Evidence map›Paper›PMID 35080751›Full record

ReviewCurrent neurology and neuroscience reports2022

Big data and predictive analytics in neurocritical care.

Ayham Alkhachroum, Julie Kromm, Michael A De Georgia

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current neurology and neuroscience reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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

3 authors at 3 institutions in 2 countries.

Ayham AlkhachroumMiller School of Medicine, Neurocritical Care Division, Department of Neurology, University of Miami, Miami, FL, 33146, USA.
Julie KrommCumming School of Medicine, Department of Critical Care Medicine, University of Calgary, Calgary, AB, Canada.
Michael A De GeorgiaCenter for Neurocritical Care, Neurological Institute, University Hospital Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106-5040, USA. michael.degeorgia@uhhospitals.org.
The Neurological Institute · USUniversity of Calgary · CAUniversity of Miami · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewTo describe predictive data and workflow in the intensive care unit when managing neurologically ill patients. RECENT

findingsIn the era of Big Data in medicine, intensive critical care units are data-rich environments. Neurocritical care adds another layer of data with advanced multimodal monitoring to prevent secondary brain injury from ischemia, tissue hypoxia, and a cascade of ongoing metabolic events. A step closer toward personalized medicine is the application of multimodal monitoring of cerebral hemodynamics, bran oxygenation, brain metabolism, and electrophysiologic indices, all of which have complex and dynamic interactions. These data are acquired and visualized using different tools and monitors facing multiple challenges toward the goal of the optimal decision support system. In this review, we highlight some of the predictive data used to diagnose, treat, and prognosticate the neurologically ill patients. We describe information management in neurocritical care units including data acquisition, wrangling, analysis, and visualization.

Indexed as

Brain InjuriesCritical CareBig DataHumansIntensive Care UnitsMonitoringNeurocritical carePrecision medicinePredictive data

Identifiers

PMID35080751
OpenAlexW4210374669

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.