Evidence map›Paper›PMID 26446002›Full record

ArticleJournal of clinical monitoring and computing2016

Trending autoregulatory indices during treatment for traumatic brain injury.

Nam Kim, Alex Krasner, Colin Kosinski, Michael Wininger, Maria Qadri, Zachary Kappus, Shabbar Danish, William Craelius

Abstract read
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In one paragraph

Article in Journal of clinical monitoring and computing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients.IEEE journal of translational engineering in health and medicine · 2022
    Article
  7. Article
  8. Article
  9. Observational
  10. Article
  11. Review
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

8 authors.

Nam KimDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
Alex KrasnerDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
Colin KosinskiDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
Michael WiningerRehabilitation Sciences, University of Hartford, West Hartford, CT, 06117, USA.
Maria QadriDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
Zachary KappusDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
Shabbar DanishDepartment of Neurosurgery, Rutgers Cancer Institute, Rutgers-RWJ Medical School, New Brunswick, NJ, 08901, USA.
William CraeliusDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. craelius@rci.rutgers.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Our goal is to use automatic data monitoring for reliable prediction of episodes of intracranial hypertension in patients with traumatic brain injury. Here we test the validity of our method on retrospective patient data. We developed the Continuous Hemodynamic Autoregulatory Monitor (CHARM), that siphons and stores signals from existing monitors in the surgical intensive care unit (SICU), efficiently compresses them, and standardizes the search for statistical relationships between any proposed index and adverse events. CHARM uses an automated event detector to reliably locate episodes of elevated intracranial pressure (ICP), while eliminating artifacts within retrospective patient data. A graphical user interface allowed data scanning, selection of criteria for events, and calculating indices. The pressure reactivity index (PRx), defined as the least square linear regression slope of intracranial pressure versus arterial BP, was calculated for a single case that spanned 259 h. CHARM collected continuous records of ABP, ICP, ECG, SpO2, and ventilation from 29 patients with TBI over an 18-month period. Analysis of a single patient showed that PRx data distribution in the single hours immediately prior to all 16 intracranial hypertensive events, significantly differed from that in the 243 h that did not precede such events (p < 0.0001). The PRx index, however, lacked sufficient resolution as a real-time predictor of IH in this patient. CHARM streamlines the search for reliable predictors of intracranial hypertension. We report statistical evidence supporting the predictive potential of the pressure reactivity index.

Indexed as

Electronic Data ProcessingArtifactsAutomationBrain Injuries, TraumaticCerebrovascular CirculationComputer GraphicsHemodynamicsHumansIntensive Care UnitsIntracranial HypertensionIntracranial PressureLeast-Squares AnalysisLinear ModelsMedical InformaticsMonitoring, PhysiologicRetrospective StudiesABPAutoregulationICPInformatics SICUPRxTBITraumatic brain injury

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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 OpenQuestion graph.