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.

Hypertension is a serious medical condition that affects over a billion people worldwide. The proper management of disease progression requires an extended knowledge of the overall functional and structural changes in the whole body in response to the hypertension. Here, we propose HyperScore, an integrative and unified measure of hypertension progression relative to multi-organ and multi-modality clinical measurements and based on a semi-supervised machine learning (ML) approach. We developed the measure based on a large participating cohort from the UK Biobank database (n=27,099) with over 500 imaging and clinical variables from multiple modalities. The semi-supervised approach was developed based on the contrastive trajectory inference mechanism to provide a score that reflects the proximity of a participant to the disease state (range: 0–1). Modelling revealed that majority of hypertensive participants had scores above 0.25, whereas normotensives had scores below this threshold. The sensitivity and specificity were above 89%, with an area under the receiver operating characteristics of 96.4%. The modelling showed a stable performance when evaluating hidden testing sets on a 10-fold cross-validation scheme with nearly 0.1 error. There was a strong association (r2>0.6) between HyperScore and organs’ phenotypic patterns, especially for variables such as white matter hyperintensity and body mass index. This study is the first to potentiate ML-based modelling of hypertension progression from a multi-organ perspective, which could significantly aid in clinical decision making to save lives.