Unsupervised machine learning analysis to enhance risk stratification in patients with asymptomatic aortic stenosis
Bibliographic record
Abstract
Abstract Aims There is a lack of studies investigating the pathophysiologic and phenotypic distinctiveness of aortic stenosis (AS). This heterogeneity has important implications for identifying optimal intervention timing and potential medical management. This study seeks to identify phenogroups of AS using unsupervised machine learning to improve risk stratification. Methods and results A total of 349 patients with asymptomatic AS from the PROGRESSA study were included in this analysis. Echocardiographic, clinical and blood sample data were used in the unsupervised clustering process. Longitudinal echocardiographic data were used to evaluate AS progression. Five clusters of patients were revealed using 18 variables selected by an unsupervised machine learning algorithm. Amongst them, aortic valvular phenotype, mean gradient, peak jet velocity (Vpeak), and left ventricle stroke volume were selected as discriminatory variables. Following the clustering process, characteristics differed between clusters, including age, body mass index, and sex ratio (all P < 0.001). Of note, cluster 1 showed higher AS severity at baseline with significantly higher initial Vpeak (344 [314; 376] cm/s) and calcium score (1257 [806; 1837] UA) (P < 0.001). Patients from cluster 1 had a faster AS progression (progression of Vpeak = 22 [9; 39] cm/s/year), and calcium score (213 [111; 307] UA/year) (P < 0.001). Cluster 1 was also associated with a higher composite risk of mortality and aortic valve replacement when adjusted for age, sex, and baseline AS severity (P < 0.001). Conclusion Artificial intelligence-guided phenotypic classification revealed 5 distinct groups and enhanced risk stratification of patients with AS. This approach may be useful to optimize and individualize medical and interventional management of AS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".