Evidence map›Paper›PMID 26324831›Full record

ArticleClinical orthopaedics and related research2015

Risk calculators predict failures of knee and hip arthroplasties: findings from a large health maintenance organization.

Elizabeth W Paxton, Maria C S Inacio, Monti Khatod, Eric Yue, Tadashi Funahashi, Thomas Barber

Open access · bronzeAbstract read
In one paragraph

Article in Clinical orthopaedics and related research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
6.8field-weighted citation impact, top 3% 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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.

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  19. CORR InsightsClinical orthopaedics and related research · 2017
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Elizabeth W PaxtonSurgical Outcomes and Analysis, Kaiser Permanente, 8954 Rio San Diego Drive, Suite 406, San Diego, CA, 92108, USA. Liz.w.paxton@kp.org.
Maria C S InacioSurgical Outcomes and Analysis, Kaiser Permanente, 8954 Rio San Diego Drive, Suite 406, San Diego, CA, 92108, USA.
Monti KhatodDepartment of Orthopaedic Surgery, Southern California Permanente Medical Group, West Los Angeles, CA, USA.
Eric YueDepartment of Orthopaedic Surgery, The Permanente Medical Group, Oakland, CA, USA.
Tadashi FunahashiDepartment of Orthopaedic Surgery, Southern California Permanente Medical Group, West Los Angeles, CA, USA.
Thomas BarberDepartment of Orthopaedic Surgery, The Permanente Medical Group, Oakland, CA, USA.
Colorado Permanente Medical Group · USKaiser Permanente · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConsidering the cost and risk associated with revision Total knee arthroplasty (TKAs) and Total hip arthroplasty (THAs), steps to prevent these operations will help patients and reduce healthcare costs. Revision risk calculators for patients may reduce revision surgery by supporting clinical decision-making at the point of care. QUESTIONS/PURPOSES: We sought to develop a TKA and THA revision risk calculator using data from a large health-maintenance organization's arthroplasty registry and determine the best set of predictors for the revision risk calculator.

methodsRevision risk calculators for THAs and TKAs were developed using a patient cohort from a total joint replacement registry and data from a large US integrated healthcare system. The cohort included all patients who had primary procedures performed in our healthcare system between April 2001 and July 2008 and were followed until January 2014 (TKAs, n = 41,750; THAs, n = 22,721), During the study period, 9% of patients (TKA = 3066/34,686; THA=1898/20,285) were lost to followup and 7% died (TKA= 2350/41,750; THA=1419/20,285). The outcome of interest was revision surgery and was defined as replacement of any component for any reason within 5 years postoperatively. Candidate predictors for the revision risk calculator were limited to preoperative patient demographics, comorbidities, and procedure diagnoses. Logistic regression models were used to identify predictors and the Hosmer-Lemeshow goodness-of-fit test and c-statistic were used to choose final models for the revision risk calculator.

resultsThe best predictors for the TKA revision risk calculator were age (odds ratio [OR], 0.96; 95% CI, 0.95-0.97; p < 0.001), sex (OR, 0.84; 95% CI, 0.75-0.95; p = 0.004), square-root BMI (OR, 1.05; 95% CI, 0.99-1.11; p = 0.140), diabetes (OR, 1.32; 95% CI, 1.17-1.48; p < 0.001), osteoarthritis (OR, 1.16; 95% CI, 0.84-1.62; p = 0.368), posttraumatic arthritis (OR, 1.66; 95% CI, 1.07-2.56; p = 0.022), and osteonecrosis (OR, 2.54; 95% CI, 1.31-4.92; p = 0.006). The best predictors for the THA revision risk calculator were sex (OR, 1.24; 95% CI, 1.05-1.46; p = 0.010), age (OR, 0.98; 95% CI, 0.98-0.99; p < 0.001), square-root BMI (OR, 1.07; 95% CI, 1.00-1.15; p = 0.066), and osteoarthritis (OR, 0.85; 95% CI, 0.66-1.09; p = 0.190).

conclusionsStudy model parameters can be used to create web-based calculators. Surgeons can enter personalized patient data in the risk calculators for identification of risk of revision which can be used for clinical decision making at the point of care. Future prospective studies will be needed to validate these calculators and to refine them with time. LEVEL OF EVIDENCE: Level III, prognostic study.

Indexed as

Decision Support Systems, ClinicalDecision Support TechniquesHealth Maintenance OrganizationsArthroplasty, Replacement, HipArthroplasty, Replacement, KneeDevice RemovalFemaleHip ProsthesisHumansKnee ProsthesisLogistic ModelsMaleMarkov ChainsMonte Carlo MethodOdds RatioPatient Selection

Identifiers

PMID26324831
PMCPMC4626526
OpenAlexW1769511019

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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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.