Evidence map›Paper›PMID 29928037›Full record

ArticlePloS one2018

A novel computer-aided diagnostic approach for detecting peripheral arterial disease in patients with diabetes.

Eva Elina Buschmann, Lulu Li, Michèle Brix, Andreas Zietzer, Philipp Hillmeister, Andreas Busjahn, Peter Bramlage, Ivo Buschmann

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Observational
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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.

Eva Elina BuschmannDept. for Cardiology, Center of Internal Medicine, Medical University Graz, Graz, Austria.
Lulu LiDept. for Angiology, Center for Internal Medicine 1, Medical University Brandenburg (MHB), Brandenburg/ Havel, Germany.
Michèle BrixDepartment of Physiology Charité Benjamin Franklin, Berlin, Germany.
Andreas ZietzerRichard-Thoma Laboratories for Arteriogenesis, Center for Cardiovascular Research (CCR), Charité Universitaetsmedizin Berlin, Berlin, Germany.
Philipp HillmeisterDept. for Angiology, Center for Internal Medicine 1, Medical University Brandenburg (MHB), Brandenburg/ Havel, Germany.
Andreas BusjahnHealthTwiSt GmbH, Berlin, Germany.
Peter BramlageDept. for Angiology, Center for Internal Medicine 1, Medical University Brandenburg (MHB), Brandenburg/ Havel, Germany.ORCID 0000-0003-4970-2110
Ivo BuschmannDept. for Angiology, Center for Internal Medicine 1, Medical University Brandenburg (MHB), Brandenburg/ Havel, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Peripheral arterial disease (PAD) is an important manifestation of systemic atherosclerosis, with diabetes being one of its most significant risk factors. Owing to medial arterial calcification (MAC), the ankle-brachial index (ABI) is not always a reliable tool for detecting PAD. Arterial Doppler flow parameters, such as systolic maximal acceleration (ACCmax) and relative pulse slope index (RPSI), may serve as effective surrogates to detect stenosis-induced flow alteration. In the present study, ACCmax and RPSI were prospectively evaluated in 166 patients (304 arteries) with clinical suspicion of PAD, including 76 patients with and 90 patients without diabetes. In the overall sample, the sensitivity of ACCmax (69%) was superior to that of ABI (58%) and RPSI (56%). In patients with diabetes, the sensitivity of ACCmax (57%), ABI (56%) and RPSI (57%) were similar, though a parallel test taking both ACCmax and RPSI into account further increased sensitivity to 68%. The specificity (98%) and accuracy (78%) of ACCmax were superior to those of ABI (83% and 70%, respectively), as were the specificity (95%) and accuracy (77%) of RPSI in patients with diabetes. The diagnostic properties of ACCmax and RPSI were superior to those of ABI for detecting PAD in patients with diabetes. Our acceleration algorithm (Gefäßtachometer®) provides a rapid, safe, noninvasive tool for identifying PAD in patients with diabetes.

Indexed as

Diagnosis, Computer-AssistedAgedAlgorithmsAngiographyAnkle Brachial IndexArea Under CurveDiabetic AngiopathiesFemaleHemodynamicsHumansMaleMiddle AgedPeripheral Arterial DiseasePulseROC CurveSensitivity and Specificity

Identifiers

PMID29928037
PMCPMC6013098

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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.