Evidence map›Paper›PMID 31221138›Full record

ArticleBMC musculoskeletal disorders2019

Development of 3D method to assess intramuscular spatial distribution of fat infiltration in patients with rotator cuff tear: reliability and concurrent validity.

Rajan Khanna, Matthew D Saltzman, James M Elliott, Mark A Hoggarth, Guido M Marra, Imran Omar, Todd Parrish, Amee L Seitz

Abstract readValidation Study
In one paragraph

Article in BMC musculoskeletal disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. 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

8 authors.

Rajan KhannaStritch School of Medicine, Loyola University, Chicago, IL, USA.
Matthew D SaltzmanDepartment of Orthopedic Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
James M ElliottNorthern Sydney Local Health District, The Kolling Research Institute & Faculty of Health Sciences, University of Sydney, Sydney, Australia.
Mark A HoggarthDepartment of Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Guido M MarraDepartment of Orthopedic Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Imran OmarDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Todd ParrishDepartment of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Amee L SeitzDepartment of Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. amee.seitz@northwestern.edu.ORCID http://orcid.org/0000-0002-4272-1354

Funding

Northwestern University Clinical and Translational Science Institute (NUCATS)UL1TR001422 · NCATS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI D'AQUILA, RICHARD · 2015 to 2023
$56.8M
Foundation for Physical Therapy New Investigator Fellowship Training InitiativeNCATS NIH HHS UL1 TR001422
6 · The paper itself

Abstract

backgroundIntramuscular fat infiltration is a critical factor in surgical decision-making and is the most important factor used to prognosticate surgical repair outcomes in patients with rotator cuff tears. Quantitative 3D assessment of total rotator cuff fat infiltration in patients with rotator cuff tears has been realized. However, a reproducible method to evaluate 3D spatial distribution of rotator cuff intramuscular fat has not been established. The objective of this study was to establish the reproducibility, change detectable beyond error, and concurrent validity of a semi-automated method to evaluate the 3D spatial distribution of fat infiltration and muscle volume in patients with rotator cuff tears.

methodsThirteen consecutive patients diagnosed with symptomatic rotator cuff pathology and 3.0 T MRI confirmation at a single center were included. Fat-water imaging was used to quantify 3D intramuscular fat (%fat) in sagittal oblique sequences and intramuscular spatial distribution with the semi-automated technique. Each rotator cuff muscle was manually segmented yielding %fat in four axial intramuscular quartile-regions (superior-inferior; Q1-4) and three sagittal (medial/ intermediate/ lateral) regions. Reliability and concurrent validity of %fat and whole muscle volume were calculated with intraclass correlation coefficients (ICC).

resultsIntra-rater reliability for intramuscular sagittal divisions (ICC = 0.93-0.99) and axial divisions (ICC = 0.78-0.99) was good/excellent. Inter-rater reliability for %fat (ICC = 0.82-0.99) and volume (ICC = 0.92-0.99) was good/excellent. Concurrent validity with commercialized software showed good/excellent agreement (ICC = 0.66-0.99).

conclusionsA new semi-automated method to assess 3-dimensional intramuscular distribution of fat infiltration in patients with rotator cuff tears using advanced MR imaging demonstrates high intra and inter-rater reliability and good concurrent validity. Minimal detectable change thresholds established facilitate clinical interpretation for future clinical application of this technique to assess change and treatment efficacy in patients with rotator cuff tears.

Indexed as

Adipose TissueAdultAgedFemaleHumansImaging, Three-DimensionalMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsRetrospective StudiesRotator CuffRotator Cuff InjuriesSoftwareAtrophyDixon MRI, shoulder, muscle degenerationSupraspinatus

Identifiers

PMID31221138
PMCPMC6587235

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.