Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension

Background

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

723 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.

Results

The one-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and 4-chamber MPCA-based predictions was statistically significant (Hazard Ratios 2.1, 95% CI 1.3, 3.4, c-index = 0.70, p = .002). The MPCA features improved the one-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (p = < .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 visualisation of prognostic cardiac features throughout the cardiac cycle at 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 one-year mortality risk in PAH.