Literature DB >> 25167610

Accuracy of blood loss estimations among anesthesia providers.

Ashleigh R Adkins, Doohee Lee, Delilah J Woody, William Asa White.   

Abstract

Research regarding the accuracy of estimating blood loss by anesthesia providers has been limited, generally consisting of small sample sizes with conflicting findings. We conducted a prospective study using a convenience sample of 91 anesthesia providers: 36 Certified Registered Nurse Anesthetists (CRNAs), 48 student registered nurse anesthetists (SRNAs), and 7 anesthesiologists. We applied a known volume of moulage (theater) blood to items at 4 stations and asked each participant to estimate the amount of blood present at each station. Descriptive and inferential statistical analyses were performed. Multivariate regression analysis was conducted to evaluate the association of estimation errors with covariates (provider training, gender, ethnicity, years of education, and years of anesthesia experience). We found no difference in the mean error of blood loss estimation based on provider training, gender, ethnicity, education, or experience. Although no statistically significant differences in blood loss estimations existed among provider groups, CRNAs had smaller mean errors than anesthesiologists in all 4 stations and smaller mean errors than SRNAs in 3 of 4 stations. Accuracy of estimating blood loss, measured by mean error, was not related to type of provider training, years of education or experience, gender, or ethnicity. Anesthesia providers may not estimate blood loss accurately.

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Year:  2014        PMID: 25167610

Source DB:  PubMed          Journal:  AANA J        ISSN: 0094-6354


  12 in total

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Journal:  Ir J Med Sci       Date:  2022-04-07       Impact factor: 1.568

2.  The impact of perioperative red blood cell transfusions in patients undergoing liver resection: a systematic review protocol.

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Journal:  Syst Rev       Date:  2016-02-29

3.  Blood Loss Estimation in Small Animals and Assessment of a Pictorial Tool to Improve Accuracy in a Global Population of Veterinary Anesthesia Staff.

Authors:  Scott H Cumming; Fernando Martinez-Taboada
Journal:  Front Vet Sci       Date:  2020-05-07

4.  Single intravenous bolus versus perioperative continuous infusion of tranexamic acid to reduce blood loss in abdominal oncosurgical procedures: A prospective randomized double-blind clinical study.

Authors:  Ramakrishna Prasad; Abhiruchi Patki; Shibany Padhy; Gopinath Ramchandran
Journal:  J Anaesthesiol Clin Pharmacol       Date:  2018 Oct-Dec

5.  Guidelines on the intraoperative transfusion of red blood cells: a protocol for systematic review.

Authors:  Laura Baker; Lily Park; Richard Gilbert; Andre Martel; Hilalion Ahn; Alexandra Davies; Daniel I McIsaac; Elianna Saidenberg; Alan Tinmouth; Dean A Fergusson; Guillaume Martel
Journal:  BMJ Open       Date:  2019-06-17       Impact factor: 2.692

6.  Visual estimates of blood loss by medical laypeople: Effects of blood loss volume, victim gender, and perspective.

Authors:  Rachel Phillips; Marc Friberg; Mattias Lantz Cronqvist; Carl-Oscar Jonson; Erik Prytz
Journal:  PLoS One       Date:  2020-11-12       Impact factor: 3.240

7.  AN AUDIT OF ORAL AND MAXILLOFACIAL PROCEDURES UNDER GENERAL ANAESTHESIA AT THE UNIVERSITY COLLEGE HOSPITAL IBADAN, NIGERIA.

Authors:  A A Olusanya; T O Aladelusi; B B Osinaike; S Akinloye; J T Arotiba
Journal:  J West Afr Coll Surg       Date:  2017 Jan-Mar

8.  Blood Loss Estimation Using Gauze Visual Analogue.

Authors:  Emran Ali Algadiem; Abdulmohsen Ali Aleisa; Huda Ibrahim Alsubaie; Noora Radhi Buhlaiqah; Jihad Bagir Algadeeb; Hussain Ali Alsneini
Journal:  Trauma Mon       Date:  2016-05-03

9.  Independent predisposing factors for subcutaneous and deep wound collection after total thyroidectomy, a prospective cohort study.

Authors:  Ahmad Mahmoud Eweida; Hafsa Mohamed Ebeed; Mahmoud Fathy Sakr; Yasser Hamza; Essam Gabr; Tarek Koraitim; Hatem Fawzy Al-Wagih; Waleed Abo-Elwafa; Tarek Ezzat Abdel-Aziz; Ayman Sameh Nabawi
Journal:  Ann Med Surg (Lond)       Date:  2018-10-14

10.  A better method for the dynamic, precise estimating of blood/haemoglobin loss based on deep learning of artificial intelligence.

Authors:  Yu-Jie Li; Li-Ge Zhang; Hong-Yu Zhi; Kun-Hua Zhong; Wen-Quan He; Yang Chen; Zhi-Yong Yang; Lin Chen; Xue-Hong Bai; Xiao-Lin Qin; Dan-Feng Li; Dan-Dan Wang; Jian-Teng Gu; Jiao-Lin Ning; Kai-Zhi Lu; Ju Zhang; Zheng-Yuan Xia; Yu-Wen Chen; Bin Yi
Journal:  Ann Transl Med       Date:  2020-10
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