TY  - JOUR
AU  - Yang, Hyun-Lim
AU  - Jung, Chul-Woo
AU  - Yang, Seong Mi
AU  - Kim, Min-Soo
AU  - Shim, Sungho
AU  - Lee, Kook Hyun
AU  - Lee, Hyung-Chul
PY  - 2021
DA  - 2021/8/16
TI  - Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data
JO  - JMIR Med Inform
SP  - e24762
VL  - 9
IS  - 8
KW  - cardiac output
KW  - deep learning
KW  - arterial pressure
AB  - Background: Arterial pressure-based cardiac output (APCO) is a less invasive method for estimating cardiac output without concerns about complications from the pulmonary artery catheter (PAC). However, inaccuracies of currently available APCO devices have been reported. Improvements to the algorithm by researchers are impossible, as only a subset of the algorithm has been released. Objective: In this study, an open-source algorithm was developed and validated using a convolutional neural network and a transfer learning technique. Methods: A retrospective study was performed using data from a prospective cohort registry of intraoperative bio-signal data from a university hospital. The convolutional neural network model was trained using the arterial pressure waveform as input and the stroke volume (SV) value as the output. The model parameters were pretrained using the SV values from a commercial APCO device (Vigileo or EV1000 with the FloTrac algorithm) and adjusted with a transfer learning technique using SV values from the PAC. The performance of the model was evaluated using absolute error for the PAC on the testing dataset from separate periods. Finally, we compared the performance of the deep learning model and the FloTrac with the SV values from the PAC. Results: A total of 2057 surgical cases (1958 training and 99 testing cases) were used in the registry. In the deep learning model, the absolute errors of SV were 14.5 (SD 13.4) mL (10.2 [SD 8.4] mL in cardiac surgery and 17.4 [SD 15.3] mL in liver transplantation). Compared with FloTrac, the absolute errors of the deep learning model were significantly smaller (16.5 [SD 15.4] and 18.3 [SD 15.1], P<.001). Conclusions: The deep learning–based APCO algorithm showed better performance than the commercial APCO device. Further improvement of the algorithm developed in this study may be helpful for estimating cardiac output accurately in clinical practice and optimizing high-risk patient care. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2021/8/e24762
UR  - https://doi.org/10.2196/24762
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34398790
DO  - 10.2196/24762
ID  - info:doi/10.2196/24762
ER  -