A machine learning-derived global organ damage score (HyperScore) accurately identified individuals with severe end-organ disease (AUC 0.964; 95% CI 0.941-0.987).
Observational (n=32,606)
Yes
Does a machine learning-derived global organ damage score improve the identification of severe end-organ disease and prediction of survival compared to blood pressure stratification in hypertensive patients?
A machine learning-derived global organ damage score (HyperScore) accurately identifies severe end-organ disease and predicts survival better than traditional blood pressure stratification in hypertensive patients.
Effect estimate: AUC 0.964 (95% CI 0.941-0.987)
BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage increases risk of vascular events and death but can be difficult to identify in the clinic. We developed a novel machine learning approach that quantifies current hypertension-associated multiorgan damage, mapping progression from health to advanced disease, in a pseudotemporal manner and predicts organ-specific disease progression trajectories. METHODS: We analyzed 566 multimodal imaging and nonimaging variables from 27 099 participants in the UK Biobank imaging substudy to develop a semisupervised contrastive trajectory inference (cTI) framework that models multiorgan alterations associated with hypertension exposure, including heart, brain, kidneys, vasculature, lungs, liver, and metabolic information. Model stability was validated through multiple internal validation steps, and external validity was tested on 5507 participants from the Atherosclerosis Risk in Communities study (ARIC). Clinical relevance was evaluated against existing risk scores and through ability to predict survival and incident multiorgan disease for up to 7 years, across both UK Biobank and ARIC. RESULTS: In the UK Biobank (mean age 63.27±7.48 years; 53.4% women) our global organ damage score (HyperScore) achieved an area under the curve of 0.964 (0.941–0.987) for identification of individuals with severe end-organ disease and robust stability in cross-validation with a mean root mean square error of 0.104±0.084. Survival odds differed significantly across HyperScore stages ( P 0.05) and consistent end-organ and outcome characteristics between ARIC and UK Biobank across HyperTrajectories. CONCLUSIONS: Machine learning–derived global organ damage scores are feasible in hypertension and enable identification of distinct hypertension-associated organ-disease phenotypes. New frameworks for hypertension assessment and monitoring using imaging to derive personalized risk assessment and phenotype-specific intervention may be achievable.
“High blood pressure affects people very differently. Some individuals develop significant damage to the heart, brain or kidneys even when blood pressure is only mildly elevated, while others appear relatively protected despite longstanding hypertension. Our findings suggest that AI methods may help us move beyond treating hypertension based purely on blood pressure numbers, towards a more personalised understanding of how the disease affects the body.”
Alkhodari et al. (Sun,) conducted a observational in Hypertension (n=32,606). Machine learning-derived global organ damage score (HyperScore) was evaluated on Identification of individuals with severe end-organ disease (AUC 0.964, 95% CI 0.941-0.987). A machine learning-derived global organ damage score (HyperScore) accurately identified individuals with severe end-organ disease (AUC 0.964; 95% CI 0.941-0.987).