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Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data
- Source :
- JMIR Medical Informatics
- Publication Year :
- 2021
- Publisher :
- JMIR Publications, 2021.
-
Abstract
- 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 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.
- Subjects :
- Cardiac output
Original Paper
Computer science
business.industry
medicine.medical_treatment
Deep learning
Pulmonary artery catheter
cardiac output
deep learning
Health Informatics
Retrospective cohort study
Stroke volume
Convolutional neural network
Blood pressure
Health Information Management
medicine
Artificial intelligence
arterial pressure
business
Prospective cohort study
Algorithm
Subjects
Details
- Language :
- English
- ISSN :
- 22919694
- Volume :
- 9
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- JMIR Medical Informatics
- Accession number :
- edsair.doi.dedup.....a30aaf895dfd0f1ddd57013dbaa702c1