Prevalence, diagnostic modalities, amputation rate, and overall mortality of arterial injuries associated with lower limb fractures/dislocations: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Data on the prevalence, diagnostic modalities, management sequence, and mortality associated with lower limb fractures/dislocations vary significantly between studies and often do not account for the impact of arterial injury associated with fractures/dislocations on the risks of ischemia, amputation, and severe disability-posing major therapeutic challenges.Considering these uncertain things, we aimed to determine the prevalence, diagnostic modalities, amputation rate, and overall mortality associated with these injuries. Methods:We conducted a systematic review and meta-analysis following the Meta-Analyses Of Observational Studies in Epidemiology (MOOSE) guidelines.A comprehensive literature was performed in Medline, Embase, and the World Health Organization Global Health Library, covering the period from January 1, 1960, to April 2024, with no language restrictions.We included observational studies (cohort studies, cross-sectional studies, case-control studies, and retrospective series) reporting on the prevalence, secondary amputation rate, diagnostic and therapeutic modality techniques, histopathological type of vascular injuries, management sequence, and time to hospital admission in cases of combined vascular and musculoskeletal injuries.Two reviewers independently selected studies and extracted data, and methodological quality was assessed using the Newcastle-Ottawa Scale.Pooled estimates for prevalence, amputation, and mortality rates were calculated using meta-analysis.Heterogeneity was assessed using Cochran's Q χ 2 test and quantified by the I 2 statistic.All analyses were performed using R software version 4.4.2 for Windows.Results: The global prevalence of vascular injuries associated with musculoskeletal trauma was 37% (95% confidence interval [CI], 25%-49%), with substantial heterogeneity across studies (I 2 = 100%; P < .00001).The most frequently reported vascular injury was arterial transection (complete or partial), found in 278 cases (46.3%).Regarding treatment, open surgery was predominant, with vascular bypass in 56.71% of the lower limb trauma population, far ahead of other techniques.Arteriography was historically the most frequently used diagnostic modality, whereas computed tomography angiography has become the most common since the 1990s.The overall amputation rate was 14% (95% CI, 11%-17%; I 2 = 97%; P < .00001),and the overall mortality rate was 9% (95% CI, 6%-12%; I 2 = 86%; P < .00001).Tibial fractures (proximal, midshaft, distal, and multifocal) were significantly associated with injuries to the anterior tibial artery (odds ratio [OR], 9.23; P < .001)and the posterior tibial artery (OR, 7.86; P = .005).Similarly, fibula fractures were associated with posterior tibial artery injury (OR, 7.80; P = .004).No significant association was found between pelvic fractures/hip dislocation and vascular injuries (P > .05).Finally, the amputation rate was significantly higher in patients who underwent initial vascular repair (19.5%) compared with those who first had fracture/dislocation reduction (6.5%; P < .001),whereas mortality rates did not significantly differ between the two treatment sequences (4% vs 3.85%; P > .05).Conclusions: Our analysis highlights a high prevalence and non-negligible rates of amputation and mortality in vascular injuries associated with musculoskeletal trauma, with a predominance of arterial transections.(JVS-Vascular Insights 2025;3:100283.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.050 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".