Clinical predictors of hospital admission in low-risk pulmonary embolism: a retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Decisions about whether to admit patients with pulmonary embolism (PE) are often guided by risk stratification tools, such as the simplified Pulmonary Embolism Severity Index (sPESI). Patients deemed low-risk are typically treated as outpatients; however, some still experience complications. This study compares characteristics of low-risk PE patients managed as outpatients versus inpatients and evaluates patient-level factors associated with admission decisions. METHODS: We conducted a retrospective cohort study of adults (≥18 years) with objectively confirmed acute PE diagnosed between 1 June 2014, and 31 May 2019. Patients classified as low-risk (sPESI = 0) and without right ventricular dysfunction (RVD) were included. Clinical data were abstracted from records, and patients were categorized by initial sites of care (inpatient versus outpatient). Data analysis included descriptive statistics as well as multivariate logistic regression to identify factors associated with hospitalization. RESULTS: Of 229 eligible patients, 140 (61.1%) were admitted, and 89 (38.9%) managed as outpatients. Baseline characteristics were similar between groups; however, hospitalized patients often had heart rates (HR) ≥90 beats per minute (bpm), lower oxygen saturation, and more medical conditions requiring inpatient care. Among low-risk patients, HR 90-109 bpm (OR 1.78, 95% CI: 1.10-3.04), oxygen saturation between 90% and 94% (OR 1.10, 95% CI: 1.01-1.27), and medical indications for hospitalization >24 hours (OR 33.97, 95% CI: 8.47-236.09) were significantly associated with admission. CONCLUSIONS: Although classified as low-risk, over half of patients with acute PE were hospitalized. Elevated HR, reduced oxygen saturation, and comorbid conditions significantly influenced site-of-care decisions in this population. Outpatient management was associated with comparable 90-day safety outcomes, reinforcing its viability when patients are appropriately selected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".