCGNN 2.739 0.498 MCGres 2.742 0.497
MCGres 2.722 0.504 CTG 3.891 0.294
CTG 3.671 0.368 MCGres 2.721 0.505
2F MCGNN 2.929 0.395 MCGres 2.966 0.380
MCGres 2.928 0.395 CTG 3.902 0.257
CTG 3.766 0.308 MCGres 3.026 0.354
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Discussion, Conclusion
- Despite precision loss compared to MCG, CTG recordings provide a valuable
part of information of the fetal functional autonomic maturation age.
- The reduced goodness of functional maturation age assessment in the CTG is
mainly caused by higher random disturbances and less precise instantaneous
heart rate estimation underlying the heart rate time series at 4 Hz resampling.
- Precise heart beat detection is necessary for a sufficient assessment of the
individual fetal functional brain maturation age (autonomic nervous system).
- fABAS was confirmed as being an appropriate candidate for standardized
assessment of functional brain developmental age and developmental
disturbances across different recording techniques.
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Running Project and Call for Collaboration
„Development of a clinic suitable marker of fetal autonomic maturation“ (fABAS)
German Research Foundation (DFG, 2018-2018) with international collaborations
- Data sets of MCG, ECG, CTG of normal fetal maturation
- Consideration of environmental/maternal physiological influences
- Assessment of developmental disturbances (MCG, ECG, CTG data sets)
- Standards and calibration links across different recording techniques
- Launch a subsequent extended multi-center study on fetal maturation disturbances
by 2018
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Acknowledgements
Team and Collaborators of „Study Group ‚Prenatal Monitoring of Fetal Maturation‘ Hans Berger Department of Neurology, Biomagnetic Center, Jena University Hospital
Dept. of Obstetrics, Div. of Prenatal Diagnostics and Fetal Physiology, Jena University Hospital
Samuel Nowack, Florian Tetschke, Jana Ziegler, Anja Rudolph, Ulrike Wallwitz, Liviu Moraru, Isabelle
Kynass, Franziska Bode, Martin Bogdanski, Eva-Maria Kowalski, Esther Heinicke. Susan Jaekel,
Franziska Jaenicke, Angelika Stacke, Carolin Michael, Janine Tegtmeier, Kathrin Kumm, Adelina Pytlik,
Sophia Leibl, Alexander Schmid, Stefan Claus, Ekkehard Schleussner, Uwe Schneider, Dirk Hoyer
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Funding
DH, US: German Research Foundation: „Development of a clinic suitable marker of
fetal autonomic maturation“ (DFG: Ho 1634/15-12, Schn 775/7-1),
Hernâni Gonçalves: post-doctoral grant (SFRH/BPD/69671/2010), Portugal.
Collaborating Partners
Teams in Porto
Peter van Leeuwen, Bochum
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