Evidence map›Paper›PMID 36551757›Full record

ArticleBiomedicines2022

Is EDSS Enough to Predict Risk of Upper Urinary Tract Damage in Patients with Multiple Sclerosis?

Kevin Stritt, Ilaria Lucca, Beat Roth, Nuno Grilo

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 32% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Automated Point-of-Care Bladder Pressure and Volume Measurement Device for Urodynamic Monitoring.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    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

4 authors at 2 institutions in 1 country.

Kevin StrittDepartment of Urology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, 1011 Lausanne, Switzerland.
Ilaria LuccaDepartment of Urology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, 1011 Lausanne, Switzerland.
Beat RothDepartment of Urology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, 1011 Lausanne, Switzerland.
Nuno GriloDepartment of Urology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, 1011 Lausanne, Switzerland.
University Hospital of Lausanne · CHUniversity of Lausanne · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lower urinary tract dysfunction is often observed in patients with multiple sclerosis (MS) and may be responsible for an increased risk of upper urinary tract (UUT) damage. Although there are well-known urodynamic risk factors for UUT damage, no clinical prediction parameters are clearly identified. We aimed to confirm the accuracy of the Expanded Disability Status Scale (EDSS) in predicting urodynamic risk factors for UUT deterioration and to assess other clinical parameters potentially predicting urodynamic risk factors. We retrospectively reviewed 201 patients with MS referred for primary neuro-urological work-up, including a video-urodynamic study (VUDS) from August 2009 to February 2020. Multivariate modeling revealed EDSS, male gender, and a number of LUTS as clinical parameters significantly associated with urodynamic risk factors for UUT damage (

Indexed as

Expanded Disability Status Scale (EDSS)multiple sclerosis (MS)neurogenic lower urinary tract dysfunction (nLUTD)retrospective studyurodynamic study

Identifiers

PMID36551757
PMCPMC9775948
OpenAlexW4309680909

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.