Abstract 158: Burden And Predictors Of Mortality And Major Adverse Cardiovascular Outcomes In Heart Failure Preserved Ejection Fraction Patients Admitted With Acute Respiratory Distress Syndrome (ARDS)
Bibliographic record
Abstract
Background: Relation between Acute Respiratory Distress Syndrome (ARDS) and heart failure with preserved ejection fraction (HFpEF) are understudied and the data on these two concomitantly is lacking in the literature. Therefore, we sought to assess the burden and predictors of major adverse cardiovascular and cerebrovascular events (MACCE) and all-cause mortality in ARDS patients with HFpEF. Methods: The National Inpatient Sample (NIS) database was used to identify patients with HFpEF (after excluding patients with heart failure reduced ejection fraction) who required inpatient hospitalization for ARDS. Administrative ICD10 codes were used to identify the population of interest. Multivariate regression analysis was performed to assess the predictors of all-cause mortality and major adverse cardiovascular outcomes in the selected cohort. Results: Of 28,731,562 hospital admissions, 3,010 (0.14%) patients were admitted with ARDS and had HFpEF. Of those patients, 1,095 (36.4%) had all-cause mortality, and 1,415 (47.0%) had MACCE. In multivariate regression analysis, older age (OR 3.60, CI 1.40-9.28), 26-50 th quartile income (OR 2.10, CI 1.13-3.91), urban hospital admissions (OR 2.19, CI 1.20-4.01) as well as comorbidities such as coagulopathy (OR 1.77, CI 1.09-2.88), fluid and electrolyte imbalance (OR 1.65, CI 1.05-2.60), prior CABG (OR 2.99, CI 1.19-7.47), need for mechanical ventilation (OR 2.18, CI 1.12-4.23) were significant predictors of all-cause mortality. In our analysis, chronic pulmonary disease, valvular heart disease, hypertension, smoking, obesity were not significant predictors. The result of our analysis is reported in Table 1. Conclusion: These results suggest HFpEF remains important comorbidity in ARDS patients. Here, we identified predictors of poor outcomes in this patient population which may help physicians to identify the high-risk patients and decrease mortality.
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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 | 0.000 |
| 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) | 0.003 | 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".