Does the Type of Chronic Heart Failure Impact In-Hospital Outcomes for Aortic Valve Replacement Procedures?
Bibliographic record
Abstract
BACKGROUND: This study assessed in-hospital outcomes of patients with chronic systolic, diastolic, or mixed heart failure (HF) undergoing transcatheter aortic valve replacement (TAVR) or surgical aortic valve replacement (SAVR). METHODS: The Nationwide Inpatient Sample database was used to identify patients with aortic stenosis and chronic HF who underwent TAVR or SAVR between 2012 and 2015. Propensity score matching and multivariate logistic regression were used to determine outcome risk. RESULTS: A cohort of 9,879 patients with systolic (27.2%), diastolic (52.2%), and mixed (20.6%) chronic HF were included. No statistically significant differences in hospital mortality were noted. Overall, patients with diastolic HF had the shortest hospital stays and lowest costs. Compared with patients with diastolic HF, the risk of acute myocardial infarction (TAVR odds ratio [OR], 1.95; 95% CI, 1.20-3.19; P = .008; SAVR OR, 1.38; 95% CI, 0.98-1.95; P = .067) and cardiogenic shock (TAVR OR, 2.15; 95% CI, 1.43-3.23; P < .001; SAVR OR, 1.89; 95% CI, 1.42-2.53; P ≤ .001) was higher in patients with systolic HF, whereas the risk of permanent pacemaker implantation (TAVR OR, 0.58; 95% CI, 0.45-0.76; P < .001; SAVR OR, 0.58; 95% CI, 0.40-0.84; P = .004) was lower following aortic valve procedures. In TAVR, the risk of acute deep vein thrombosis and kidney injury was higher, although not statistically significant, in patients with systolic HF than in those with diastolic HF. CONCLUSION: These outcomes suggest that chronic HF types do not incur statistically significant hospital mortality risk in patients undergoing TAVR or SAVR.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".