Ejection Fraction Improvement Following Contemporary High-Risk Percutaneous Coronary Intervention: RESTORE EF Study Results
Bibliographic record
Abstract
Background Despite many reports of clinical outcomes in patients undergoing high-risk percutaneous coronary intervention (HRPCI) with hemodynamic support, little is known about whether this approach improves left ventricular ejection fraction (LVEF). The purpose of the present observational study was to examine, in an ideal patient population with Impella-supported HRPCI, whether there is an impact on left ventricular function at midterm follow-up. Methods RESTORE EF is a multicenter, retrospective analysis of a prospectively collected observational data set that aimed to assess 90-day LVEF in patients undergoing Impella-supported nonemergent HRPCI (NCT04648306), who survived with no intervening cardiac procedures prior to the primary endpoint follow-up window (90-day LVEF assessment). Secondary endpoints included change in New York Heart Association Functional Classification and Canadian Cardiovascular Society Angina Grade at the last follow-up. Results From August 2019 to May 2021, 406 patients were enrolled at 22 US sites. Age was 70.2 ± 11.4 years; 26% were female. In paired assessment at 90-day follow-up, baseline LVEF improved from 35 ± 15% to 45 ± 14% ( N = 251, P < .0001), with significantly greater improvement in patients with residual SYNTAX score I of 0. Percentage classified as New York Heart Association class III/IV decreased from 62% at baseline to 15% at last follow-up ( P < .001), and percentage with Canadian Cardiovascular Society grade III/IV symptoms decreased from 72% to 2% ( P < .0001). Conclusions In an ideal cohort of HRPCI patients, there is a signal that hemodynamically supported HRPCI affords significant improvement in 90-day LVEF, with complete revascularization associated with greater LVEF improvement. These hypothesis-generating findings merit further assessment in large, all-comer studies and randomized trials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".