Abstract 4369781: Predictors of Mortality Among Elderly Patients Undergoing Redo Surgical Mitral Valve Replacement; a National Estimate in the United States
Bibliographic record
Abstract
Introduction: Although redo surgical mitral valve replacement(redo-SMVR) is associated with a higher mortality risk compared to valve-in-valve transcatheter mitral valve replacement(ViV-TMVR), it is still frequently chosen due to various factors such as anatomical limitations and limited availability of ViV-TMVR expertise. We conducted this study to support the development of improved risk stratification protocols. Methods: For this study, we considered patients aged 60 years or older who were hospitalized with a primary code for redo-SMVR through the National Inpatient Sample (NIS, 2016-2022). We adopted the methodologies set in prior studies and applied appropriate stratification and clustering of data. The all-cause mortality rates were estimated, and regression models were set up to estimate the adjusted odds ratio (aOR), 95% confidence intervals (95% CI), and p-values of clinically relevant variables. Results: Our study included a total of 6,825 cases of redo surgical mitral valve replacement (redo-SMVR). The overall mortality rate was 7.91% (n=540). Increased mortality was associated with older age, a history of percutaneous coronary intervention (PCI), and long-term anticoagulant use. However, no statistically significant difference in mortality was observed based on weekend (vs. weekday) admission, sex (female vs. male), elective admission status, insurance type (Medicaid/other vs. Medicare), race (Black/other vs. White), or the presence of comorbidities including dyslipidemia, chronic kidney disease (CKD), prior coronary artery bypass grafting (CABG), history of stroke, alcohol abuse, prior myocardial infarction (MI), obesity, chronic obstructive pulmonary disease (COPD), or hypertension. Patients who developed post-procedural complications such as cardiogenic shock, cardiac tamponade, sepsis, and acute kidney injury (AKI) had significantly higher mortality. However, patients with SIRS and pericardial effusion were not statistically associated with increased mortality(Table 1). Conclusions: Our findings confirm the crucial role that various patient demographics and in-hospital complications have in the all-cause mortality of elderly patients undergoing redo-SMVR. Modifications in treatment protocols and pre- and peri-surgical care should be considered.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".