Geometrical predictors of small virtual neoLVOT size in functional mitral regurgitation
Bibliographic record
Abstract
Abstract Background LVOT obstruction is a potentially lethal complication of transcatheter mitral valve replacement (TMVR). An anticipated neoLVOT area of <2cm2 is presumed to imply prohibitive risk. Measurement of the anticipated neoLVOT can be time consuming and requires specialist software to facilitate virtual valve implantation. Purpose To determine simple geometrical predictors of prohibitive neoLVOT size. Methods 165 consecutive, non-calcific FMR patients referred to a transcatheter heart valve program were analysed. Segmentation of the mitral annulus and left heart geometry was performed using CT. Suitability for a default D-shaped TMVR was determined by proprietary annular inclusion criteria. Systolic neoLVOT area was determined via virtual valve implantation of the default TMVR. Results Sufficient image data for annular and neoLVOT suitability assessment was available in 152 patients. 105 patients (69%) were suitable for TMVR based on annular measurements. Of these, neoLVOT area was >2cm2 in 88 (84%). Overall, compared to patients not suitable for TMVR (n=64), those suitable had larger ventricles with lower LVEF, and larger annuli (table 1). Using binomial logistic regression involving the variables within table 1, LVESD was the sole statistically significant variable to predict neoLVOT area of <2cm2 (p=0.02). LVESD <48mm had 82% sensitivity and 94% specificity for the presence of prohibitive neoLVOT (figure 1). Conclusion Smaller LVESD is a strong predictor of small neoLVOT, and hence LVOT obstruction post default D-shaped TMVR implantation. This simple measure may therefore be used to streamline patient selection for advanced pre-procedural imaging analysis. Predicting NeoLVOT size <2 cm2 Funding Acknowledgement Type of funding source: None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".