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Record W4401864565 · doi:10.1038/s41390-024-03484-x

Neuromonitoring practices for neonates with congenital heart disease: a scoping review

2024· review· en· W4401864565 on OpenAlexaff
Andrea C. Pardo, Melisa Carrasco, Pia Wintermark, Denise Nunes, Valerie Y. Chock, Shawn Sen, Courtney J. Wusthoff, Sonia L. Bonifacio, Hany Aly, Vann Chau, Hannah C. Glass, Monica E. Lemmon, Gabrielle deVeber, James P. Boardman, Dawn Gano, Eric S. Peeples, Lara M. Leijser, Firdose Nakwa, Thiviya Selvanathan

Bibliographic record

VenuePediatric Research · 2024
Typereview
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsUniversity of British ColumbiaBC Children's HospitalSickKids FoundationUniversity of TorontoUniversity of CalgaryMcGill University Health Centre
Fundersnot available
KeywordsMedicineElectroencephalographyAdverse effectIntensive care medicineNeonatal intensive care unitHeart diseaseDiseasePediatricsCardiologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Neonates with congenital heart disease (CHD) are at risk for adverse neurodevelopmental outcomes. This scoping review summarizes neuromonitoring methods in neonates with CHD. We identified 84 studies investigating the use of near-infrared spectroscopy (NIRS) (n = 37), electroencephalography (EEG) (n = 20), amplitude-integrated electroencephalography (aEEG) (n = 10), transcranial Doppler sonography (TCD) (n = 6), and multimodal monitoring (n = 11). NIRS was used to evaluate cerebral oxygenation, identify risk thresholds and adverse events in the intensive care unit (ICU), and outcomes. EEG was utilized to screen for seizures and to predict adverse outcomes. Studies of aEEG have focused on characterizing background patterns, detecting seizures, and outcomes. Studies of TCD have focused on correlation with short-term clinical outcomes. Multimodal monitoring studies characterized cerebral physiologic dynamics. Most of the studies were performed in single centers, had a limited number of neonates (range 3-183), demonstrated variability in neuromonitoring practices, and lacked standardized approaches to neurodevelopmental testing. We identified areas of improvement for future research: (1) large multicenter studies to evaluate developmental correlates of neuromonitoring practices; (2) guidelines to standardize neurodevelopmental testing methodologies; (3) research to address geographic variation in resource utilization; (4) integration and synchronization of multimodal monitoring; and (5) research to establish a standardized framework for neuromonitoring techniques across diverse settings. IMPACT: This scoping review summarizes the literature regarding neuromonitoring practices in neonates with congenital heart disease (CHD). The identification of low cerebral oxygenation thresholds with NIRS may be used to identify neonates at risk for adverse events in the ICU or adverse neurodevelopmental outcomes. Postoperative neuromonitoring with continuous EEG screening for subclinical seizures and status epilepticus, allow for early and appropriate therapy. Future studies should focus on enrolling larger multicenter cohorts of neonates with CHD with a standardized framework of neuromonitoring practices in this population. Postoperative neurodevelopmental testing should utilize standard assessments and testing intervals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.659
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.282
GPT teacher head0.547
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2024
Admission routes1
Has abstractyes

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