Health Care Cost and Resource Utilization After Aortic Valve Replacement According to the Extent of Cardiac Damage
Bibliographic record
Abstract
BACKGROUND: The extent of cardiac damage has been shown to be associated with increased mortality, repeat hospitalization, and decreased quality of life after aortic valve replacement (AVR). However, the association between the extent of cardiac damage at the time of AVR and health care costs and resource utilization has never been described. METHODS: The Optum de-identified Market Clarity database was used to identify patients with aortic stenosis treated with AVR between 2016 and 2022. Patients were categorized into 5 groups (stages 0–4) based on their stage of cardiac damage in the year before AVR. Health care costs and resource utilization (including all-cause hospitalizations, heart failure hospitalizations, and total inpatient days) were assessed for the AVR hospitalization and the following year. Cost and utilization outcomes by stage of cardiac damage were estimated using covariate-adjusted generalized linear models. RESULTS: A total of 24 644 patients with AVR were included in our analysis. Patients were distributed across the 5 stages of cardiac damage as follows: 8.1% in stage 0, 17.1% in stage 1, 37.3% in stage 2, 36.2% in stage 3, and 1.4% in stage 4. Total costs increased with the extent of cardiac damage (increased by $2746 in stage 1, $19 511 in stage 2, $19 198 in stage 3, and $35 663 in stage 4, compared with stage 0; P <0.01). Similarly, length of stay, number of all-cause and heart failure hospitalizations, and all-cause and heart failure days in-hospital significantly increased with the extent of cardiac damage. Risk-adjusted models demonstrated that advanced stages of cardiac damage were associated with both higher cost and resource utilization when compared with patients with stage 0 damage. CONCLUSIONS: Among patients undergoing AVR for aortic stenosis, the extent of cardiac damage before AVR was independently associated with increased costs and health care resource utilization during the index AVR admission and through 1 year post-AVR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".