Evidence map›Paper›PMID 28092203›Full record

ArticleRadiology2017

Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study.

Timothy J W Dawes, Antonio de Marvao, Wenzhe Shi, Tristan Fletcher, Geoffrey M J Watson, John Wharton, Christopher J Rhodes, Luke S G E Howard, J Simon R Gibbs, Daniel Rueckert and 3 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
107citing papers in PubMed, 5 pooled it
17.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

107 citing papers in PubMed, 5 syntheses or guidelines pooled it, 223 citations in OpenAlex.

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  14. Advances in the imaging of pulmonary hypertension.International journal of cardiology. Congenital heart disease · 2025
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47 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 2 countries.

Timothy J W DawesFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Antonio de MarvaoFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Wenzhe ShiFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Tristan FletcherFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Geoffrey M J WatsonFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
John WhartonFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Christopher J RhodesFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Luke S G E HowardFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
J Simon R GibbsFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Daniel RueckertFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Stuart A CookFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Martin R WilkinsFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).
Declan P O'ReganFrom the MRC Clinical Sciences Centre, Du Cane Rd, London W12 0NN, England (T.J.W.D., A.d.M., W.S., T.F., S.A.C., D.P.O.); Division of Experimental Medicine, Department of Medicine (T.J.W.D, T.F., G.M.J.W., J.W., C.J.R., M.R.W.), Department of Computing (W.S., D.R.), and National Heart and Lung Institute (J.S.R.G.), Imperial College London, London, England; National Heart Centre Singapore, Singapore and Duke-NUS Graduate Medical School, Singapore (S.A.C.); and Department of Cardiology, National Pulmonary Hypertension Service, Imperial College Healthcare NHS Trust, London, England (L.S.G.E.H., J.S.R.G.).ORCID 0000-0002-0691-0270
J.P. Morgan · GBImperial College Healthcare NHS Trust · GB

Funding

British Heart Foundation FS/15/59/31839British Heart Foundation PG/12/27/29489British Heart Foundation SP/10/10/28431Medical Research Council MC_U120085815Medical Research Council MC_UP_1102/19Wellcome Trust 100211
6 · The paper itself

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 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.

Indexed as

Stroke VolumeAgedFemaleHeart VentriclesHumansHypertension, PulmonaryImage Interpretation, Computer-AssistedImaging, Three-DimensionalMachine LearningMagnetic Resonance Imaging, CineMaleMiddle AgedPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityVentricular Dysfunction, Right

Identifiers

PMID28092203
PMCPMC5398374
OpenAlexW2579231017

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.