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The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report

BACKGROUND: The Hypotension Prediction Index (HPI) displays an innovative monitoring tool which predicts intraoperative hypotension before its onset. CASE PRESENTATION: We report the case of an 84-year-old Caucasian woman undergoing major spinal surgery with no possibility for the transfer of blood...

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Autores principales: Denn, Sara, Schneck, Emmanuel, Jablawi, Fidaa, Bender, Michael, Schmidt, Götz, Habicher, Marit, Uhl, Eberhard, Sander, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652964/
https://www.ncbi.nlm.nih.gov/pubmed/36369059
http://dx.doi.org/10.1186/s13256-022-03653-8
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author Denn, Sara
Schneck, Emmanuel
Jablawi, Fidaa
Bender, Michael
Schmidt, Götz
Habicher, Marit
Uhl, Eberhard
Sander, Michael
author_facet Denn, Sara
Schneck, Emmanuel
Jablawi, Fidaa
Bender, Michael
Schmidt, Götz
Habicher, Marit
Uhl, Eberhard
Sander, Michael
author_sort Denn, Sara
collection PubMed
description BACKGROUND: The Hypotension Prediction Index (HPI) displays an innovative monitoring tool which predicts intraoperative hypotension before its onset. CASE PRESENTATION: We report the case of an 84-year-old Caucasian woman undergoing major spinal surgery with no possibility for the transfer of blood products given her status as a Jehovah’s Witness. The hemodynamic treatment algorithm we employed was based on HPI and resulted in a high degree of hemodynamic stability during the surgical procedure. Further, the patient was not at risk for either hypo- or hypervolemia, conditions which might have caused dilution anemia. By using HPI as a tool for patient blood management, it was possible to reduce the incidence of intraoperative hypotension to a minimum. CONCLUSIONS: In sum, this HPI-based treatment algorithm represents a useful application for the treatment of complex anesthesia and perioperative patient blood management. It is a simple but powerful extension of standard monitoring for the prevention of intraoperative hypotension. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13256-022-03653-8.
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spelling pubmed-96529642022-11-15 The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report Denn, Sara Schneck, Emmanuel Jablawi, Fidaa Bender, Michael Schmidt, Götz Habicher, Marit Uhl, Eberhard Sander, Michael J Med Case Rep Case Report BACKGROUND: The Hypotension Prediction Index (HPI) displays an innovative monitoring tool which predicts intraoperative hypotension before its onset. CASE PRESENTATION: We report the case of an 84-year-old Caucasian woman undergoing major spinal surgery with no possibility for the transfer of blood products given her status as a Jehovah’s Witness. The hemodynamic treatment algorithm we employed was based on HPI and resulted in a high degree of hemodynamic stability during the surgical procedure. Further, the patient was not at risk for either hypo- or hypervolemia, conditions which might have caused dilution anemia. By using HPI as a tool for patient blood management, it was possible to reduce the incidence of intraoperative hypotension to a minimum. CONCLUSIONS: In sum, this HPI-based treatment algorithm represents a useful application for the treatment of complex anesthesia and perioperative patient blood management. It is a simple but powerful extension of standard monitoring for the prevention of intraoperative hypotension. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13256-022-03653-8. BioMed Central 2022-11-12 /pmc/articles/PMC9652964/ /pubmed/36369059 http://dx.doi.org/10.1186/s13256-022-03653-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Case Report
Denn, Sara
Schneck, Emmanuel
Jablawi, Fidaa
Bender, Michael
Schmidt, Götz
Habicher, Marit
Uhl, Eberhard
Sander, Michael
The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
title The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
title_full The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
title_fullStr The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
title_full_unstemmed The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
title_short The use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a Jehovah’s Witness: a case report
title_sort use of artificial intelligence and machine learning monitoring to safely administer a fluid-restrictive goal-directed treatment protocol to minimize the risk of transfusion during major spine surgery of a jehovah’s witness: a case report
topic Case Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652964/
https://www.ncbi.nlm.nih.gov/pubmed/36369059
http://dx.doi.org/10.1186/s13256-022-03653-8
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