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Comment
. 2019 May 6;23(1):157.
doi: 10.1186/s13054-019-2444-3.

Does training level affect the accuracy of visual assessment of capillary refill time?

Koichiro Shinozaki  1   2 , Lee S Jacobson  3 , Kota Saeki  4 , Naoki Kobayashi  5 , Steve Weisner  4 , Julianne M Falotico  3 , Timmy Li  3 , Junhwan Kim  6 , Joshua W Lampe  6   7 , Lance B Becker  6   3
Affiliations
Comment

Does training level affect the accuracy of visual assessment of capillary refill time?

Koichiro Shinozaki et al. Crit Care. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board (no. 17-0805). Informed consent for participation was obtained from all patients prior to the completion of any study procedures.

Consent for publication

Not applicable.

Competing interests

LSJ, JMF, TL, and JK have no known conflicts of interest associated with this study, and there has been no significant financial support for this work that could have influenced its outcome. Kota S., NK, and SW are employees of Nihon Kohden Corporation and Nihon Kohden Innovation Center, INC. There are no products in market to declare. This does not alter the authors’ adherence to all the journal’s policies on sharing data and materials. Koichiro S. and LBB have a patent right of metabolic measurements in critically ill patients. Koichiro S. has a grant/research support from Nihon Kohden Corp.. LBB has a grant/research support from Philips Healthcare, the NIH, Nihon Kohden Corp., Zoll Medical Corp, PCORI, BrainCool, and United Therapeutics and owes patents including 7 issued patents and several pending patents involving the use of medical slurries as human coolant devices to create slurries, reperfusion cocktails, and measurement of respiratory quotient.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Image analysis CRT. The recorded videos were used thereafter in a separate setting to calculate CRT via image software analysis (image analysis CRT). Averaged color of the fingernail area was extracted from the digital video file and the color change was represented as RGB waveforms. And then, the RGB waveforms were converted to a grayscale waveform. A curve fitting the returning phase of the grayscale waveform was modeled as an exponential decay using the least squares method. The time to achieve 90% return of fitting curve was reported as “image analysis CRT”
Fig. 2
Fig. 2
Scatter plot showing intra-rater (observer) reliability of video assessment CRT as a function of correlation coefficient of video assessment CRT with image analysis CRT. Attending physicians (22 and 28 years of ED work experience), residents (3 years of ED work experience), nurses (2 years of ED work experience), and physician assistants (1 and 2 years of ED work experience) participated in the study. Six clinicians were actively performing CRT assessments in their clinical work. Observers, who showed higher correlation with image analysis CRT, demonstrated higher intra-rater reliability, and there was a strong correlation between these coefficient values (r = 0.72, p < 0.05)
See this image and copyright information in PMC

Comment on

References

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