Dataset Information

Validation of a clinically applicable device for fast and accurate quantification of blood volume.

ABSTRACT:

Introduction

Determination of blood volume (BV) using the dual-isotope (e.g., ^99m Tc-labeled red blood cells [^99m Tc-RBC] and ¹²⁵ I-labeled human serum albumin [¹²⁵ I-HSA]) injection method is limited in medicine due to the long isotope half-life. However, BV has been determined in laboratory settings for 100 years using the carbon monoxide (CO)-rebreathing-based procedure, which allows frequent BV measurements.

Methods

We investigated the reliability and accuracy of a semi-automated CO-rebreathing device by comparing it against the dual-isotope methodology and its ability to detect a known blood removal. In study A, BV was determined three times in ~2 h; twice using the device with rebreathing protocols lasting 2 (CO_2min ) and 10 min (CO_10min ) and once with the dual-isotope technique. In study B, the accuracy of the device was assessed by its ability to detect a 2% removal of BV.

Results

A good correlation was observed between both the CO-rebreathing protocols (r² = 0.89-0.98; p < 0.001) and the dual-isotope approach (r² = 0.89-0.95; p < 0.001). In absolute terms BV was 425 ± 263 mL and 491 ± 388 mL lower (p < 0.001) when quantified with the dual-isotope compared to the CO-rebreathing protocols. When reducing BV by 132 ± 25 mL (2%), the device quantified a lower (p < 0.001) BV of 150 ± 45 mL.

Conclusion

This study emphasizes that the semi-automated device accurately determines small changes (i.e., 2%) in BV and that a high correlation with the dual-isotope methodology exists. The findings are clinically relevant owing to the method's simple and fast nature (the absence of radioactive tracers and reduced time requirements, i.e., ~15 min vs. ~180 min) and the possibility for repeated measurements within a single day.

SUBMITTER: Breenfeldt Andersen A

PROVIDER: S-EPMC10388222 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Validation of a clinically applicable device for fast and accurate quantification of blood volume.

Breenfeldt Andersen Andreas A Bonne Thomas C TC Hansen Joar J Oturai Peter P Lundby Carsten C

Journal of clinical laboratory analysis 20230501 9-10

<h4>Introduction</h4>Determination of blood volume (BV) using the dual-isotope (e.g., <sup>99m</sup> Tc-labeled red blood cells [<sup>99m</sup> Tc-RBC] and <sup>125</sup> I-labeled human serum albumin [<sup>125</sup> I-HSA]) injection method is limited in medicine due to the long isotope half-life. However, BV has been determined in laboratory settings for 100 years using the carbon monoxide (CO)-rebreathing-based procedure, which allows frequent BV measurements.<h4>Methods</h4>We investigated t ...[more]

PMID: 37332175

Similar Datasets

Project description:Background Three-dimensional (3D) fractional moving blood volume (FMBV) derived from 3D power Doppler US has been proposed for noninvasive approximation of perfusion. However, 3D FMBV has never been applied in animals against a ground truth. Purpose To determine the correlation between 3D FMBV and the reference standard of fluorescent microspheres (FMS) for measurement of renal perfusion in a porcine model. Materials and Methods From February 2017 to September 2017, adult pigs were administered FMS before and after measurement of renal 3D FMBV at baseline (100%) and approximately 75%, 50%, and 25% flow levels by using US machines from two different vendors. The 3D power Doppler US volumes were converted and segmented, and correlations between FMS and 3D FMBV were made with simple linear regression (r2). Similarity and reproducibility of manual segmentation were determined with the Dice similarity coefficient and 3D FMBV reproducibility (intraclass correlation coefficient [ICC]). Results Thirteen pigs were studied with 33 flow measurements. Kidney volume (mean Dice similarity coefficient ± standard deviation, 0.89 ± 0.01) and renal segmentation (coefficient of variation = 12.6%; ICC = 0.86) were consistent. The 3D FMBV calculations had high reproducibility (ICC = 0.97; 95% confidence interval: 0.96, 0.98). The 3D FMBV per-pig correlation showed excellent correlation for US machines from both vendors (mean r2 = 0.96 [range, 0.92-1.0] and 0.93 [range, 0.78-1.0], respectively). The correlation between 3D FMBV and perfusion measured with microspheres was high for both US machines (r2 = 0.80 [P < .001] and 0.70 [P < .001], respectively). Conclusion The strong correlation between three-dimensional (3D) fractional moving blood volume (FMBV) and fluorescent microspheres indicates that 3D FMBV shows excellent correlation to perfusion and good reproducibility. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Morrell et al in this issue.

Project description:BackgroundDespite the clinical importance of assessing the efficiency and accuracy of fluency in terms of content words production during connected speech, assessments based on discourse tasks are very time-consuming and thus not clinically feasible.Aims(1) Examine the relationship between single-word naming and word retrieval during discourse production. (2) Investigate the relationship between word retrieval and content word fluency derived from a simple versus naturalistic discourse tasks. (3) Develop and validate an efficient and accurate index of content word fluency that is clinically viable.MethodsTwo discourse tasks (simple picture description and naturalistic storytelling narrative) were collected from 46 participants with post-stroke aphasia, and 20 age/education matched neuro-typical controls. Each discourse sample was fully transcribed and quantitative analysis was applied to each sample to measure word retrieval and content word fluency. Three single-word naming tasks were also administered to each participant with aphasia.ResultsCorrelational analyses between single-word naming and word retrieval in connected speech revealed weak/moderate relationships. Conversely, strong correlations were found between measures derived from simple picture description against naturalistic storytelling discourse tasks. Moreover, we derived a novel, transcription-less index of content word fluency from the discourse samples of an independent group (neuro-typical controls), and then we validated this index across two discourse tasks in the tested group (persons with aphasia). Correlation and regression analyses revealed extremely strong relationships between participants' (neuro-typical controls and persons with aphasia) scores on the novel index and measures of content word fluency derived from the formal transcription and quantitative analyses of discourse samples, indicating high accuracy and validity of the new index.ConclusionsSimple picture description rather than picture naming provides a better estimate of word retrieval in naturalistic connected speech. The novel developed index is transcription-less and can be implemented online to provide an accurate and efficient measure of content word fluency. Thus, it is viable during clinical practice for assessment purposes, and possibly as an outcome measure to monitor therapy effectiveness, which can also be used in randomised clinical trials.

Project description:BackgroundOver half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain.ObjectiveAdopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice.MethodsThe model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions.ResultsWe demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training.ConclusionsDeep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Dataset Information

Validation of a clinically applicable device for fast and accurate quantification of blood volume.

Introduction

Methods

Results

Conclusion

Publications

Validation of a clinically applicable device for fast and accurate quantification of blood volume.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets