Mechanical Thrombectomy Versus Conventional Anticoagulants Alone in Treatment of Pulmonary Embolism: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Pulmonary embolism (PE) is a life‐threatening condition and a leading cause of sudden death. It occurs when a thrombus develops in the venous system and then dislodges to embolize into the pulmonary arteries, causing obstruction. The current mainstay of treatment is anticoagulation. Adjunct mechanical thrombectomy (MT) or pharmacological thrombolysis may be added, following a risk assessment, for intermediate to massive PE. However, physicians remain reluctant to adopt them as first‐line treatment when comparing the benefits and risks to anticoagulation alone. This study aims to compare the safety and efficacy of MT with anticoagulation therapy alone in the treatment of intermediate to large‐sized PE patients, focusing on hospital stay, ICU admission, morbidity, and mortality. Following PRISMA guidelines, a search was conducted on PubMed, Scopus, Web of Science, and Google Scholar for studies published from inception up to July 5, 2025. Studies were then screened based on predefined inclusion and exclusion criteria. The Newcastle−Ottawa Scale was used to assess the quality of evidence. Studies were required to report at least one of the following outcomes: mortality rate, hospital stay, overall complications, and ICU stay. Screening & data were extracted independently by eight co‐authors, and a senior co‐author resolved all conflicts. From a pool of 5661 studies, a total of six studies (1509 patients) were included. MT was associated with a trend toward lower all‐cause mortality (OR = 0.5, 95% CI, 0.27 to 1.01; p = 0.05; I 2 = 46%) and a significantly lower rate of ICU length of stay by 3.0 days than those in the anticoagulant therapy group (MD: − 3.01, 95% CI, −5.57 to −0.44, p = 0.02; I 2 = 83%). No significant difference was found in overall complications and length of hospital stay between the MT and anticoagulant groups. However, MT was associated with lower rates of overall complications by 49% (OR = 0.51, 95% CI, 0.10 to 2.67, p = 0.43; I 2 = 83%) and shorter hospital stays (MD: −0.76; 95% CI, −4.64 to 3.13, p = 0.70; I 2 = 93%). MT showed greater efficacy in reducing mortality rates and ICU stay compared to intermediate to high‐risk PE patients treated with conventional anticoagulant therapy.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".