Evidence map›Paper›PMID 41167383›Full record

Trial reportJournal of vascular surgery2026

Using precision medicine methods to identify disease stages for chronic limb-threatening ischemia in participants of the BEST-CLI trial.

Christina W Zhou, Margaret Hoch, Nikki L B Freeman, Matthew T Menard, Alik Farber, Jayer Chung, Michael R Kosorok, Katharine L McGinigle

Abstract readRandomized Controlled TrialMulticenter Study
In one paragraph

Trial report in Journal of vascular surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Christina W ZhouDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Margaret HochDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Nikki L B FreemanDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC.
Matthew T MenardDivision of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Alik FarberDivision of Vascular and Endovascular Surgery, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA.
Jayer ChungDivision of Vascular and Endovascular Therapy, Baylor College of Medicine, Houston, TX.
Michael R KosorokDepartment of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Katharine L McGinigleDivision of Vascular Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC. Electronic address: mcginik@med.unc.edu.

Funding

SToP Cancer SPORE: Developmental Research ProgramP50CA257911 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Alina Iuga · 2022 to 2026
$12.9M
Machine learning-based staging system to predict chronic limb-threatening ischemia disease progression and survival: A secondary analysis of the BEST-CLI trial dataR21HL172091 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI MCGINIGLE, KATHARINE L · 2024 to 2025
$233k
NCI NIH HHS P50 CA257911NHLBI NIH HHS R21 HL172091
6 · The paper itself

Abstract

backgroundChronic limb-threatening ischemia (CLTI), the most severe form of peripheral artery disease, is associated with a high risk of limb loss. CLTI clinical presentation is highly heterogeneous, ranging from neuropathic ulcers with only mild to moderate ischemia to gangrene resulting from severe ischemia. Understanding the etiology and limb- and systemic-based disease patterns, as well as differential procedural risks and outcomes, is pivotal for making treatment choices over the course of this chronic disease. In other words, accurate staging (and restaging over time) of CLTI that includes limb-based and systemic features is needed to improve the treatment decision-making process and clinical outcomes. Precision medicine analytics can integrate and synthesize multimodal data, in this case anatomy data alongside comorbidities and physical examination findings, offering a more complete staging system from which to make nuanced treatment decisions well-tailored to patient-specific risks.

methodsUsing data from the BEST-CLI (Best Endovascular vs Best Surgical Therapy in Patients with Critical Limb Ischemia) international randomized controlled trial, we used supervised latent topic modeling to identify clusters of patient features associated with amputation-free survival after stratifying for each assigned revascularization type. Patients were assigned to the cluster they belonged to with highest probability; clusters were characterized by analyzing the characteristics of patients within them. Although the clusters were not naturally ordinal, we subsequently organized them to mirror stages of disease progression for clearer clinical interpretation.

resultsBased on patient- and limb-focused characteristics, we identified three distinct clusters as disease stages. Across the three stages, rates of 2-year mortality were 11.59%, 20.91%, and 24.73% and rates of 2-year amputation-free survival were 83.26%, 70.03%, and 65.82%, respectively, for patients undergoing open bypass. Patients receiving endovascular therapy had 2-year mortality rates of 15.88%, 22.62%, and 20.32% and 2-year amputation-free survival rates of 77.98%, 66.06%, and 67.74%, respectively, for stages 1, 2, and 3. Stage 1 generally included patients who were less likely to have wounds, diabetes, and renal disease. Stage 2 was primarily driven by diabetes and some foot infection. Stage 3 is characterized by high rates of comorbidities, particularly end-stage renal disease and diabetes, as well as higher Wound, Ischemia, and foot Infection grades.

conclusionsWe identified three distinct stages of CLTI using precision medicine methods. The results from this analysis of the BEST-CLI randomized clinical trial dataset are consistent with previous findings in other cohorts. Future research focused on tailored treatment algorithms for each specific stage of CLTI is warranted.

Indexed as

Chronic Limb-Threatening IschemiaDecision Support TechniquesEndovascular ProceduresIschemiaPeripheral Arterial DiseasePrecision MedicineVascular Surgical ProceduresAgedAged, 80 and overAmputation, SurgicalChronic DiseaseClinical Decision-MakingFemaleHumansLimb SalvageMaleLimb amputationMachine learningPeripheral artery diseasePrecision medicineRisk stratification

Identifiers

PMID41167383
PMCPMC12927038

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.