Abstract P579: Predictors of Cardiogenic Shock Among Patients With Alcohol Abuse Undergoing SAVR in the United States
Bibliographic record
Abstract
Introduction: While alcohol is a modifiable cause of cardiac complications, its use is on the rise. We sought to understand the predictors of cardiogenic shock (CS) among patients with a history of alcohol abuse undergoing surgical aortic valve replacement (SAVR). Hypothesis: We assessed the hypothesis that patients with alcohol abuse undergoing SAVR have various predictors of CS during hospitalization. Method: We retrospectively analyzed cases of SAVR among those with a diagnosis of alcohol abuse from the 2019 National Inpatient Sample. Logistic models helped find the adjusted odds ratio (aOR) of CS. Results: In total, 1980 patients with alcohol abuse underwent SAVR, and 10.9% (215 cases) reported an episode of CS. Blacks (aOR 2.553, CI 1.354-4.814, p=0.004) and races other than White, Black, or Hispanic (aOR 3.136, CI 1.29-7.62, p=0.012) showed higher odds of CS compared to Whites. While Medicare covered the highest proportion of CS patients (39.5%), Medicaid beneficiaries were least likely to develop CS (aOR 0.241, CI 0.132 -0.442, p<0.01) compared to those on Medicare. Our study also found that patients with anemia (aOR 10.317, CI 5.234-20.334, p<0.01), acute kidney injury (aOR 3.921, CI 2.54-6.052, p<0.01), and cirrhosis (aOR 4.587, CI 2.719-7.736, p<0.01), had higher odds of reporting CS. However, those with underlying obesity (aOR 0.237, CI 0.126-0.447, p<0.01), diabetes mellitus (aOR 0.344, CI 0.197-0.602, p<0.01), and hypertension (aOR 0.436, CI 0.264-0.721, p<0.01) were less likely to do so. Unfortunately, 25 patients with CS (11.6%) did not survive their hospitalization (aOR 13.771, 95% CI 5.526-34.317, p<0.01). Conclusion: In our analysis, we found that anemia, acute kidney injury, cirrhosis, Black race, and Races other than Black, White or Hispanic are possibly linked to a higher incidence of CS, while obesity, diabetes mellitus, hypertension, and being a Medicaid beneficiary are associated with a lower incidence of CS. Further studies and changes in protocols may help identify them early and improve 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".