The Prognostic Value of the Dandel's Index in Patients Undergoing Tricuspid Transcatheter Edge-to-Edge Repair
Bibliographic record
Abstract
AIMS: Conventional parameters of right ventricular (RV) function are load-dependent and therefore do not accurately reflect contractility in patients with relevant tricuspid regurgitation (TR). RV adaptability to load has been characterized using the Dandel's index in patients with heart failure, but its prognostic value in patients undergoing tricuspid transcatheter edge-to-edge repair (T-TEER) has not been investigated so far. METHODS AND RESULTS: From the EuroTR registry (2019 to 2022), patients with complete datasets and a minimum of 2-years of follow-up were included. RV functional parameters (i.e. tricuspid annular plane systolic excursion [TAPSE], fractional area change [FAC], TAPSE/systolic pulmonary arterial pressure [sPAP]), as well as a echocardiographic RV load adaptation index (Dandel's index) were assessed and their predictive value in terms of all-cause mortality evaluated using logistic multivariate logistic regression. The majority of the 364 patients had secondary TR (96%) and were severely symptomatic (New York Heart Association class ≥III; 92%). At 2-year follow-up, 36% of patients had died. Functional RV parameters (TAPSE: hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.62-0.84; FAC: HR 0.73, 95% CI 0.59-0.91), coupling index (TAPSE/sPAP: HR 0.8, 95% CI 0.65-0.99) and Dandel's index (HR 0.67, 95% CI 0.53-0.85) were all associated with mortality at 2 years in univariable analyses. In a multivariate logistic regression model, the Dandel's index maintained its predictive value (p < 0.001), along with TAPSE and absence of signs of right heart failure, with an optimal threshold of 20.5 determined by the receiver-operating characteristic analysis. This threshold also successfully predicted cardiac hospital readmission. A multivariate analysis was conducted to identify parameters linked to RV function and predicting clinical outcomes. CONCLUSION: Assessment of the RV capacity to adjust for changes in loading conditions predicted mortality in patients with severe symptomatic TR undergoing T-TEER. The use of a multiparametric approach including the Dandel's index to assess RV function had an incremental value for the stratification of patients into subgroups with different prognosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".