A systematic review and meta-analysis of the relative safety and efficacy of treating lower extremity deep vein thrombosis via pharmacomechanical thrombectomy and catheter-directed thrombolysis
Bibliographic record
Abstract
Objective To evaluate the safety and efficacy of pharmacomechanical thrombectomy and catheter-directed thrombolysis (CDT) as approaches to treating deep venous thrombosis of lower extremities (LEDVT). Methods The PubMed, Web of Science, Wanfang, Embase, Chinese Science and Technology Journal, Cochrane, and China National Knowledge Infrastructure (CNKI) databases were systematically searched for relevant articles published through October 2023, after which appropriate inclusion and exclusion criteria were used to screen out relevant articles. Review Manager 5.4.1 was used to extract key data from these studies, and pooled analyses were conducted based on mead difference (MD) or odds ratio (OR) values and corresponding 95% confidence interval (CI). Study quality was assessed with the Newcastle–Ottawa scale. Trial registration This study has been registered at INPLASY.COM (No. INPLASY2023100075). Results In total, 31 relevant studies enrolling 2413 patients were included in this meta-analysis, with 1184 and 1229 patients in the AngioJet and CDT groups, respectively. These analyses revealed that the AngioJet group exhibited significantly higher rates of early postoperative deep vein patency (MD = 7.73, 95% CI (3.29, 12.17), p = .0006) and affected limb symptom improvement (MD = 6.31, 95% CI (1.82,10.80), p = .006) relative to the CDT group, whereas no differences in grade II or III thrombus clearance rates (OR = 1.30, 95% CI (0.95, 1.77), p = .10) or changes in thigh circumference before and after treatment (MD = 0.01, 95% CI (−0.80, 0.83), p = .97) were observed. The AngioJet group also exhibited lower urokinase doses (MD = −145.33, 95% CI (−164.28,126.38), p < .00001), shorter thrombolysis time (MD = −2.35, 95% CI(−2.80, −1.90), p < .00001), a less prolonged hospital stay (MD = −3.13, 95% CI(−3.81, −2.45), p < .00001), lower rates of PTS incidence (OR = 0.56, 95% CI(0.36, 0.88), p = .01), and reduced complication rates (OR = 0.51, 95% CI(0.31, 0.83), p = .0007). Conclusion Studies published to date suggest that relative to CDT treatment, pharmacomechanical thrombectomy is associated with improved thrombus clearance, fewer complications, and lower complication rates in LEDVT patients, underscoring the safety and efficacy of this therapeutic strategy.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".