{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:48:11Z","timestamp":1740170891171,"version":"3.37.3"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"7-8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:sec>\n                  <jats:title>Introduction<\/jats:title>\n                  <jats:p>Inappropriate fluid management during patient transport may lead to casualty morbidity. Percent systolic pressure variation (%SPV) is one of several technologies that perform a dynamic assessment of fluid responsiveness (FT-DYN). Trained anesthesia providers can visually estimate and use %SPV to limit the incidence of erroneous volume management decisions to 1-4%. However, the accuracy of visually estimated %SPV by other specialties is unknown. The aim of this article is to determine the accuracy of estimated %SPV and the incidence of erroneous volume management decisions for Critical Care Air Transport (CCAT) team members before and after training to visually estimate and utilize %SPV.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Material and Methods<\/jats:title>\n                  <jats:p>In one sitting, CCAT team providers received didactics defining %SPV and indicators of fluid responsiveness and treatment with %SPV\u2009\u22647 and \u226514.5 defining a fluid nonresponsive and responsive patient, respectively; they were then shown ten 45-second training arterial waveforms on a simulated Propaq M portable monitor\u2019s screen. Study subjects were asked to visually estimate %SPV for each arterial waveform and queried whether they would treat with a fluid bolus. After each training simulation, they were told the true %SPV. Seven days post-training, the subjects were shown a different set of ten 45-second testing simulations and asked to estimate %SPV and choose to treat, or not. Nonparametric limits of agreement for differences between true and estimated %SPV were analyzed using Bland\u2013Altman graphs. In addition, three errors were defined: (1) %SPV visual estimate errors that would label a volume responsive patient as nonresponsive, or vice versa; (2) incorrect treatment decisions based on estimated %SPV (algorithm application errors); and (3) incorrect treatment decisions based on true %SPV (clinically significant treatment errors). For the training and testing simulations, these error rates were compared between, and within, provider groups.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Sixty-one physicians (MDs), 64 registered nurses (RNs), and 53 respiratory technicians (RTs) participated in the study. For testing simulations, the incidence and 95% CI for %SPV estimate errors with sufficient magnitude to result in a treatment error were 1.4% (0.5%, 3.2%), 1.6% (0.6%, 3.4%), and 4.1% (2.2%, 6.9%) for MDs, RNs, and RTs, respectively. However, clinically significant treatment errors were statistically more common for all provider types, occurring at a rate of 7%, 10%, and 23% (all P\u2009&amp;lt;\u2009.05). Finally, students did not show clinically relevant reductions in their errors between training and testing simulations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Although most practitioners correctly visually estimated %SPV and all students completed the training in interpreting and applying %SPV, all groups persisted in making clinically significant treatment errors with moderate to high frequency. This suggests that the treatment errors were more often driven by misapplying FT-DYN algorithms rather than by inaccurate visual estimation of %SPV. Furthermore, these errors were not responsive to training, suggesting that a decision-making cognitive aid may improve CCAT teams\u2019 ability to apply FT-DYN technologies.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/milmed\/usad281","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T12:51:22Z","timestamp":1690289482000},"page":"1514-1522","source":"Crossref","is-referenced-by-count":0,"title":["The Ability of\u00a0Military Critical Care Air Transport Members to Visually Estimate Percent Systolic Pressure Variation"],"prefix":"10.1093","volume":"189","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8044-5047","authenticated-orcid":false,"given":"Mark A","family":"Cheney","sequence":"first","affiliation":[{"name":"Center for Sustainment of Trauma and Readiness Skills, University of Cincinnati , Cincinnati, OH 45219, USA"},{"name":"Department of Anesthesiology, University of Cincinnati , Cincinnati, OH 45219, 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Emergency Medicine, Wright State University , Dayton, OH 45324, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher","family":"Horn","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Cincinnati , Cincinnati, OH 45219, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Hare","sequence":"additional","affiliation":[{"name":"Center for Sustainment of Trauma and Readiness Skills, University of Cincinnati , Cincinnati, OH 45219, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Alderman","sequence":"additional","affiliation":[{"name":"Center for Sustainment of Trauma and Readiness Skills, University of Cincinnati , Cincinnati, OH 45219, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Nelson","sequence":"additional","affiliation":[{"name":"Center for Sustainment of Trauma and Readiness Skills, University of Cincinnati , 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