Alabed Samer, Uthoff Johanna, Zhou Shuo, Garg Pankaj, Dwivedi Krit, Alandejani Faisal, Gosling Rebecca, Schobs Lawrence, Brook Martin, Shahin Yousef, Capener Dave, Johns Christopher S, Wild Jim M, Rothman Alexander M K, van der Geest Rob J, Condliffe Robin, Kiely David G, Lu Haiping, Swift Andrew J
Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK.
Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK.
Eur Heart J Digit Health. 2022 May 2;3(2):265-275. doi: 10.1093/ehjdh/ztac022. eCollection 2022 Jun.
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, -index = 0.70, = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from -index = 0.71 to 0.76 ( ≤ 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.
肺动脉高压（PAH）是一种罕见但严重的疾病，若不治疗，死亡率很高。本研究旨在使用机器学习评估PAH患者的心脏磁共振成像（CMR）预后特征。
从ASPIRE注册研究中连续纳入723例未经治疗的PAH患者；516例纳入训练队列，207例纳入验证队列。采用基于多线性主成分分析（MPCA）的机器学习方法，提取整个心动周期的死亡和生存特征。通过对死亡预测值的高、低风险进行阈值化和聚类，将这些特征叠加在原始图像上。验证队列中的1年死亡率为10%。基于短轴和四腔心MPCA联合预测的单变量Cox回归分析具有统计学意义（风险比：2.1，95%CI：1.3，3.4，-指数=0.70，P=0.002）。MPCA特征将REVEAL模型对1年死亡率的预测能力从-指数=0.71提高到0.76（P≤0.001）。收缩末期室间隔和舒张末期左心室异常提示死亡风险最高。
基于MPCA的机器学习是一种可解释的时间分辨方法，能够在群体水平上可视化整个心动周期的心脏预后特征，使该方法具有透明度和临床可解释性。此外，相对于REVEAL风险评分和CMR容积测量，其增加的预后价值能够更准确地预测PAH患者1年的死亡风险。
