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Effect of Transit Time and Gas Density on the Conservation of Agitated Saline Contrast in an <i>in vitro</i> Model of the Pulmonary Circulation

2016· article· en· W4389008124 on OpenAlexafffund
Heather Hackett, Lindsey M. Boulet, Paolo B. Dominelli, Glen E. Foster

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSalineMicrobubblesBiomedical engineeringUltrasoundBolus (digestion)ChemistryMedicineAnesthesiaSurgeryRadiology

Abstract

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Agitated saline contrast echocardiography is often used as a tool to subjectively measure blood flow through intrapulmonary arteriovenous anastomoses (Q̇ IPAVA ). This method assumes that an intravenous injection of agitated saline contrast will not survive the pulmonary circulation or reach the left heart unless it travels through a shunt. However, agitated saline contrast is unstable, and contrast lost to microbubble dissolution likely impacts its utility for measuring Q̇ IPAVA . We applied indicator dilution theory to acoustic intensity‐time curves obtained from a bolus injection of hand‐agitated saline contrast to acquire a quantitative index of contrast mass. Using this methodology and an in vitro model of the pulmonary circulation, the purpose of this study was to determine the effect of transit time on the conservation of contrast mass between two detection sites separated by a convoluted network of vessels with a variable volume (300–600 ml). In addition, we wished to determine if agitating saline with a high density, low solubility gas (sulfur hexafluoride, SF 6 ) could improve contrast mass conservation by minimizing microbubble dissolution. We hypothesized that the contrast lost between the in‐ and out‐flow detection sites would increase with increasing transit times and would be reduced by using microbubbles composed of SF 6 instead of air. The experimental apparatus consisted of a reservoir of 0.9% saline connected to a centrifugal pump and a network of latex surgical tubing mimicking the pulmonary circulation. An in‐ and out‐flow detection site passed under a 3.5 MHz ultrasound transducer to record acoustic intensity‐time curves. A 6 ml bolus of agitated saline contrast (10:1 ratio of saline and gas) was injected into the circuit proximal to the in‐flow detection site and distal to a calibrated flow probe. Transit time was manipulated in two ways: (1) setting flow rate to 1.5 or 2.0 l min −1 ; and, (2) reducing the volume of the circulatory network (no reduction, 25%, 50%, 75%). Five trials were conducted for each combination. Contrast conservation was measured as the ratio of outflow contrast mass to inflow contrast mass, where contrast mass is the respective area under the acoustic intensity‐time curve. Inflow contrast mass did not differ amongst all conditions (P>0.05). Transit time ranged from 9.12 ± 0.03 to 24.47 ± 0.03s as the cross sectional area and flow of the system changed. For air, 53.2 ± 3.4% of contrast was conserved at a transit time of 9.25 ± 0.02s but dropped to 16.0 ± 1.0% at a transit time of 10.17 ± 0.06s. Compared to air, SF 6 contrast conservation was significantly greater (P<0.01) with 98.3 ± 11.5 % and 94.8 ± 5.8% of contrast conserved at a transit time of 10.40 ± 0.21s and 13.46 ± 0.04s respectively. In summary, acoustic‐intensity‐time curves can be used to quantify agitated saline contrast mass but loss of contrast due to microbubble dissolution makes measuring Q̇ IPAVA across varying transit time difficult. Agitated saline mixed with SF 6 is stabilized and may be a suitable alternative for Q̇ IPAVA measurement. Support or Funding Information Funding: NSERC, CFI

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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