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Machine learning of threedimensional right ventricular motion enables outcome prediction in pulmonary hypertension: A cardiac MR imaging study

Dawes TJW, De Marvao A, Shi W, Fletcher T, Watson GMJ, Wharton J, Rhodes CJ, Howard LSGE, Gibbs JSR, Rueckert D, Cook SA, Wilkins MR, O'Regan DP (2017)

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Publication Type: Journal article

Publication year: 2017

Journal

Book Volume: 283

Pages Range: 381-390

Journal Issue: 2

DOI: 10.1148/radiol.2016161315

Abstract

Purpose: To determine if patient survival and mechanisms of right ventricular failure in pulmonary hypertension could be predicted by using supervised machine learning of threedimensional patterns of systolic cardiac motion. Materials and Methods: The study was approved by a research ethics committee, and participants gave written informed consent. Two hundred fifty-six patients (143 women; mean age 6 standard deviation, 63 years 6 17) with newly diagnosed pulmonary hypertension underwent cardiac magnetic resonance (MR) imaging, right-sided heart catheterization, and 6-minute walk testing with a median follow-up of 4.0 years. Semiautomated segmentation of short-axis cine images was used to create a three-dimensional model of right ventricular motion. Supervised principal components analysis was used to identify patterns of systolic motion that were most strongly predictive of survival. Survival prediction was assessed by using difference in median survival time and area under the curve with time-dependent receiver operating characteristic analysis for 1-year survival. Results: At the end of follow-up, 36% of patients (93 of 256) died, and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and free wall, coupled with reduced basal longitudinal motion. When added to conventional imaging and hemodynamic, functional, and clinical markers, three-dimensional cardiac motion improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, respectively; P<.001) and provided greater differentiation according to difference in median survival time between high- and low-risk groups (13.8 vs 10.7 years, respectively; P<.001). Conclusion: A machine-learning survival model that uses three-dimensional cardiac motion predicts outcome independent of conventional risk factors in patients with newly diagnosed pulmonary hypertension.

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How to cite

APA:

Dawes, T.J.W., De Marvao, A., Shi, W., Fletcher, T., Watson, G.M.J., Wharton, J.,... O'Regan, D.P. (2017). Machine learning of threedimensional right ventricular motion enables outcome prediction in pulmonary hypertension: A cardiac MR imaging study. Radiology, 283(2), 381-390. https://doi.org/10.1148/radiol.2016161315

MLA:

Dawes, Timothy J. W., et al. "Machine learning of threedimensional right ventricular motion enables outcome prediction in pulmonary hypertension: A cardiac MR imaging study." Radiology 283.2 (2017): 381-390.

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