Although the stroke volume (SV) estimation by arterial blood pressure has been widely used in clinical practice, its accuracy is questionable, especially during periods of hemodynamic instability. We aimed to create novel SV estimating model based on deep-learning (DL) method. A convolutional neural network was applied to estimate SV from arterial blood pressure waveform data recorded from liver transplantation (LT) surgeries. The model was trained using a gold standard referential SV measured via pulmonary artery thermodilution method. Merging a gold standard SV and corresponding 10.24 seconds of arterial blood pressure waveform as an input/output data set with 2-senconds of sliding overlap, 484,384 data sets from 34 LT surgeries were used for training and validation of DL model. The performance of DL model was evaluated by correlation and concordance analyses in another 491,353 data sets from 31 LT surgeries. We also evaluated the performance of pre-existing commercialized model (EV1000), and the performance results of DL model and EV1000 were compared. The DL model provided an acceptable performance throughout the surgery (r = 0.813, concordance rate = 74.15%). During the reperfusion phase, where the most severe hemodynamic instability occurred, DL model showed superior correlation (0.861; 95% Confidence Interval, (CI), 0.855-0.866 vs. 0.570; 95% CI, 0.556-0.584, P < 0.001) and higher concordance rate (90.6% vs. 75.8%) over EV1000. In conclusion, the DL-based model was superior for estimating intraoperative SV and thus might guide physicians to precise intraoperative hemodynamic management. Moreover, the DL model seems to be particularly promising because it outperformed EV1000 in circumstance of rapid hemodynamic changes where physicians need most help.
Keywords: cardiac output; hemodynamic monitoring; intraoperative monitoring; machine learning; perioperative care; stroke volume.