Evidence map›Paper›PMID 39680255›Full record

ArticleActa neurochirurgica2024

Exploration of simultaneous transients between cerebral hemodynamics and the autonomic nervous system using windowed time-lagged cross-correlation matrices: a CENTER-TBI study.

Agnieszka Uryga, Cyprian Mataczyński, Adam I Pelah, Małgorzata Burzyńska, Chiara Robba, Marek Czosnyka, CENTER-TBI high-resolution sub-study participants and investigators

Abstract read
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In one paragraph

Article in Acta neurochirurgica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Agnieszka UrygaDepartment of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland. agnieszka.uryga@pwr.edu.pl.
Cyprian MataczyńskiDepartment of Computer Engineering, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wroclaw, Poland.
Adam I PelahBrain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke Hospital, University of Cambridge, Cambridge, UK.
Małgorzata BurzyńskaClinical Department of Anesthesiology and Intensive Care, Faculty of Medicine, Wroclaw Medical University, Wroclaw, Poland.
Chiara RobbaIRCCS Policlinico San Martino, Genoa, Italy.
Marek CzosnykaBrain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke Hospital, University of Cambridge, Cambridge, UK.
CENTER-TBI high-resolution sub-study participants and investigators

Funding

Narodowe Centrum Nauki UMO-2022/47/D/ST7/00229
6 · The paper itself

Abstract

backgroundTraumatic brain injury (TBI) can significantly disrupt autonomic nervous system (ANS) regulation, increasing the risk for secondary complications, hemodynamic instability, and adverse outcome. This retrospective study evaluated windowed time-lagged cross-correlation (WTLCC) matrices for describing cerebral hemodynamics-ANS interactions to predict outcome, enabling identifying high-risk patients who may benefit from enhanced monitoring to prevent complications.

methodsThe first experiment aimed to predict short-term outcome using WTLCC-based convolution neural network models on the Wroclaw University Hospital (WUH) database (P

resultsFor short-term outcome prediction, the best-performing WTLCC-based model used ICP-LF/HF matrices. It had an area under the curve (AUC) of 0.80, vs. 0.71 for averages of ANS and cerebral hemodynamics metrics, adjusted for clinical metadata. For long-term outcome prediction, the best-score WTLCC-based model used ICP-LF/HF matrices. It had an AUC of 0.63, vs. 0.66 for adjusted neuromonitoring parameters.

conclusionsAmong all neuromonitoring parameters, ICP and LF/HF signals were the most effective in generating the WTLCC matrices. WTLCC-based model outperformed adjusted neuromonitoring parameters in short-term but had moderate utility in long-term outcome prediction.

Indexed as

Autonomic Nervous SystemBrain Injuries, TraumaticCerebrovascular CirculationHemodynamicsAdultBaroreflexFemaleHeart RateHumansIntracranial PressureMaleMiddle AgedNeural Networks, ComputerRetrospective StudiesYoung AdultAutonomic nervous systemBrain–heart couplingMachine learningTraumatic brain injury

Identifiers

PMID39680255
PMCPMC11649841

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.