Predictors of Hospital Length of Stay among Patients with Low-risk Pulmonary Embolism
Bibliographic record
Abstract
BACKGROUND: Increased hospital length of stay is an important cost driver in hospitalized low-risk pulmonary embolism (LRPE) patients, who benefit from abbreviated hospital stays. We sought to measure length-of-stay-associated predictors among Veterans Health Administration LRPE patients. METHODS: Adult patients (aged ≥18 years) with ≥1 inpatient pulmonary embolism (PE) diagnosis (index date = discharge date) between 10/2011-06/2015 and continuous enrollment for ≥12 months pre- and 3 months post-index were included. PE patients with simplified Pulmonary Embolism Stratification Index score 0 were considered low risk; all others were considered high risk. LRPE patients were further stratified into short (≤2 days) and long length of stay cohorts. Logistic regression was used to identify predictors of length of stay among low-risk patients. RESULTS: Among 6746 patients, 1918 were low-risk (28.4%), of which 688 (35.9%) had short and 1230 (64.1%) had long length of stay. LRPE patients with computed tomography angiography (Odds ratio [OR]: 4.8, 95% Confidence interval [CI]: 3.82-5.97), lung ventilation/perfusion scan (OR: 3.8, 95% CI: 1.86-7.76), or venous Doppler ultrasound (OR: 1.4, 95% CI: 1.08-1.86) at baseline had an increased probability of short length of stay. Those with troponin I (OR: 0.7, 95% CI: 0.54-0.86) or natriuretic peptide testing (OR: 0.7, 95% CI: 0.57-0.90), or more comorbidities at baseline, were less likely to have short length of stay. CONCLUSION: Understanding the predictors of length of stay can help providers deliver efficient treatment and improve patient outcomes which potentially reduces the length of stay, thereby reducing the overall burden in 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".