2018
DOI: 10.1093/aje/kwy121
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Estimating an Individual’s Probability of Revision Surgery After Knee Replacement: A Comparison of Modeling Approaches Using a National Data Set

Abstract: Tools that provide personalized risk prediction of outcomes after surgical procedures help patients make preference-based decisions among the available treatment options. However, it is unclear which modeling approach provides the most accurate risk estimation. We constructed and compared several parametric and nonparametric models for predicting prosthesis survivorship after knee replacement surgery for osteoarthritis. We used 430,455 patient-procedure episodes between April 2003 and September 2015 from the N… Show more

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Cited by 22 publications
(19 citation statements)
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References 62 publications
(61 reference statements)
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“… 13 , 17 However, these variables do influence other clinical outcomes of interest to patients in the decision-making process that justifies their inclusion in the model. 7 …”
Section: Discussionmentioning
confidence: 99%
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“… 13 , 17 However, these variables do influence other clinical outcomes of interest to patients in the decision-making process that justifies their inclusion in the model. 7 …”
Section: Discussionmentioning
confidence: 99%
“…4 - 6 Personalized instruments are more complex to construct than generic ones and require data gathered from a population of sufficient size, and with sufficient diversity of input and output variables to provide a statistically meaningful estimation of the outcomes. 7 , 8 …”
Section: Introductionmentioning
confidence: 99%
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“…Nationwide and worldwide statistical data has been collected to analyze the rate and trends of revision (2,(54)(55)(56). Recently, a research group established several parametric and non-parametric models to estimate prostheses' survivorship more accurately (57). Another research group analyzed the predictive factors of revision, prosthetic infection and mortality in rheumatoid arthritis patients based on Danish healthcare registers (58).…”
Section: Discussionmentioning
confidence: 99%
“…In this paper, we will include these ensemble techniques in the definition of machine learning even though some of the component algorithms are parametric statistical models. Recent applications of machine learning methods for clinical prediction include: assessment of delirium risk (Wong et al., 2018), 3‐year survival for CF patients (Alaa & van der Schaar, 2018), mortality in coronary artery disease (Steele et al., 2018) and time to revision surgery after knee replacement (Aram et al., 2018). These studies and others have compared machine learning methods to traditional methods and found mixed results with some finding superior performance for parametric statistical models and others finding improved results with various machine learning algorithms.…”
Section: Introductionmentioning
confidence: 99%