{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:20:28Z","timestamp":1777566028106,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T00:00:00Z","timestamp":1605571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1312000"],"award-info":[{"award-number":["2018YFC1312000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 \u00b1 3.52 kg) underwent a controlled hemorrhage at 20 mL\/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean \u00b1 standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 \u00b1 156.2, 6.5 \u00b1 161.5, and 7.0 \u00b1 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 \u00b1 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV.<\/jats:p>","DOI":"10.3390\/s20226558","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T07:23:28Z","timestamp":1605597808000},"page":"6558","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4890-313X","authenticated-orcid":false,"given":"Yang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Electronics and Information Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengcheng","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6166-700X","authenticated-orcid":false,"given":"Michael R.","family":"Pinsky","sequence":"additional","affiliation":[{"name":"Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gilles","family":"Clermont","sequence":"additional","affiliation":[{"name":"Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1007\/s00134-017-4895-9","article-title":"The research agenda for trauma critical care","volume":"43","author":"Asehnoune","year":"2017","journal-title":"Intensive Care Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1097\/CCM.0000000000003407","article-title":"ICU Management of Trauma Patients","volume":"46","author":"Tisherman","year":"2018","journal-title":"Crit. 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