JMIR Publications

ABSTRACT

Background:

The arterial pressure-based cardiac output (APCO) is a less-invasive method for estimating the cardiac output without worries about complications from the pulmonary artery catheter (PAC). However, inaccuracies of the currently available APCO devices have been reported. Improvements of the algorithm by researchers are also impossible, since only a subset of the algorithm has been released.

Objective:

In this study, we developed and validated an open source APCO algorithm using convolutional neural network and the transfer learning technique.

Methods:

We did a retrospective study using data from a prospective cohort registry of intraoperative bio-signal data at a university hospital. The convolutional neural network model was trained using the arterial pressure waveform as input and the stroke volume (SV) value as output. The model parameters were pre-trained using the SV values from a commercial APCO device (Vigileo™ or EV1000™ with FloTrac™ algorithm) and adjusted by a transfer learning technique using SV values from the PAC. The performance of the model was evaluated by 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 SV values from the PAC.

Results:

We used 2,057 surgical cases (1,958 training and 99 testing) in the registry for modelling. In the deep learning model, the absolute errors of SV were 14.5 ± 13.4 mL (10.2 ± 8.4 mL and 17.4 ± 15.3 in cardiac surgery and liver transplantation, respectively). In the comparison with FloTrac, the absolute errors of the deep learning model were significantly smaller than those of the FloTrac (16.5 ± 15.4 and 18.3 ± 15.1, respectively, 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. Clinical Trial: Not applicable.