Evidence map›Paper›PMID 28816837›Full record

ArticleCritical care medicine2017

Individualizing Thresholds of Cerebral Perfusion Pressure Using Estimated Limits of Autoregulation.

Joseph Donnelly, Marek Czosnyka, Hadie Adams, Chiara Robba, Luzius A Steiner, Danilo Cardim, Brenno Cabella, Xiuyun Liu, Ari Ercole, Peter John Hutchinson and 3 more

Abstract read
In one paragraph

Article in Critical care medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers.

0numbers the graph read from it
0cells of the map it votes in
91citing 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

91 citing papers in PubMed.

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31 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Joseph Donnelly1Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge, United Kingdom. 2Institute of Electronic Systems, Warsaw University of Technology, Warsaw, Poland. 3Division of Anaesthesia, Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom. 4Department of Neuroscience, University of Genoa, Italy. 5Department of Anesthesia, Surgical Intensive Care, Prehospital Emergency Medicine and Pain Therapy, University Hospital Basel, University of Basel, Basel, Switzerland. 6Division of Neurosurgery, Department of Clinical Neurosciences, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom. 7Department of Intensive Care, University of Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands.
Marek Czosnyka
Hadie Adams
Chiara Robba
Luzius A Steiner
Danilo Cardim
Brenno Cabella
Xiuyun Liu
Ari Ercole
Peter John Hutchinson
David Krishna Menon
Marcel J H Aries
Peter Smielewski

Funding

Department of Health NIHR-RP-R3-12-013Medical Research Council G0600986Medical Research Council G0601025
6 · The paper itself

Abstract

objectivesIn severe traumatic brain injury, cerebral perfusion pressure management based on cerebrovascular pressure reactivity index has the potential to provide a personalized treatment target to improve patient outcomes. So far, the methods have focused on identifying "one" autoregulation-guided cerebral perfusion pressure target-called "cerebral perfusion pressure optimal". We investigated whether a cerebral perfusion pressure autoregulation range-which uses a continuous estimation of the "lower" and "upper" cerebral perfusion pressure limits of cerebrovascular pressure autoregulation (assessed with pressure reactivity index)-has prognostic value.

designSingle-center retrospective analysis of prospectively collected data.

settingThe neurocritical care unit at a tertiary academic medical center. PATIENTS: Data from 729 severe traumatic brain injury patients admitted between 1996 and 2016 were used. Treatment was guided by controlling intracranial pressure and cerebral perfusion pressure according to a local protocol.

interventionsNone. METHODS AND MAIN

resultsCerebral perfusion pressure-pressure reactivity index curves were fitted automatically using a previously published curve-fitting heuristic from the relationship between pressure reactivity index and cerebral perfusion pressure. The cerebral perfusion pressure values at which this "U-shaped curve" crossed the fixed threshold from intact to impaired pressure reactivity (pressure reactivity index = 0.3) were denoted automatically the "lower" and "upper" cerebral perfusion pressure limits of reactivity, respectively. The percentage of time with cerebral perfusion pressure below (%cerebral perfusion pressure < lower limit of reactivity), above (%cerebral perfusion pressure > upper limit of reactivity), or within these reactivity limits (%cerebral perfusion pressure within limits of reactivity) was calculated for each patient and compared across dichotomized Glasgow Outcome Scores. After adjusting for age, initial Glasgow Coma Scale, and mean intracranial pressure, percentage of time with cerebral perfusion pressure less than lower limit of reactivity was associated with unfavorable outcome (odds ratio %cerebral perfusion pressure < lower limit of reactivity, 1.04; 95% CI, 1.02-1.06; p < 0.001) and mortality (odds ratio, 1.06; 95% CI, 1.04-1.08; p < 0.001).

conclusionsIndividualized autoregulation-guided cerebral perfusion pressure management may be a plausible alternative to fixed cerebral perfusion pressure threshold management in severe traumatic brain injury patients. Prospective randomized research will help define which autoregulation-guided method is beneficial, safe, and most practical.

Indexed as

Academic Medical CentersAdultAge FactorsBrain Injuries, TraumaticCerebrovascular CirculationCritical CareFemaleGlasgow Coma ScaleHomeostasisHumansIntracranial PressureMaleMiddle AgedRetrospective StudiesTime Factors

Identifiers

PMID28816837
PMCPMC5595234

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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.