Evidence map›Paper›PMID 20670411›Full record

ArticleCritical care (London, England)2010

Bedside quantification of dead-space fraction using routine clinical data in patients with acute lung injury: secondary analysis of two prospective trials.

Hassan Siddiki, Marija Kojicic, Guangxi Li, Murat Yilmaz, Taylor B Thompson, Rolf D Hubmayr, Ognjen Gajic

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Article in Critical care (London, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Hassan SiddikiDepartment of Radiology, Mayo Clinic College of Medicine, 200 1stStreet, Rochester 55905, USA. hassansiddiki@hotmail.com.
Marija Kojicic
Guangxi Li
Murat Yilmaz
Taylor B Thompson
Rolf D Hubmayr
Ognjen Gajic

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDead-space fraction (Vd/Vt) has been shown to be a powerful predictor of mortality in acute lung injury (ALI) patients. The measurement of Vd/Vt is based on the analysis of expired CO2 which is not a part of standard practice thus limiting widespread clinical application of this method. The objective of this study was to determine prognostic value of Vd/Vt estimated from routinely collected pulmonary variables.

methodsSecondary analysis of the original data from two prospective studies of ALI patients. Estimated Vd/Vt was calculated using the rearranged alveolar gas equation: Vd/Vt=1-[(0.86×VCO2est)/(VE×PaCO2)] where VCO2est is the estimated CO2 production calculated from the Harris Benedict equation, minute ventilation (VE) is obtained from the ventilator rate and expired tidal volume and PaCO2 from arterial gas analysis. Logistic regression models were created to determine the prognostic value of estimated Vd/Vt.

resultsOne hundred and nine patients in Mayo Clinic validation cohort and 1896 patients in ARDS-net cohort demonstrated an increase in percent mortality for every 10% increase in Vd/Vt in a dose response fashion. After adjustment for non-pulmonary and pulmonary prognostic variables, both day 1 (adjusted odds ratio-OR = 1.07, 95%CI 1.03 to 1.13) and day 3 (OR = 1.12, 95% CI 1.06 to 1.18) estimated dead-space fraction predicted hospital mortality.

conclusionsElevated estimated Vd/Vt predicts mortality in ALI patients in a dose response manner. A modified alveolar gas equation may be of clinical value for a rapid bedside estimation of Vd/Vt, utilizing routinely collected clinical data.

Indexed as

Point-of-Care SystemsAcute Lung InjuryAgedBlood Gas AnalysisCarbon DioxideFemaleHospital MortalityHumansLogistic ModelsMaleMiddle AgedOxygenPredictive Value of TestsProspective StudiesRespiratory Dead SpaceCarbon DioxideOxygen

Identifiers

PMID20670411
PMCPMC2945122

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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.