A machine-learning survival model using 3D cardiac motion improved survival prediction compared to conventional markers (AUC 0.73 vs 0.60; P<.001) in patients with pulmonary hypertension.
Observational (n=256)
Does supervised machine learning of three-dimensional right ventricular motion improve survival prediction compared to conventional markers in patients with newly diagnosed pulmonary hypertension?
A machine-learning survival model using 3D right ventricular motion from cardiac MRI significantly improves outcome prediction independent of conventional risk factors in patients with newly diagnosed pulmonary hypertension.
Absolute Event Rate: 0.73% vs 0.6%
p-value: p=< .001
Purpose To determine if patient survival and mechanisms of right ventricular failure in pulmonary hypertension could be predicted by using supervised machine learning of three-dimensional 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 ± standard deviation, 63 years ± 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. Online supplemental material is available for this article.
Dawes et al. (2017) conducted an observational in newly diagnosed pulmonary hypertension (n=256). Supervised machine learning of three-dimensional patterns of systolic cardiac motion vs. Conventional imaging and hemodynamic, functional, and clinical markers was evaluated on Survival prediction (area under the receiver operating characteristic curve for 1-year survival) (p=< .001). A machine-learning survival model using 3D cardiac motion improved survival prediction compared to conventional markers (AUC 0.73 vs 0.60; P<.001) in patients with pulmonary hypertension.