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Record W4410038888 · doi:10.1177/15266028251333701

The Influence of Lesion Length on the Efficacy of Angioplasty with Atherectomy in Treating Infrapopliteal Artery Disease: Systematic Review and Meta-Analysis

2025· review· en· W4410038888 on OpenAlexaboutno aff
Jian-Feng Gao, Lianglin Wu, Wenxuan Xiang, Kun Li, Lei Zhou, Yuehong Zheng

Bibliographic record

VenueJournal of Endovascular Therapy · 2025
Typereview
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAngioplastyMeta-analysisAtherectomyPublication biasOdds ratioLesionBalloonTarget lesionConfidence intervalRadiologyRestenosisInternal medicineSubgroup analysisSurgeryStentPercutaneous coronary intervention

Abstract

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Purpose: For the treatment of infrapopliteal artery disease (IPAD), endovascular intervention is a main choice, including atherectomy (ATH) and balloon angioplasty. However, the comparative efficacy between both approaches was heterogeneous in prior studies. Lesion length is a critical factor in IPAD anatomy classification. There is no meta-analysis to study the influence of lesion length on the effects of atherectomy for IPAD treatment. This meta-analysis aimed to determine the efficacy of angioplasty with ATH in different ranges of lesion length compared to angioplasty without ATH. Methods: Studies comparing ATH and angioplasty without ATH, which published up to April 2024 were retrieved from Pubmed, Embase, and Web of Science. Endpoints included limb prognosis and vessel complications. Referring to Trans-Atlantic Inter-Society Consensus, 10 cm lesion length was defined as a cutoff value for subgroup analysis. Pooled data were calculated using the Revman 5.4 and Stata 15, presented as odds ratio (OR) with 95% confidence interval (CI). Heterogeneity was calculated using I 2 value. Methodological quality was assessed by Cochrane tool and Newcastle-Ottawa Scale. The robustness of findings would be evaluated by sensitivity analysis. Egger’s test was performed to detect publication bias. Results: Eleven studies (8412 patients) were included in this meta-analysis. Significant higher technical success was detected in ATH group (OR=1.99, 95% CI 1.44–2.76, I 2 =0%). In a subgroup of mean lesion length <10cm, ATH was associated with lower incidence of dissection (OR=0.19, 95% CI 0.05–0.67, I 2 =15%, p=0.01) and bailout stenting (OR=0.18, 95% CI 0.05–0.71, I 2 =16%, p=0.01). No significant differences were found between ATH and no ATH cohorts in primary patency (OR=1.65, 95% CI 0.85–3.19), all-cause death (OR=0.75, 95% CI 0.5–1.13), limb salvage (OR=1.03, 95% CI 0.75–1.41), target-lesion revascularization (OR=0.77, 95% CI 0.47–1.27), and embolization (OR=1.19, 95% CI 0.5–2.83). Conclusion: For IPAD, lesion length is a notable factor affecting the efficacy of ATH. In comparisons with angioplasty without ATH, the use of ATH is contributable in achieving higher technical success. The superiority of ATH may appear in reducing dissection and bailout stenting in treating IPAD with length <10cm. Nevertheless, regardless of lesion length, there seems that ATH cannot effectively improve limb salvage compared to angioplasty alone. Clinical Impact Prior results of several meta-analyses comparing angioplasty with atherectomy to angioplasty alone were inconsistent in prognosis of infrapopliteal artery disease (IPAD), due to high heterogeneity. Considering the lesion length plays a critical role in IPAD anatomy classification, we performed an updated meta-analysis and subgroup analysis based on average lesion length to furtherly evaluate the efficacy of angioplasty following atherectomy. Our study revealed atherectomy showed advantages in obtaining technical success for IPAD ≥ 10cm and reducing the risks of bailout stenting and dissection for IPAD < 10cm. Atherectomy may prevent target-vessel complications rather than improving late patency and limb salvage effectively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.449
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.326
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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