Evidence map›Paper›PMID 35333958›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2022

Using hierarchical unsupervised learning to integrate and reduce multi-level and multi-paraspinal muscle MRI data in relation to low back pain.

Abel Torres-Espin, Anastasia Keller, Gabriel T A Johnson, Aaron J Fields, Roland Krug, Adam R Ferguson, Alan R Hargens, Conor W O'Neill, Jeffrey C Lotz, Jeannie F Bailey

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2022. 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
0.3field-weighted citation impact, top 39% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  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

10 authors at 3 institutions in 1 country.

Abel Torres-Espin *Department of Neurological Surgery, Brain and Spinal Injury Center (BASIC), Weill Institute for Neuroscience, UC San Francisco, 1001 Potrero Ave, San Francisco, CA, 94110, USA. abel.torresespin@ucsf.edu.
Anastasia KellerDepartment of Neurological Surgery, Brain and Spinal Injury Center (BASIC), Weill Institute for Neuroscience, UC San Francisco, 1001 Potrero Ave, San Francisco, CA, 94110, USA.
Gabriel T A JohnsonDepartment of Orthopaedic Surgery, UC San Francisco, 95 Kirkham St, San Francisco, CA, 94122, USA.
Aaron J FieldsDepartment of Orthopaedic Surgery, UC San Francisco, 95 Kirkham St, San Francisco, CA, 94122, USA.
Roland KrugDepartment of Radiology, UC San Francisco, San Francisco, CA, USA.
Adam R FergusonDepartment of Neurological Surgery, Brain and Spinal Injury Center (BASIC), Weill Institute for Neuroscience, UC San Francisco, 1001 Potrero Ave, San Francisco, CA, 94110, USA.
Alan R HargensDepartment of Orthopaedic Surgery, UC San Diego, La Jolla, CA, USA.
Conor W O'NeillDepartment of Orthopaedic Surgery, UC San Francisco, 95 Kirkham St, San Francisco, CA, 94122, USA.
Jeffrey C LotzDepartment of Orthopaedic Surgery, UC San Francisco, 95 Kirkham St, San Francisco, CA, 94122, USA.
Jeannie F BaileyDepartment of Orthopaedic Surgery, UC San Francisco, 95 Kirkham St, San Francisco, CA, 94122, USA. jeannie.bailey@ucsf.edu.ORCID http://orcid.org/0000-0003-4618-7512
University of California, San Francisco · USSan Francisco VA Medical Center · USUniversity of California San Diego · US

Funding

UCSF Core Center for Patient-centric Mechanistic Phenotyping in Chronic Low Back PainU19AR076737 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BAILEY, JEANNIE FERN · 2019 to 2023
$34.1M
Phenotypes of pathologic vertebral endplate degenerationR01AR063705 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LOTZ, JEFFREY C. · 2013 to 2017
$3.0M
NIAMS NIH HHS R01 AR063705NIAMS NIH HHS U19 AR076737
6 · The paper itself

Abstract

purposeThe paraspinal muscles (PSM) are a key feature potentially related to low back pain (LBP), and their structure and composition can be quantified using MRI. Most commonly, quantifying PSM measures across individual muscles and individual spinal levels renders numerous separate metrics that are analyzed in isolation. However, comprehensive multivariate approaches would be more appropriate for analyzing the PSM within an individual. To establish and test these methods, we hypothesized that multivariate summaries of PSM MRI measures would associate with the presence of LBP symptoms (i.e., pain intensity).

methodsWe applied hierarchical multiple factor analysis (hMFA), an unsupervised integrative method, to clinical PSM MRI data from unique cohort datasets including a longitudinal cohort of astronauts with pre- and post-spaceflight data and a cohort of chronic LBP subjects and asymptomatic controls. Three specific use cases were investigated: (1) predicting longitudinal changes in pain using combinations of baseline PSM measures; (2) integrating baseline and post-spaceflight MRI to assess longitudinal change in PSM and how it relates to pain; and (3) integrating PSM quality and adjacent spinal pathology between LBP patients and controls.

resultsOverall, we found distinct complex relationships with pain intensity between particular muscles and spinal levels. Subjects with high asymmetry between left and right lean muscle composition and differences between spinal segments PSM quality and structure are more likely to increase in pain reported outcome after prolonged time in microgravity. Moreover, changes in PSM quality and structure between pre and post-spaceflight relate to increase in pain after prolonged microgravity. Finally, we show how unsupervised hMFA recapitulates previous research on the association of CEP damage and LBP diagnostic.

conclusionOur analysis considers the spine as a multi-segmental unit as opposed to a series of discrete and isolated spine segments. Integrative and multivariate approaches can be used to distill large and complex imaging datasets thereby improving the clinical utility of MRI-based biomarkers, and providing metrics for further analytical goals, including phenotyping.

Indexed as

Low Back PainWeightlessnessHumansMagnetic Resonance ImagingParaspinal MusclesUnsupervised Machine LearningHierarchical unsupervised learningIntegrative analysisLow back painLumbar spineMRIMultiple factor analysisParaspinal muscles

Identifiers

PMID35333958
PMCPMC9338899
OpenAlexW4220883735

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.