Evidence map›Paper›PMID 41761380›Full record

ArticleArthroplasty (London, England)2026

Exploring the optimal age for total knee arthroplasty to minimize risk of adverse outcomes: machine learning analysis of a statewide cohort.

Chloe Heiting, Yiyuan Wu, Susan M Goodman, Peter Sculco, Fei Wang, Said Ibrahim, Peter Cram, Rich Caruana, Bella Mehta

Abstract read
In one paragraph

Article in Arthroplasty (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Chloe HeitingDonald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA.
Yiyuan WuWeill Cornell Medicine, New York, NY, 10065, USA.
Susan M GoodmanWeill Cornell Medicine, New York, NY, 10065, USA.
Peter SculcoHospital for Special Surgery, New York, NY, 10021, USA.
Fei WangWeill Cornell Medicine, New York, NY, 10065, USA.
Said IbrahimSidney Kimmel Medical College, Philadelphia, PA, 19107, USA.
Peter CramUniversity of Maryland School of Medicine, Baltimore, MD, 21201, USA.
Rich CaruanaIntelligible Inc., Redmond, WA, 98052, USA.
Bella MehtaWeill Cornell Medicine, New York, NY, 10065, USA. drbellamehta@gmail.com.

Funding

Deconstructing Disparities in Lupus PregnanciesK23AR082991 · NIAMS · HOSPITAL FOR SPECIAL SURGERY · PI Bella Mehta · 2024 to 2026
$528k
Anna-Maria and Stephen Kellen Foundation Total Knee Improvement ProgramNIAMS NIH HHS 1K23-AR-082991-01A1NIAMS NIH HHS K23 AR082991
6 · The paper itself

Abstract

backgroundRates of total knee arthroplasty (TKA) in the United States have risen in patients of a wide age range. Although rates of postoperative TKA complications have decreased, they remain a significant concern. In this study, we aim to determine how the risk of adverse TKA outcomes changes dynamically with age and explore the optimal ages with the lowest risk for adverse outcomes.

methodsThis retrospective cohort study included patients who underwent elective primary TKA from 2012 to 2018 in the Pennsylvania Health Care Cost Containment Council Database. We trained (70% train:30% test) an explainable boosting machine (EBM), a modern generalized additive model, to predict risk for 90-day mortality, 90-day readmission, 1-year revision, and longer length of stay (LOS). This "glass box" model allowed us to measure and visualize feature importance using mean absolute scores and determine the role of age in the model. We then ran EBM models that allowed two-way interactions between age and patient-level covariates.

resultsIn our cohort of 227,959 patients, 90-day readmission was observed in 7.5%, 90-day mortality in 0.2%, and 1-year revision in 0.8%. The median LOS was 2 days (IQR [2, 3]). Age was among the most important factors for predicting all outcomes, and these were nonlinear relationships. The risk for 90-day mortality increased substantially at 76.5 years, and for 90-day readmission and longer LOS at 73.5 years. Risk for 1-year revision was greater before 63.5 years.

conclusionsWe determined that there is a nonlinear relationship between age and risk for adverse TKA outcomes, and it changes dramatically at specific time points. Our data suggests that the optimal age for lower risk of 90-day mortality, 90-day readmission, and longer LOS is below 73.5 years, and above 63.5 years for 1-year revision. These findings can help in decision-making when trying to quantify risks related to aging.

Indexed as

AgeArtificial intelligenceMachine learningOutcomesRisk assessmentTotal knee arthroplasty

Identifiers

PMID41761380
PMCPMC12949503

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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.