Phenotypic clustering analysis of patients rejected for mitral valve interventions: implications for future transcatheter technologies
Bibliographic record
Abstract
AIMS: Although several treatment options are available for patients with severe mitral regurgitation (MR), a significant proportion of patients remain ineligible for any mitral valve (MV) intervention. We aimed to analyse the phenotypic characteristics of surgical high-risk patients ineligible for MV interventions using an unsupervised phenotypic clustering approach. METHODS AND RESULTS: Between 2014 and 2022, the CHOICE-MI registry included 984 patients with MR undergoing screening for transcatheter MV replacement at 33 international sites. For this study, only patients with screening failure receiving medical therapy alone were included. Patients receiving transcatheter or surgical treatment were excluded. A cluster analysis using K-means was performed on baseline clinical, demographic, and imaging variables to identify different patient phenotypes. Among 284 patients with MR (77.4 ± 8.82 years, 56.0% female, EuroSCORE II: 6.6 ± 5.8%) considered ineligible for any MV intervention, two clinically distinct phenogroups (PGs) were identified using unsupervised hierarchical clustering of principal components: PG1, elderly women with primary MR, preserved left ventricular function, and annular calcification; and PG2, patients with secondary MR, advanced heart failure, and high prevalence of comorbidities. One-year all-cause mortality did not differ between the PGs (PG1: 21.4%, PG2: 23.4%, P = 0.89). Predictors of mortality were albumin, renal function, and extracardiac arteriopathy for PG1 and albumin, coronary artery disease, and prior myocardial infarction for PG2. CONCLUSION: This study identified two major subgroups among patients ineligible for mitral interventions showing profound differences in clinical and anatomical profiles. Identifying these factors may drive technological evolution to address the unmet clinical need for therapeutic options in MR patients. CLINICALTRIALS.GOV IDENTIFIER: NCT04688190 (CHOICE-MI Registry).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".