Pulmonary Embolism Response Teams—Evidence of Benefits? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Venous thromboembolisms constitute a major cause of morbidity and mortality with 60,000 to 100,000 deaths attributed to pulmonary embolism in the US annually. Both clinical presentations and treatment strategies can vary greatly, and the selection of an appropriate therapeutic strategy is often provider specific. A pulmonary embolism response team (PERT) offers a multidisciplinary approach to clinical decision making and the management of high-risk pulmonary emboli. There is insufficient data on the effect of PERT programs on clinical outcomes. Methods: We searched PubMed, Scopus, Web of Science, and Cochrane to identify PERT studies through March 2024. The primary outcome was all-cause mortality, and the secondary outcomes included the rates of surgical thrombectomy, catheter directed thrombolysis, hospital length of stay (HLOS), and ICU length of stay (ICULOS). We used the Newcastle−Ottawa Scale tool to assess studies’ quality. We used random-effects models to compare outcomes between the pooled populations and moderator analysis to identify sources of heterogeneity and perform subgroup analysis. Results: We included 13 observational studies, which comprised a total of 12,586 patients, 7512 (60%) patients were from the pre-PERT period and 5065 (40%) patients were from the PERT period. Twelve studies reported the rate of all-cause mortality for their patient population. Patients in the PERT period were associated with similar odds of all-cause mortality as patients in the pre-PERT period (OR: 1.52; 95% CI: 0.80–2.89; p = 0.20). In the random-effects meta-analysis, there was no significant difference in ICULOS between PERT and pre-PERT patients (difference in means: 0.08; 95% CI: −0.32 to 0.49; p = 0.68). There was no statistically significant difference in HLOS between the two groups (difference in means: −0.82; 95% CI: −2.86 to 1.23; p = 0.43). Conclusions: This meta-analysis demonstrates no significant difference in all studied measures in the pre- and post-PERT time periods, which notably included patient mortality and length of stay. Further study into the details of the PERT system at institutions reporting mortality benefits may reveal practice differences that explain the outcome discrepancy and could help optimize PERT implementation at other institutions.
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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 | 0.025 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.043 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".