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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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Detalles Bibliográficos
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
Descripción
Sumario: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.