Evidence map›Paper›PMID 37730622›Full record

SynthesisCritical care (London, England)2023

Variables influencing the prediction of fluid responsiveness: a systematic review and meta-analysis.

Jorge Iván Alvarado Sánchez, Juan Daniel Caicedo Ruiz, Juan José Diaztagle Fernández, Luís Eduardo Cruz Martínez, Fredy Leonardo Carreño Hernández, Carlos Andrés Santacruz Herrera, Gustavo Adolfo Ospina-Tascón

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Critical care (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 4 pooled it
7.6field-weighted citation impact, top 2% 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

17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 24 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Review
  6. Article
  7. Observational
  8. Observational
  9. Article
  10. Observational
  11. Article
  12. Article
  13. Article
  14. Hemodynamic Support in Sepsis.Anesthesiology · 2024
    Review
  15. Article
  16. Article
  17. 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

7 authors at 4 institutions in 1 country.

Jorge Iván Alvarado SánchezFundación Santa Fe de Bogotá, Department of Intensive Care, Bogotá, Colombia. Jialvarados@unal.edu.co.ORCID https://orcid.org/0000-0003-4320-3150
Juan Daniel Caicedo RuizDepartment of Physiology Sciences, Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, Colombia.
Juan José Diaztagle FernándezDepartment of Physiology Sciences, Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, Colombia.
Luís Eduardo Cruz MartínezDepartment of Physiology Sciences, Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, Colombia.
Fredy Leonardo Carreño HernándezUniversidad de Los Andes, Bogotá, Colombia.
Carlos Andrés Santacruz HerreraFundación Santa Fe de Bogotá, Department of Intensive Care, Bogotá, Colombia.
Gustavo Adolfo Ospina-TascónDepartment of Intensive Care, Fundación Valle del Lili, Cali, Colombia.ORCID https://orcid.org/0000-0001-9370-3298
Universidad Nacional de Colombia · COFundación Santa Fe de Bogotá · COFundación Valle del Lili · COUniversidad de Los Andes · CO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPrediction of fluid responsiveness in acutely ill patients might be influenced by a number of clinical and technical factors. We aim to identify variables potentially modifying the operative performance of fluid responsiveness predictors commonly used in clinical practice.

methodsA sensitive strategy was conducted in the Medline and Embase databases to search for prospective studies assessing the operative performance of pulse pressure variation, stroke volume variation, passive leg raising (PLR), end-expiratory occlusion test (EEOT), mini-fluid challenge, and tidal volume challenge to predict fluid responsiveness in critically ill and acutely ill surgical patients published between January 1999 and February 2023. Adjusted diagnostic odds ratios (DORs) were calculated by subgroup analyses (inverse variance method) and meta-regression (test of moderators). Variables potentially modifying the operative performance of such predictor tests were classified as technical and clinical.

resultsA total of 149 studies were included in the analysis. The volume used during fluid loading, the method used to assess variations in macrovascular flow (cardiac output, stroke volume, aortic blood flow, volume‒time integral, etc.) in response to PLR/EEOT, and the apneic time selected during the EEOT were identified as technical variables modifying the operative performance of such fluid responsiveness predictor tests (p < 0.05 for all adjusted vs. unadjusted DORs). In addition, the operative performance of fluid responsiveness predictors was also influenced by clinical variables such as the positive end-expiratory pressure (in the case of EEOT) and the dose of norepinephrine used during the fluid responsiveness assessment for PLR and EEOT (for all adjusted vs. unadjusted DORs).

conclusionPrediction of fluid responsiveness in critically and acutely ill patients is strongly influenced by a number of technical and clinical aspects. Such factors should be considered for individual intervention decisions.

Indexed as

AortaBlood PressureCardiac OutputDatabases, FactualHumansProspective StudiesCritical careEnd-expiratory occlusion testFluid responsivenessMini-fluid challengePassive leg raisingPulse pressure variationStroke volume variation

Identifiers

PMID37730622
PMCPMC10510289
OpenAlexW4386896462

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.