HypertensionDiagnostic or prognostic study
Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study
- Machine learning development and external validation study
- multinational
- multimodal
- semisupervised contrastive trajectory inference
- internal and external validation
Authors' conclusion
Machine learning-derived global organ damage scores are feasible in hypertension and enable identification of distinct hypertension-associated organ-disease phenotypes.
Study context
- Population
- UK Biobank imaging substudy participants; external validation in ARICARIC validation n = 5507
- Participants
- 27,099 participants
Performance
As reported in the abstract
HyperScore (global organ damage score)
AUC for identifying severe end-organ disease
0.964 (0.941-0.987)
Cross-validation mean root mean square error
0.104±0.084
Survival odds differed across HyperScore stages; stratification by BP nonsignificant
P<0.001
Further findings
External testing in ARIC
- Jensen-Shannon distances for HyperScore distributions as low as
- 0.10
- No significant deviation in organ damage progression patterns
- P > 0.05
Original publication
Contrastive Machine Learning to Quantify Hypertensive Multiorgan Damage and Identify New Disease Phenotypes: A Multinational Multimodal Study. Circulation. Published 21 June 2026. doi:10.1161/circulationaha.125.077394
Summarised from the published abstract; every number is checked against it