The Impact of Cardiac Damage on In‐Hospital Outcomes for Patients With Aortic Stenosis in the United States: An Analysis From The National Inpatient Sample
Bibliographic record
Abstract
INTRODUCTION: The objective of this study is to determine if cardiac damage based on hospital discharge codes is associated with in-hospital outcomes in patients with aortic stenosis (AS). METHODS: We conducted a retrospective cohort study of hospital admissions between 2016 and 2021 with a diagnosis of AS in the National Inpatient Sample (NIS). The cardiac damage stages 0-4 were determined based on hospital discharge codes. Logistic and linear regressions were used to determine the association between cardiac stage and in-hospital mortality, length of stay (LoS) and cost. RESULTS: A total of 2,980,150 hospital admissions were included in the analysis (82.5% conservative management, 11.2% transcatheter aortic valve replacement [TAVR], 6.3% surgical aortic valve replacement [SAVR]). The association between cardiac damage stage and in-hospital outcome was most significant for patients who had SAVR treatment (stage 4 vs. stage 0: mortality OR 27.70 95% CI 17.35-35.17, LoS 7.34 95% CI 6.34-8.35, cost 70,710 95% CI 65,110-76,310) compared to TAVR treatment (stage 4 vs. stage 0: mortality OR 9.15 95% CI 5.52-15.15, LoS 6.27 95% CI 5.63-6.90, cost 28,384 25,084 to 31,684) and conservative treatment (stage 4 vs. stage 0: mortality OR 3.55 95% CI 3.13-4.04, LoS 2.09 95% CI 1.87 to 2.31, cost 6362 95% CI 5642-7083). CONCLUSIONS: Cardiac damage can be evaluated using diagnostic codes in patients with AS and it is associated with in-hospital mortality, LoS and cost, and has more impact on these outcomes in patients treated with SAVR versus those treated with TAVR.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".