Evidence mapPaperPMID 39777307Full record

ArticleFrontiers in neurology2024

Data analysis protocol for early autonomic dysfunction characterization after severe traumatic brain injury.

Kejun Dong, Vijay Krishnamoorthy, Monica S Vavilala, Joseph Miller, Zeljka Minic, Tetsu Ohnuma, Daniel Laskowitz, Benjamin A Goldstein, Luis Ulloa, Huaxin Sheng and 3 more

Abstract read
In one paragraph

Article in Frontiers in neurology, 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

13 authors.

Kejun DongCenter for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States.
Vijay KrishnamoorthyCritical Care and Perioperative Population Health Research (CAPER) Unit, Department of Anesthesiology, Duke University, Durham, NC, United States.
Monica S VavilalaDepartment of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, United States.
Joseph MillerDepartment of Emergency Medicine, Henry Ford Hospital, Detroit, MI, United States.
Zeljka MinicDepartment of Emergency Medicine, School of Medicine, Wayne State University, Detroit, MI, United States.
Tetsu OhnumaDepartment of Anesthesiology, School of Medicine, Duke University, Durham, NC, United States.
Daniel LaskowitzDepartment of Neurology, Duke University Medical Center, Durham, NC, United States.
Benjamin A GoldsteinDepartment of Biostatistics and Bioinformatics, School of Medicine, Duke University, Durham, NC, United States.
Luis UlloaDepartment of Anesthesiology, School of Medicine, Duke University, Durham, NC, United States.
Huaxin ShengDepartment of Anesthesiology, School of Medicine, Duke University, Durham, NC, United States.
Frederick K KorleyDepartment of Emergency Medicine, University of Michigan, Ann Arbor, MI, United States.
William MeurerDepartment of Emergency Medicine, University of Michigan, Ann Arbor, MI, United States.
Xiao HuCenter for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States.

Funding

Impact of Autonomic Dysfunction on Multi-Organ Dysfunction following Severe TBI: The AUTO-BOOST StudyR01NS130832 · DUKE UNIVERSITY · 2025 to 2025
$599k
NINDS NIH HHS R01 NS130832
6 · The paper itself

Abstract

Background: Traumatic brain injury (TBI) disrupts normal brain tissue and functions, leading to high mortality and disability. Severe TBI (sTBI) causes prolonged cognitive, functional, and multi-organ dysfunction. Dysfunction of the autonomic nervous system (ANS) after sTBI can induce abnormalities in multiple organ systems, contributing to cardiovascular dysregulation and increased mortality. Currently, detailed characterization of early autonomic dysfunction in the acute phase after sTBI is lacking. This study aims to use physiological waveform data collected from patients with sTBI to characterize early autonomic dysfunction and its association with clinical outcomes to prevent multi-organ dysfunction and improving patient outcomes. Objective: This data analysis protocol describes our pre-planned protocol using cardiac waveforms to evaluate early autonomic dysfunction and to inform multi-dimensional characterization of the autonomic nervous system (ANS) after sTBI. Methods: We will collect continuous cardiac waveform data from patients managed in an intensive care unit within a clinical trial. We will first assess the signal quality of the electrocardiogram (ECG) using a combination of the structural image similarity metric and signal quality index. Then, we will detect premature ventricular contractions (PVC) on good-quality ECG beats using a deep-learning model. For arterial blood pressure (ABP) data, we will employ a singular value decomposition (SVD)-based approach to assess the signal quality. Finally, we will compute multiple indices of ANS functions through heart rate turbulence (HRT) analysis, time/frequency-domain analysis of heart rate variability (HRV) and pulse rate variability, and quantification of baroreflex sensitivity (BRS) from high-quality continuous ECG and ABP signals. The early autonomic dysfunction will be characterized by comparing the values of calculated indices with their normal ranges. Conclusion: This study will provide a detailed characterization of acute changes in ANS function after sTBI through quantified indices from cardiac waveform data, thereby enhancing our understanding of the development and course of eAD post-sTBI.

Indexed as

arterial blood pressure (ABP)early autonomic dysfunction (eAD)electrocardiogram (ECG)physiological waveformsevere traumatic brain injury (sTBI)

Identifiers

PMID39777307
PMCPMC11704490

What Socratic holds

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Registered trials

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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.