Author + information
- Received April 22, 2019
- Revision received June 12, 2019
- Accepted June 18, 2019
- Published online October 21, 2019.
- James P. Howard, MA, MB BChira,∗,
- Christopher M. Cook, MBBS, BSca,∗,
- Tim P. van de Hoef, MD, PhDb,
- Martijn Meuwissen, MD, PhDc,
- Guus A. de Waard, MDd,
- Martijn A. van Lavieren, MScb,
- Mauro Echavarria-Pinto, MDe,
- Ibrahim Danad, MD, PhDd,
- Jan J. Piek, MD, PhDb,
- Matthias Götberg, MD, PhDf,
- Rasha K. Al-Lamee, MBBS, PhDa,
- Sayan Sen, MBBS, PhDa,
- Sukhjinder S. Nijjer, MBChB, PhDa,
- Henry Seligman, MBBSa,
- Niels van Royen, MD, PhDd,
- Paul Knaapen, MD, PhDd,
- Javier Escaned, MD, PhDe,
- Darrel P. Francis, MA, MDa,
- Ricardo Petraco, MD, PhDa and
- Justin E. Davies, MD, PhDa,∗ (, )@jerd10
- aDepartment of Cardiology, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom
- bDepartment of Cardiology, Academic Medical Center, Amsterdam, the Netherlands
- cDepartment of Cardiology, Amphia Hospital, Breda, the Netherlands
- dDepartment of Cardiology, VU University Medical Center, Amsterdam, the Netherlands
- eDepartment of Cardiology, Hospital Clínico San Carlos, Madrid, Spain
- fDepartment of Cardiology, Skane University Hospital, Lund, Sweden
- ↵∗Address for correspondence:
Dr. Justin E. Davies, Hammersmith Hospital, Du Cane Road, London W12 0NN, United Kingdom.
Objectives This study developed a neural network to perform automated pressure waveform analysis and allow real-time accurate identification of damping.
Background Damping of aortic pressure during coronary angiography must be identified to avoid serious complications and make accurate coronary physiology measurements. There are currently no automated methods to do this, and so identification of damping requires constant monitoring, which is prone to human error.
Methods The neural network was trained and tested versus core laboratory expert opinions derived from 2 separate datasets. A total of 5,709 aortic pressure waveforms of individual heart beats were extracted and classified. The study developed a recurrent convolutional neural network to classify beats as either normal, showing damping, or artifactual. Accuracies were reported using the opinions of 2 independent core laboratories.
Results The neural network was 99.4% accurate (95% confidence interval: 98.8% to 99.6%) at classifying beats from the testing dataset when judged against the opinions of the internal core laboratory. It was 98.7% accurate (95% confidence interval: 98.0% to 99.2%) when judged against the opinions of an external core laboratory not involved in neural network training. The neural network was 100% sensitive, with no beats classified as damped misclassified, with a specificity of 99.8%. The positive predictive and negative predictive values were 98.1% and 99.5%. The 2 core laboratories agreed more closely with the neural network than with each other.
Conclusions Arterial waveform analysis using neural networks allows rapid and accurate identification of damping. This demonstrates how machine learning can assist with patient safety and the quality control of procedures.
↵∗ Drs. Howard and Cook contributed equally to this work.
This work was supported by the Imperial College Healthcare NHS Trust Biomedical Research Centre. Drs. Cook, Al-Lamee, Sen, Nijjer, and van de Hoef have received speaker honoraria from Philips Volcano. Dr. Piek has received consultant and speaker fees from Abbott Vascular and Philips Volcano. Dr. Seligman has received research grant support from Amgen. Dr. van Royen has received research grant support from Abbott, Philips, and Biotronik; and has received honoraria from Medtronic, Microport, and Amgen. Dr. Escaned has received consulting and speaker fees from Philips Volcano, Boston Scientific, and Abbott/St. Jude Medical. Dr. Petraco has received research grant support from Amgen and Miracor; and has received consulting and speaker honoraria from Philips Volcano. Dr. van Lavieren is an employee of Philips. Dr. Davies holds patents pertaining to the iFR technology; and has served as a consultant for and received research grants from Philips Volcano. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received April 22, 2019.
- Revision received June 12, 2019.
- Accepted June 18, 2019.
- 2019 American College of Cardiology Foundation
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