Evidence map›Paper›PMID 35975355›Full record

ArticleACR open rheumatology2022

Construction of an Administrative Osteoarthritis Severity Index.

Sarah C Gebauer, Timothy Chrusciel, Joanne Salas, Jamil Neme, Leigh F Callahan, Jeffrey Scherrer

Abstract read
In one paragraph

Article in ACR open rheumatology, 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
–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

1 citing paper in PubMed.

  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

6 authors.

Sarah C GebauerSaint Louis University School of Medicine St. Louis, Missouri.ORCID https://orcid.org/0000-0003-3790-6490
Timothy ChruscielSaint Louis University School of Medicine St. Louis, Missouri.
Joanne SalasSaint Louis University School of Medicine St. Louis, Missouri.
Jamil NemeSaint Louis University School of Medicine St. Louis, Missouri.
Leigh F CallahanThurston Arthritis Research Center, University of North Carolina at Chapel Hill.
Jeffrey ScherrerSaint Louis University School of Medicine St. Louis, Missouri.

Funding

Neighborhood Barriers and Facilitators to Walking, the Epidemiology of Knee Osteoarthritis and Developing Effective Exercise PrescriptionsK23AR079035 · NIAMS · SAINT LOUIS UNIVERSITY · PI Sarah Gebauer · 2022 to 2026
$787k
NIAMS NIH HHS K23 AR079035
6 · The paper itself

Abstract

objectiveElectronic health record (EHR) databases are a powerful resource to investigate clinical trajectories of osteoarthritis (OA). There are no existing EHR tools to evaluate risk for knee arthroplasty (KA). We developed an OA severity index (OASI) using EHR data and demonstrate the index's association with time to KA.

methodsThis retrospective cohort study used 2010-2018 nationally distributed Optum EHR data. Eligible patients were 45 to 80 years old with a new diagnosis of knee OA in 2011-2012 and no prior KA. The OASI was a sum of first instance of x-ray imaging, advanced imaging, intra-articular injection, nonsteroidal anti-inflammatory drugs, and opioids. Principal components analysis index (PCI) score was also explored. Extended Cox proportional hazard models assessed time-dependent OASI and time to KA.

resultsAmong 16,675 eligible patients, 12.7% underwent KA. Median follow-up time was 72 months. Adjusted OASI models showed each additional event almost doubled the risk for KA (adjusted hazard ratio = 1.80, 95% confidence interval: 1.75-1.86). Similar results were observed for PCI.

conclusionThe sum OASI performs well identifying patients who would undergo KA and offers simplicity versus the PCI. Although replication in other cohorts is recommended, the OASI appears to be a novel and valid means to measure clinical OA severity in research studies using large EHR-based cohorts.

Identifiers

PMID35975355
PMCPMC9661818

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