Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension.
Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension.
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DOI：
10.1093/ehjdh/ztac022
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发表时间：
2022-06
期刊：
影响因子：
--
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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. 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. 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. Prognostic features on CMR can be extracted using multilinear principal component analysis (MPCA) machine learning to predict outcome in pulmonary arterial hypertension (PAH).
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影响因子：
14
作者：
Tello, Khodr;Dalmer, Antonia;Richter, Manuel J.
通讯作者：
Richter, Manuel J.
DOI：
10.1164/rccm.201909-1771oc
发表时间：
2020-02-15
影响因子：
24.7
作者：
Lewis, Robert A.;Johns, Christopher S.;Kiely, David G.
通讯作者：
Kiely, David G.
DOI：
10.1093/ehjci/jeaa001
发表时间：
2021-01-22
期刊：
European heart journal. Cardiovascular Imaging
影响因子：
--
作者：
Swift AJ;Lu H;Uthoff J;Garg P;Cogliano M;Taylor J;Metherall P;Zhou S;Johns CS;Alabed S;Condliffe RA;Lawrie A;Wild JM;Kiely DG
通讯作者：
Kiely DG
影响因子：
7.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
DOI：
10.1186/s12968-016-0258-x
发表时间：
2016-06-16
期刊：
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
影响因子：
--
作者：
de Siqueira ME;Pozo E;Fernandes VR;Sengupta PP;Modesto K;Gupta SS;Barbeito-Caamaño C;Narula J;Fuster V;Caixeta A;Sanz J
通讯作者：
Sanz J