Evidence map›Paper›PMID 28765738›Full record

ArticleKorean circulation journal2017

Baseline Characteristics of a Retrospective Patient Cohort in the Korean Vascular Intervention Society Endovascular Therapy in Lower Limb Artery Diseases (K-VIS ELLA) Registry.

Young-Guk Ko, Chul-Min Ahn, Pil-Ki Min, Jae-Hwan Lee, Chang-Hwan Yoon, Cheol Woong Yu, Seung Whan Lee, Sang-Rok Lee, Seung Hyuk Choi, Yoon Seok Koh and 3 more

Abstract read
In one paragraph

Article in Korean circulation journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Should We Leave Something There?Korean circulation journal · 2024
    Article
  8. Comparison between intermittent claudicationAnnals of surgical treatment and research · 2024
    Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
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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

13 authors.

Young-Guk KoDivision of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.
Chul-Min AhnDivision of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.
Pil-Ki MinDivision of Cardiology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Jae-Hwan LeeDivision of Cardiology, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, Korea.
Chang-Hwan YoonSeoul National University Bundang Hospital, Seongnam, Korea.
Cheol Woong YuDivision of Cardiology, Department of Internal Medicine, Korea University Anam Hospital, Seoul, Korea.
Seung Whan LeeDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Sang-Rok LeeDivision of Cardiology, Department of Internal Medicine, Chonbuk National University Hospital, Jeonju, Korea.
Seung Hyuk ChoiDivision of Cardiology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Yoon Seok KohDivision of Cardiology, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
In-Ho ChaeSeoul National University Bundang Hospital, Seongnam, Korea.
Donghoon ChoiDivision of Cardiology, Department of Internal Medicine, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Korea.
K-VIS investigators

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesThe Korean Vascular Intervention Society Endovascular Therapy in Lower Limb Artery Diseases (K-VIS ELLA) Registry is a multicenter observational study with retrospective and prospective cohorts of patients with lower extremity peripheral artery disease (PAD) undergoing endovascular treatment. In this study, we report the baseline characteristics of this retrospective cohort. SUBJECTS AND

methodsIn the present study we analyzed datasets of 3073 patients with 3972 target limbs from a retrospective cohort treated with endovascular therapy in 31 Korean hospitals. Data regarding patient baseline clinical and lesion characteristics and postintervention medications were collected from electronic medical records.

resultsThe mean patient age was 68.3±9.4 years. The majority were male (82.1%) with comorbidities such as diabetes mellitus (58.0%), hypertension (73.4%), and coronary artery disease (CAD; 55.3%). Patients more commonly presented with intermittent claudication (66.3%) than with critical limb ischemia (CLI; 33.7%). Femoropopliteal artery (41.2%) was the most common target vessel for endovascular treatment, followed by the aortoiliac (35.6%) and infrapopliteal arteries (23.2%). TransAtlantic Inter-Society Consensus for the Management of Peripheral Artery Disease (TASC II) type C/D aortoiliac (48.0%) or femoropopliteal lesions (60.2%) were frequent targets of endovascular treatment. At hospital discharge, only 73.1% of patients received dual antiplatelet therapy and 69.2% received a statin.

conclusionThe majority of Korean patients with PAD exhibited conventional risk factors, such as male sex, older age, diabetes, and hypertension with coexisting CAD. Complex lesions were frequently treated with endovascular therapy. However, the rate of adherence to guidelines regarding post-procedural medical treatment requires improvement.

Indexed as

Endovascular therapyLower extremityPeripheral artery diseaseRisk factors

Identifiers

PMID28765738
PMCPMC5537148

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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.