Evidence map›Paper›PMID 38638114›Full record

ArticleJournal of orthopaedics2024

Excellent 10-Year survivorship of robotic-arm-assisted unicompartmental knee arthroplasty.

Martin Roche, Rushabh M Vakharia, Tsun Law, Hugo C Rodriguez, Kevin L Mekkawy, Nipun Sodhi, Jeremy A Dubin, Daniel Hameed, Michael A Mont

Open access · greenAbstract read
In one paragraph

Article in Journal of orthopaedics, 2024. 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
1.1field-weighted citation impact, top 24% 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. Review
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 6 institutions in 1 country.

Martin RocheDepartment of Orthopaedic Surgery, Hospital for Special Surgery Florida, West Palm Beach, FL, United States.
Rushabh M VakhariaDepartment of Orthopaedic Surgery, Maimonides Medical Center, Brooklyn, NY, United States.
Tsun LawHoly Cross Hospital, Orthopaedic Research Institute, Ft. Lauderdale, FL, 33308, United States.
Hugo C RodriguezLarkin Community Hospital, South Miami, FL, 33143, United States.
Kevin L MekkawyLarkin Community Hospital, South Miami, FL, 33143, United States.
Nipun SodhiDepartment of Orthopaedic Surgery, North Shore University Hospital, Northwell Health, New York, NY, United States.
Jeremy A DubinLifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopaedics, Baltimore, MD, United States.
Daniel HameedLifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopaedics, Baltimore, MD, United States.
Michael A MontLifeBridge Health, Sinai Hospital of Baltimore, Rubin Institute for Advanced Orthopaedics, Baltimore, MD, United States.
Sinai Hospital · USLarkin Community Hospital · USHoly Cross Hospital · USHospital for Special Surgery · USMaimonides Medical Center · USNorthwell Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Robotic-arm-assisted unicompartmental knee arthroplasty (UKA) is an excellent solution for patients suffering from single-compartment knee arthritis. While outcomes tend to be favorable for UKAs, revision operations, commonly due to component malpositioning and malalignment resulting in accelerated wear, are a major concern. Intraoperative technologies, such as robotic assistance, can help better ensure that implants are positioned based on a patient's specific anatomy and mechanical physiology. However, long-term survivorship and patient-reported satisfaction with robotic-assisted UKAs are limited. Therefore, the purpose of this study was to assess the 10-year outcomes of patients who underwent robotic-arm-assisted unicompartmental knee arthroplasty. Specifically, we evaluated: 1) 10-year survivorships; 2) patient satisfaction scores; and 3) re-operations. Methods: From a single surgeon and single institution, 185 patients who had a mean age of 65 years (range, 39 to 92) and a mean body mass index of 31.6 (range, 22.4 to 39) at a mean of 10 years follow-up were evaluated (range, 9 to 11). For all patients, the same robotic-assistive device was utilized intraoperatively, and all patients underwent standardized physical therapy and received standardized pain control management. Then 10-year survivorships with Kaplan-Meir curves, patient satisfaction evaluations with a 5-point Likert scale, and re-operations were assessed as primary outcomes. Results: Overall implant survivorship was 99%, with only two patients requiring revision surgery. There was one patient who was converted to a total knee arthroplasty, while the other patient underwent polyethylene exchange at 5 weeks for an acute infection with successful implant retention. Overall, 97% of the patients were satisfied with their postoperative outcomes, with 81% of patients reporting being very satisfied. There were two other patients who required arthroscopic intervention: one to remove a cement loose body, the other to remove adhered scar from the fat pad and the anterior cruciate ligament. Conclusion: This study is one of the first to provide longer-term (mean 10-year) survivorship and patient-reported satisfaction outcomes for robotic-assisted UKA patients. These data show strong support for utilizing this surgical technique, as nearly all patients maintained their original prostheses and reported being satisfied after a mean of 10 years. Therefore, based on these results, we recommend the use of robotic assistance when performing UKAs.

Indexed as

10-year outcomesImplant survivorshipPatient satisfactionRevision surgeryRobotic-assistedUnicompartmental knee arthroplasty

Identifiers

PMID38638114
PMCPMC11021903
OpenAlexW4392948062

What OpenQuestion holds

Textmetadata
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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.