Development of a Deep Learning Network to Classify Inferior Vena Cava Collapse to Predict Fluid Responsiveness.

Autor: Blaivas M; Department of Medicine, University of South Carolina School of Medicine, Columbia, South Carolina, USA.; Department of Emergency Medicine, St Francis Hospital, Columbus, Georgia, USA., Blaivas L; Michigan State University, East Lansing, Michigan, USA., Philips G; Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA., Merchant R; Department of Emergency Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA., Levy M; Department of Medicine, Division of Pulmonary Critical Care and Sleep, Warren Alert Medical School of Brown University, Providence, Rhode Island, USA., Abbasi A; Department of Medicine, Division of Pulmonary Critical Care and Sleep, Warren Alert Medical School of Brown University, Providence, Rhode Island, USA., Eickhoff C; Brown Center for Biomedical Informatics, Brown University, Providence, Rhode Island, USA., Shapiro N; Department of Emergency Medicine, Beth Israel Deaconess Hospital, Harvard Medical School, Boston, Massachusetts, USA., Corl K; Department of Medicine, Division of Pulmonary Critical Care and Sleep, Warren Alert Medical School of Brown University, Providence, Rhode Island, USA.
Jazyk: angličtina
Zdroj: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine [J Ultrasound Med] 2021 Aug; Vol. 40 (8), pp. 1495-1504. Date of Electronic Publication: 2020 Oct 10.
DOI: 10.1002/jum.15527
Abstrakt: Objectives: To create a deep learning algorithm capable of video classification, using a long short-term memory (LSTM) network, to analyze collapsibility of the inferior vena cava (IVC) to predict fluid responsiveness in critically ill patients.
Methods: We used a data set of IVC ultrasound (US) videos to train the LSTM network. The data set was created from IVC US videos of spontaneously breathing critically ill patients undergoing intravenous fluid resuscitation as part of 2 prior prospective studies. We randomly selected 90% of the IVC videos to train the LSTM network and 10% of the videos to test the LSTM network's ability to predict fluid responsiveness. Fluid responsiveness was defined as a greater than 10% increase in the cardiac index after a 500-mL fluid bolus, as measured by bioreactance.
Results: We analyzed 211 videos from 175 critically ill patients: 191 to train the LSTM network and 20 to test it. Using standard data augmentation techniques, we increased our sample size from 191 to 3820 videos. Of the 175 patients, 91 (52%) were fluid responders. The LSTM network was able to predict fluid responsiveness moderately well, with an area under the receiver operating characteristic curve of 0.70 (95% confidence interval [CI], 0.43-1.00), a positive likelihood ratio of infinity, and a negative likelihood ratio of 0.3 (95% CI, 0.12-0.77). In comparison, point-of-care US experts using video review offline and manual diameter measurement via software caliper tools achieved an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.83-0.99).
Conclusions: We demonstrated that an LSTM network can be trained by using videos of IVC US to classify IVC collapse to predict fluid responsiveness. Our LSTM network performed moderately well given the small training cohort but worse than point-of-care US experts. Further training and testing of the LSTM network with a larger data sets is warranted.
(© 2020 American Institute of Ultrasound in Medicine.)
Databáze: MEDLINE
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