Evidence map›Paper›PMID 40545127›Full record

ArticleEuropean journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery2025

AMPREDICT Mobility-4: A Novel Four Category Mobility Outcome Prediction Model for Patients Undergoing Vascular Amputation.

Daniel C Norvell, Joseph M Czerniecki, Alison W Henderson, Aaron J Baraff, Amy Y Jeon, Aaron P Turner, Rhonda M Williams, Gale L Tang, Joseph B Webster

Abstract read
In one paragraph

Article in European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Daniel C NorvellVA Puget Sound Health Care System, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA; VA Centre for Limb Loss and Mobility (CLiMB), Seattle, WA, USA. Electronic address: daniel.norvell@va.gov.
Joseph M CzernieckiVA Puget Sound Health Care System, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA; VA Centre for Limb Loss and Mobility (CLiMB), Seattle, WA, USA.
Alison W HendersonVA Puget Sound Health Care System, Seattle, WA, USA; VA Centre for Limb Loss and Mobility (CLiMB), Seattle, WA, USA.
Aaron J BaraffVA Puget Sound Health Care System, Seattle, WA, USA; VA Seattle Epidemiologic Research and Information Centre (ERIC), Seattle, WA, USA.
Amy Y JeonVA Puget Sound Health Care System, Seattle, WA, USA; VA Seattle Epidemiologic Research and Information Centre (ERIC), Seattle, WA, USA.
Aaron P TurnerVA Puget Sound Health Care System, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA; VA Centre for Limb Loss and Mobility (CLiMB), Seattle, WA, USA.
Rhonda M WilliamsVA Puget Sound Health Care System, Seattle, WA, USA; Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA; VA Centre for Limb Loss and Mobility (CLiMB), Seattle, WA, USA.
Gale L TangVA Puget Sound Health Care System, Seattle, WA, USA; Department of Vasular Surgery, University of Washington, Seattle, WA, USA.
Joseph B WebsterDepartment of Physical Medicine and Rehabilitation, School of Medicine at Virginia Commonwealth University, Richmond, VA, USA; Department of Physcial Medicine and Rehabilitation, Fayetteville VA Medical Center, Fayetteville, NC, USA.

Funding

RRD VA I01 RX003690
6 · The paper itself

Abstract

objectiveThe aim of this study was to create a new mobility outcome prediction model (AMPREDICT Mobility-4) that can be applied at the time a decision is made that amputation is necessary due to an underlying vascular aetiology. It can be used to predict mobility outcome in patients undergoing transmetatarsal (TM), transtibial (TT), or transfemoral (TF) amputation.

methodsA cohort study retrospectively identified persons with lower limb amputation (LLA) through a large Veterans Affairs dataset, then prospectively enrolled participants to obtain their 12 month post-amputation self reported mobility. Seven hundred and four patients with first unilateral TM, TT, or TF amputation secondary to diabetes and or peripheral arterial disease were identified between February 2021 and September 2022. Potential predictors incorporated factors from the following domains: prior revascularisation; amputation level; demographics; comorbidities; mental health; and health behaviour factors. The predicted mobility outcome included four functional levels: wheelchair mobility; household ambulatory mobility; basic community ambulation; and advanced community ambulation. Multinomial logistic regression was used to fit 1 year post-incident amputation risk prediction models. Variable selection was performed using lasso, a machine learning methodology.

resultsVariable selection led to a final model of 15 predictors that successfully discriminated between all categories of mobility, except household vs. basic community mobility. The model discriminated best when comparing wheelchair mobility with at least some community ambulatory mobility (optimism adjusted c index = 0.71), a basic level of community mobility vs. less than a basic level of community mobility (optimism adjusted c index = 0.70), and advanced community mobility vs. less than advanced community mobility (optimism adjusted c index = 0.73).

conclusionThe AMPREDICT Mobility-4 model predicted four categories of functional mobility at each of three LLA levels using predictors available in the electronic health record. The model can augment clinical personalised mobility outcome prediction, enhancing both surgeon and patient awareness of the consequences of amputation level on mobility, to facilitate patient surgeon amputation level shared decision-making and assisting in setting appropriate patient outcome expectations.

Indexed as

Amputation, SurgicalDecision Support TechniquesLower ExtremityMobility LimitationPeripheral Arterial DiseaseAgedFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsTreatment OutcomeAmputationDiabetesMobilityPeripheral arterial diseasePrediction models

Identifiers

PMID40545127
PMCPMC12697929

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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.