Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension
European Heart Journal - Digital Health, vol. 3, pp. 265–275
Abstract
Abstract Aims Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning. Methods and results Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and four-chamber MPCA-based predictions was statistically significant (hazard ratios: 2.1, 95% CI: 1.3, 3.4, c-index = 0.70, P = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (P ≤ 0.001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality. Conclusion The MPCA-based machine learning is an explainable time-resolved approach that allows visualization of prognostic cardiac features throughout the cardiac cycle at the population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of 1-year mortality risk in PAH.
Authors 18
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Samer Alabed corresponding
Sheffield Teaching Hospitals NHS Foundation Trust · Insigneo · University of Sheffield
Affiliation as printed
Department of Clinical Radiology, Sheffield Teaching Hospitals , Sheffield , UK
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
INSIGNEO, Institute for in silico medicine, University of Sheffield , UK
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Affiliation as printed
Department of Computer Science, University of Sheffield , Sheffield , UK
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Affiliation as printed
Department of Computer Science, University of Sheffield , Sheffield , UK
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Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
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Sheffield Teaching Hospitals NHS Foundation Trust · University of Sheffield
Affiliation as printed
Department of Clinical Radiology, Sheffield Teaching Hospitals , Sheffield , UK
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
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Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
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Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
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Affiliation as printed
Department of Computer Science, University of Sheffield , Sheffield , UK
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Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
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Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
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Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
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Insigneo · University of Sheffield
Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
INSIGNEO, Institute for in silico medicine, University of Sheffield , UK
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Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
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Rob J. van der Geest Aachen
Leiden University Medical Center
Affiliation as printed
Leiden University Medical Center , Leiden , The Netherlands
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Royal Hallamshire Hospital · University of Sheffield
Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital , Sheffield , UK
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Royal Hallamshire Hospital · Insigneo · University of Sheffield
Affiliation as printed
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
INSIGNEO, Institute for in silico medicine, University of Sheffield , UK
Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital , Sheffield , UK
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Insigneo · University of Sheffield
Affiliation as printed
Department of Computer Science, University of Sheffield , Sheffield , UK
INSIGNEO, Institute for in silico medicine, University of Sheffield , UK
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Sheffield Teaching Hospitals NHS Foundation Trust · Insigneo · University of Sheffield
Affiliation as printed
Department of Clinical Radiology, Sheffield Teaching Hospitals , Sheffield , UK
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield , Sheffield , UK
INSIGNEO, Institute for in silico medicine, University of Sheffield , UK
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