Evidence map›Paper›PMID 38745319›Full record

ArticleCritical care (London, England)2024

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study.

Stefan Yu Bögli, Ihsane Olakorede, Michael Veldeman, Erta Beqiri, Miriam Weiss, Gerrit Alexander Schubert, Jan Folkard Willms, Emanuela Keller, Peter Smielewski

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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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

9 authors.

Stefan Yu BögliBrain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. stefanyu.boegli@usz.ch.
Ihsane OlakoredeBrain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Michael VeldemanDepartment of Neurosurgery, RWTH Aachen University, Aachen, Germany.
Erta BeqiriBrain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Miriam WeissDepartment of Neurosurgery, RWTH Aachen University, Aachen, Germany.
Gerrit Alexander SchubertDepartment of Neurosurgery, RWTH Aachen University, Aachen, Germany.
Jan Folkard WillmsNeurocritical Care Unit, Institute for Intensive Care and Department for Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Emanuela KellerNeurocritical Care Unit, Institute for Intensive Care and Department for Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Peter SmielewskiBrain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.

Funding

Medical Research Council MR N013433-1Wellcome Trust 210839
6 · The paper itself

Abstract

backgroundSignal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet.

methodsaSAH patients from 2 prospective cohorts (Zurich-derivation cohort, Aachen-validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1-4 vs. 5-8) or ordinal outcome (GOSE-extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination.

resultsA total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity.

conclusionsMSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

Indexed as

Subarachnoid HemorrhageAdultAgedCohort StudiesFemaleGlasgow Outcome ScaleHumansLogistic ModelsMaleMiddle AgedPrognosisProspective StudiesComplexityMultiscale entropyOutcomeSubarachnoid hemorrhage

Identifiers

PMID38745319
PMCPMC11092006

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