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Abstract 4364722: Arterial Duplex Ultrasound Impacts Endovascular Outcomes: Unintended Consequences of Patient Harm From the 2023 and 2025 Amputation Reduction and Compassion Act

2025· article· en· W4415792086 on OpenAlexaff
Avrodet Mourkus, D. Adeyemo, Lindsey Ondieki, Daniel Novák, Stephen Stuk, Edwin L. Kendrick

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsCARE Canada
Fundersnot available
KeywordsCohortAmputationClaudicationRevascularizationRetrospective cohort studyAtherectomyCritical limb ischemiaRisk factorIschemia

Abstract

fetched live from OpenAlex

Background: The Amputation Reduction and Compassion (ARC) Act of 2023 and 2025 supports arterial duplex ultrasound (DUS) as an initial screening tool to detect and treat peripheral arterial disease (PAD) to reduce amputations. Our prior research found use of DUS in PAD management was associated with higher rates of atherectomy procedures, questionable interventions, and worse surgical outcomes when compared to the use of ankle-brachial index (ABI) with toe-brachial index (TBI). Given DUS’s higher false-positive rate, we hypothesize DUS may be associated with higher vascular complications of claudication, chronic limb threatening ischemia (CLTI), major amputations, and increased endovascular intervention. If confirmed, the ARC Act’s recommendation for DUS may have unintended consequences of patient harm. Methods: Using the TriNetx de-identified database from April 2010–2025, we performed a retrospective cohort study. Inclusion criteria were: a) PAD diagnosis; b) at least one PAD risk factor (diabetes, hypertension, hyperlipidemia, smoking); c) an endovascular procedure (atherectomy, stenting, or angioplasty) performed within two years of both PAD diagnosis and PAD risk factor. Cohorts were assessed for PAD diagnosis by ABI/TBI alone (Cohort A), DUS alone (Cohort B), or both (Cohort C). After propensity score matching for age, gender, and race, we compared Cohorts A-B, A-C, and B-C, to assess for vascular complications, including chronic kidney disease/renal failure and lower extremity CTA, within five years of intervention. Results: Cohort A-B (n=9,180) displayed claudication (RR=1.334, p<0.0001) and re-interventions (RR=1.407, p<0.0001) in Cohort B. Cohort A-C (n=27,833) displayed claudication (RR=2.152, p<0.0001), CLTI (RR=1.687, p<0.0001), amputation (RR=1.286, p<0.0001), lower extremity CTA (RR=1.977, p<0.0001), and re-interventions (RR=1.817, p<0.0001) in Cohort C. Cohort B-C (n=9,683) displayed claudication (RR=1.724, p<0.0001), CLTI (RR=1.487, p<0.0001), amputation (RR=1.385, p<0.0001), lower extremity CTA (RR=1.987, p<0.0001), and re-interventions (RR=1.286, p<0.0001) in Cohort C. After excluding PAD diagnosis as a confounder, major amputation increased (RR=1.156, p<0.0072) in Cohort A. Conclusion: This is the second research group study linking DUS with increased and questionable interventions. These results highlight the risk for greater public harm if DUS is used as a screening tool, as detailed in the 2023 and 2025 ARC ACT.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.282
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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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