Abstract 3550: Temporal Trends, Risk Factors, and Hospital Costs of the Adult Respiratory Distress Syndrome (ARDS) after Acute Ischemic Stroke in the United States
Bibliographic record
Abstract
Background. We sought to determine the prevalence and risk factors of ARDS, and its impact on hospital length of stay (LOS) and cost, after acute ischemic stroke (AIS) in the U.S. Methods. Data were derived from the National Inpatient Sample from 1998-2008. We searched for admissions of patients >18 years, with a diagnosis of AIS and ARDS. Definitions were based on ICD9-CM codes. Prevalence proportions, and hospital LOS and cost were calculated. Multivariate logistic regression models were then fitted to determine odds ratios (OR) and 95% confidence intervals (CI) for determinants of ARDS and to assess for its impact on hospital LOS and cost. Results. Over the 10-year period, we identified 4,066,043 admissions that corresponded to a primary diagnosis of AIS of which 157,464 had ARDS for a cumulative prevalence of 4%. Cases of ARDS after AIS increased from 13,395 (3.6%) in 1998 to 17,222 (4%) in 2008. ARDS was more common among old (OR 0.9; 95%CI 0.9-0.98), men (OR 1.2; 95%CI 1.2-1.21), blacks (OR 1.2; 95%CI 1.1-1.2), urban-academic centers (OR 1.4; 95%CI 1.3-1.5); and in sepsis (OR 8.0; 95%CI 7.6-8.4), cardiovascular dysfunction (OR 3.5; 95%CI 3.3-3.7), respiratory dysfunction(OR 2.3; 95%CI, 2.2-2.4), hepatic dysfunction (OR 2.9; 95%CI 2.4-3.4), hematological dysfunction (OR 1.9; 95%CI, 1.8-2.1), and after thrombolysis (OR 3.8; 95%CI, 3.6-4.0). The median hospital LOS for ARDS was 8 days Inter-Quartile range [IQR] 14-23 vs. 3 days IQR 5-7, p<0.001. Median hospital cost for ARDS was $56,990 IQR $29,360-$111,900 vs. $17,240 IQR $10,760-$28,620, p<0.001. ARDS independently predicted higher hospital LOS (OR 2.4, 95% CI, 2.4-2.5) and higher costs (OR 2.6, 95% CI 2.5-2.6). Conclusion. Our analysis demonstrates an increase in the prevalence of ARDS after AIS in U.S. ARDS is associated with significant increase in hospital LOS and overall costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".