Acute Valve Syndrome Before Aortic Valve Replacement: Impact on Clinical Outcomes, Health Care Costs, and Resource Use
Bibliographic record
Abstract
Background A classification system describing clinical presentation before aortic valve replacement (AVR) has recently been proposed (stable valve syndrome [SVS]), mildly symptomatic (progressive valve syndrome), or with acute and severe signs and symptoms (acute valve syndrome [AVS]). We aimed to evaluate the clinical impact, health care costs, and resource use associated with the mode of clinical presentation at the time of AVR among patients presenting with aortic stenosis. Methods Using the Market Clarity database (2017–2023), patients >18 years old with aortic stenosis and undergoing AVR (transcatheter or surgical) were included. Patients were categorized as SVS, progressive valve syndrome, or AVS based on their clinical presentation during the 1 year before AVR. All‐cause death, heart failure hospitalization, total health care costs (index hospitalization plus 1‐year follow‐up), and resource use were compared across clinical presentation groups using adjusted regression models. Results Of the 24 075 patients undergoing AVR for aortic stenosis, 270 (1.1%) had SVS, 10 195 (42.3%) progressive valve syndrome, and 13 610 (56.5%) AVS. Compared with SVS, AVS presentation was associated with increased risk of 1‐year death (adjusted hazard ratio [aHR], 2.93 [95% CI, 1.1–7.8]; P =0.03) and heart failure hospitalization (aHR, 4.15 [95% CI, 1.6–11.1]; P <0.01) after AVR. The total costs of AVR and 1‐year follow‐up were significantly higher in both the progressive valve syndrome (difference $27 410 [$13 507–$41 314]) and AVS groups (difference $36 267 [$22 302–$50 232]) compared with patients with SVS. Conclusions AVS was frequent among patients undergoing AVR and was associated with increase in death, heart failure hospitalization, health care costs, and resource use 1 year after AVR, and SVS presentation was associated with the best clinical and economic outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".