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Record W4387798610 · doi:10.1089/neu.2023.0374

Intracranial Pressure-Derived Cerebrovascular Reactivity Indices and Their Critical Thresholds: A Canadian High Resolution-Traumatic Brain Injury Validation Study

2023· article· en· W4387798610 on OpenAlexafffundabout
Kevin Y. Stein, Logan Froese, Mypinder Sekhon, Donald Griesdale, Eric Peter Thelin, Rahul Raj, Jeanette Tas, Marcel Aries, Clare Gallagher, Françis Bernard, Alwyn Gomez, Andreas H. Kramer, Frederick A. Zeiler

Bibliographic record

VenueJournal of Neurotrauma · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversité de MontréalUniversity of CalgaryHotchkiss Brain InstituteUniversity of British ColumbiaUniversity of Manitoba
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaFondation Brain CanadaResearch ManitobaHealth Sciences Centre Foundation
KeywordsTraumatic brain injuryIntracranial pressureMedicineCerebral perfusion pressureNeurointensive careUnivariateInternal medicineCardiologyAnesthesiaMultivariate statisticsPerfusionStatistics

Abstract

fetched live from OpenAlex

Current neurointensive care guidelines recommend intracranial pressure (ICP) and cerebral perfusion pressure (CPP) centered management for moderate-severe traumatic brain injury (TBI) because of their demonstrated associations with patient outcome. Cerebrovascular reactivity metrics, such as the pressure reactivity index (PRx), pulse amplitude index (PAx), and RAC index, have also demonstrated significant prognostic capabilities with regard to outcome. However, critical thresholds for cerebrovascular reactivity indices have only been identified in two studies conducted at the same center. In this study, we aim to determine the critical thresholds of these metrics by leveraging a unique multi-center database. The study included a total of 354 patients from the CAnadian High-Resolution TBI (CAHR-TBI) Research Collaborative. Based on 6-month Glasgow Outcome Scores, patients were dichotomized into alive versus dead and favorable versus unfavorable. Chi-square values were then computed for incrementally increasing values of each physiological parameter of interest against outcome. The values that generated the greatest chi-squares for each parameter were considered to be the thresholds with the greatest outcome discriminatory capacity. To confirm that the identified thresholds provide prognostic utility, univariate and multivariable logistical regression analyses were performed adjusting for the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) variables. Through the chi-square analysis, a lower limit CPP threshold of 60 mm Hg and ICP thresholds of 18 mm Hg and 22 mm Hg were identified for both survival and favorable outcome predictions. For the cerebrovascular reactivity metrics, different thresholds were identified for the two outcome dichotomizations. For survival prediction, thresholds of 0.35, 0.25, and 0 were identified for PRx, PAx, and RAC, respectively. For favorable outcome prediction, thresholds of 0.325, 0.20, and 0.05 were found. Univariate logistical regression analysis demonstrated that the time spent above/below thresholds were associated with outcome. Further, multivariable logistical regression analysis found that percent time above/below the identified thresholds added additional variance to the IMPACT core model for predicting both survival and favorable outcome. In this study, we were able to validate the results of the previous two works as well as to reaffirm the ICP and CPP guidelines from the Brain Trauma Foundation (BTF) and the Seattle International Severe Traumatic Brain Injury Consensus Conference (SIBICC).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.321
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2023
Admission routes3
Has abstractyes

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