Evidence map›Paper›PMID 37125017›Full record

ArticleJournal of orthopaedics2023

PROMIS cluster analysis predicts two-year outcomes in knee surgery patients.

Justin E Kung, Samir Kaveeshwar, Michael Rocca, Daniel Rivkin, Jacob Hartline, Jonathan D Packer, Sean J Meredith, Natalie L Leong, R Frank Henn

Open access · greenAbstract read
In one paragraph

Article in Journal of orthopaedics, 2023. 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
1.6field-weighted citation impact, top 18% 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, 5 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

9 authors at 1 institution in 1 country.

Justin E KungDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
Samir KaveeshwarDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
Michael RoccaDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
Daniel RivkinDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
Jacob HartlineDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
Jonathan D PackerDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
Sean J MeredithDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
Natalie L LeongDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
R Frank HennDepartment of Orthopaedics, University of Maryland School of Medicine, Baltimore, MD, USA.
University of Maryland, Baltimore · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims & objectives: Meaningful clinical interpretation of orthopaedic patient-reported outcome scores remains challenging. Grouping scores may be more meaningful than individual score analysis. The purpose of this study was to determine if grouping knee surgery patients into four preoperative Patient-Reported Outcomes Measurement Information System (PROMIS) clusters would have prognostic value for two-year postoperative outcomes. Materials & methods: 488 of 697 (70%) patients undergoing elective knee surgery at a single urban institution were enrolled in an orthopaedic registry and completed two-year follow up. Patients were administered questionnaires for PROMIS, International Knee Documentation Committee Score (IKDC), Marx Activity Rating Scale (MARS), and Surgical Satisfaction (SSQ-8). A k-means cluster analysis was performed to identify preoperative PROMIS clusters. Chi-square or Kruskal-Wallis tests were conducted for bivariate analyses. Least-squares multiple linear regression models were performed to identify if cluster group was an independent predictor. Results: Cluster analysis revealed four clusters of patients. Psychological distress was most significant in determining classification. More impaired clusters were associated with higher rates of arthroplasty, African American race, preoperative opioid use, lower income, higher comorbidity index, and other sociodemographic and operative factors. Worse preoperative cluster status was associated with higher chance of achieving minimally clinically important change (MCID) on all metrics except PROMIS Pain Interference (PI), IKDC, and MARS. Multivariable analysis confirmed better preoperative cluster as predictive of better PROMIS Physical Function (PF), PI, IKDC scores, and satisfaction. Worse preoperative cluster was predictive of greater improvement on PF and PI but not IKDC. Conclusion: Preoperative PROMIS clusters have prognostic value in predicting outcomes for knee surgery patients. Better preoperative cluster function predicts superior outcomes. While worse preoperative cluster predicts worse outcome, all clusters still significantly improve, so worse preoperative cluster is not a contraindication to surgery.

Indexed as

ClusterKneeMCIDPatient-reported outcomesPROMISValue-based healthcare

Identifiers

PMID37125017
PMCPMC10139851
OpenAlexW4364355864

What OpenQuestion holds

Textmetadata
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.