Prediction of postoperative outcomes using intraoperative hemodynamic monitoring data
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s41598-017-16233-4.pdf
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Main article
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1.55 MB
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Author(s)
Prasad, Varesh
Guerrisi, Maria
Dauri, Mario
Coniglione, Filadelfo
Tisone, Giuseppe
De Carolis, Elisa
Cillis, Annagrazia
Canichella, Antonio
Toschi, Nicola
Heldt, Thomas
Date Issued
November 2017
Journal
Scientific Reports
Publisher
Nature Publishing Group
Citation
Prasad, Varesh et al. “Prediction of Postoperative Outcomes Using Intraoperative Hemodynamic Monitoring Data.” Scientific Reports 7, 1 (November 2017): 16376 © 2017 he Author(s)
Version
Final published version
Abstract
Major surgeries can result in high rates of adverse postoperative events. Reliable prediction of which patient might be at risk for such events may help guide peri- and postoperative care. We show how archiving and mining of intraoperative hemodynamic data in orthotopic liver transplantation (OLT) can aid in the prediction of postoperative 180-day mortality and acute renal failure (ARF), improving upon predictions that rely on preoperative information only. From 101 patient records, we extracted 15 preoperative features from clinical records and 41 features from intraoperative hemodynamic signals. We used logistic regression with leave-one-out cross-validation to predict outcomes, and incorporated methods to limit potential model instabilities from feature multicollinearity. Using only preoperative features, mortality prediction achieved an area under the receiver operating characteristic curve (AUC) of 0.53 (95% CI: 0.44–0.78). By using intraoperative features, performance improved significantly to 0.82 (95% CI: 0.56–0.91, P = 0.001). Similarly, including intraoperative features (AUC = 0.82; 95% CI: 0.66–0.94) in ARF prediction improved performance over preoperative features (AUC = 0.72; 95% CI: 0.50–0.85), though not significantly (P = 0.32). We conclude that inclusion of intraoperative hemodynamic features significantly improves prediction of postoperative events in OLT. Features strongly associated with occurrence of both outcomes included greater intraoperative central venous pressure and greater transfusion volumes.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1038/s41598-017-16233-4
