Abstract 13112: Benefit of Early Discharge Among Patients With Low-risk Pulmonary Embolism: Focus on Net Clinical Impact
Bibliographic record
Abstract
Introduction: Clinical guidelines recommend early discharge of patients with Low Risk Pulmonary Embolism (LRPE). With increasing awareness of the risks of hospital acquired complications (HAC), we sought to measure the overall impact of early discharge of LRPE patients on clinical outcomes and costs within the Veterans Health Administration. Methods: Adult patients with ≥1 inpatient diagnosis for PE (index date) between 10/2011- 6/2015 and continuous enrollment for ≥12 months pre- and 3 months post-index date were included. PE risk stratification was done using the simplified Pulmonary Embolism Stratification Index (sPESI). Propensity score matching (PSM) was used to compare 90-day PE-related outcomes, HAC, healthcare utilization and costs among short versus long length of stay (LOS) LRPE patients. We defined short LOS as ≤2 days, adverse PE events (APE) as recurrent deep vein thrombosis, major bleed, or death, and net clinical benefit as 1 minus the combined rate of APE and HAC. Results: Among 6,746 PE patients, 95.4% were male, 67.7% were white, 22.0% were African American, and 1,918 had a LRPE. Among LRPE patients, only 688 (35.9%) had short LOS. After PSM, 784 LRPE patients were evaluated. There were no differences in APE, but short LOS had fewer HAC (1.5% vs. 13.3%; p<0.0001) and bacterial pneumonias (5.9% vs. 11.7%, p<0.005). Net clinical benefit was higher for short LOS patients (86.9% vs. 78.3%, p=0.0013). Among long LOS, the number of HAC (52) exceeded APE (14 recurrent DVT, 5 bleeds). Short LOS had lower inpatient costs ($2,164 vs. $5,100; p<0.0001) and lower total costs ($9,056 vs. $12,544; p<0.0001) versus long LOS. Conclusions: LRPE patients with short LOS had better net clinical outcomes at lower costs than those with long LOS. Providers should deploy risk stratification strategies and consider early discharge for LRPE patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".