Deep Learning-based Modelling of Complex Hypertensive Multi-Organ Damage with Uncertainty Quantification from Simple Clinical Measures

Hypertension is a leading risk factor for a number of diseases and can cause severe damage to the vital organs such as the brain and heart. However, the level of hypertension itself does not necessarily reflect the full extent of underlying end-organ changes, which may hinder the development of effective treatment strategies. While recent research has demonstrated that these end-organ changes can be measured with deep phenotyping, its clinical translation may not be feasible. In this study, we propose a state-of-art deep learning approach that can quantify multi-organ (e.g., heart, brain, vasculature) phenotypical changes due to persistent hypertension from simple and popular clinical measures such as electrocardiogram (ECG), routinely acquired clinical data (age, BMI, diastolic and systolic blood pressures), and cardiac short axis (SAX) images from the UK Biobank, one of the largest open-access biomedical databases. Our proposed approach captures the intricate patterns of hypertensive disease state without resorting to the complex measures, which is hard to obtain in practical settings. It generates a numeric score between 0 and 1 of multi-organ damage, as well as provides an estimate of the overall uncertainty. The performance of our models is evaluated in different experimental settings and compared against the reference model. The results consistently demonstrate that the proposed approach can effectively model the multi-organ phenotypical changes from simple clinical measures with high performance (best-performing model MAE=0.108, MSE=0.019, variance=0.0005), and underscores its feasibility for potential clinical use.