Development of Machine Learning Algorithms to Predict Patient Dissatisfaction After Primary Total Knee Arthroplasty

Kunze, Kyle N.; Polce, Evan M.; Sadauskas, Alexander J.; Levine, Brett R.

doi:10.1016/j.arth.2020.05.061

Cited by 62 publications

(62 citation statements)

References 22 publications

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“…(2020) [ 63 ] Moderate Low Moderate Low Low Moderate Moderate Moderate Kunze et al. (2020) [ 41 ] Moderate Moderate Low Low Moderate Low Low Moderate Belford et al. (2020) [ 64 ] Moderate Moderate Moderate Low Moderate Moderate Moderate Moderate Pua et al.…”

Section: Resultsmentioning

confidence: 99%

“…Several studies identified clinical comorbidities as significant predictive factors of poor outcomes after TKA [ 16 ]. Diabetes [ 20 , 39 ] or allergies [ 40 , 41 ] were singled out.…”

Section: Resultsmentioning

confidence: 99%

“…However, machine learning techniques have been shown to outperform linear regression model in specific tasks, such as prediction of post-TKA EQ-5D-3L visual analog scale [ 51 ], estimating risk of total joint replacement [ 52 ], and more recently length-of-stay prediction after TKA [ 34 , 53 ]. More and more studies try to use the machine learning to predict the TKA outcomes and to adapt the practices according to the established predictive features [ 21 , 26 , 29 , 41 ]. The development of clinical decision-making tools generated from machine learning, which can be used in consultation to help discuss risk stratification with patients, could provide a means of better understanding which patients are at a greater risk to experience dissatisfaction after primary TKA [ 34 , 54 , 55 ].…”

Section: Discussionmentioning

confidence: 99%

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Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

Batailler

Lording²,

Massari

et al. 2021

Arthroplasty Today

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Background: Predictive modeling promises to improve our understanding of what variables influence patient satisfaction after total knee arthroplasty (TKA). The purpose of this article was to systematically review the relevant literature using predictive models of clinical outcomes after TKA. The aim was to identify the predictor strategies used for systematic data collection with the highest likelihood of success in predicting clinical outcomes. Methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol systematic review was conducted using 3 databases (MEDLINE, EMBASE, and PubMed) to identify all clinical studies that had used predictive models or that assessed predictive features for outcomes after TKA between 1996 and 2020. The ROBINS-I tool was used to evaluate the quality of the studies and the risk of bias. Results: A total of 75 studies were identified of which 48 met our inclusion criteria. Preoperative predictive factors strongly associated with postoperative clinical outcomes were knee pain, knee-specific Patient-Reported Outcome Measure (PROM) scores, and mental health scores. Demographic characteristics, pre-existing comorbidities, and knee alignment had an inconsistent association with outcomes. The outcome measures that correlated best with the predictive models were improvement of PROM scores, pain scores, and patient satisfaction. Conclusions: Several algorithms, based on PROM improvement, patient satisfaction, or pain after TKA, have been developed to improve decision-making regarding both indications for surgery and surgical strategy. Functional features such as preoperative pain and PROM scores were highly predictive for clinical outcomes after TKA. Some variables such as demographics data or knee alignment were less strongly correlated with TKA outcomes. Level of evidence: Systematic review e Level III.

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Section: Resultsmentioning

confidence: 99%

“…Several studies identified clinical comorbidities as significant predictive factors of poor outcomes after TKA [ 16 ]. Diabetes [ 20 , 39 ] or allergies [ 40 , 41 ] were singled out.…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

Batailler

Lording²,

Massari

et al. 2021

Arthroplasty Today

View full text Add to dashboard Cite

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“…This process occurs for all 10 of the folds and then is repeated 3 times, which prevents overfitting and enhances the generalizability of the model. Computational differences between these 5 models have been described in detail in previously published literature [26][27][28][29][30].…”

Section: Data Preprocessing and Model Trainingmentioning

confidence: 99%

“…After model development on the training set, the performance of each model was evaluated on the independent testing (hold-out) set of patients. Metrics used to assess model performance, including discrimination, calibration, Brier score, and decision curve analysis, have been described previously [26,31]. Briefly, discrimination was evaluated using receiver operating characteristic (ROC) curve with area under the curve (AUC) analysis [32,33].…”

Section: Evaluation Of Model Performancementioning

confidence: 99%

Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty

et al. 2021

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Background: Despite reasonable accuracy with preoperative templating, the search for an optimal planning tool remains an unsolved dilemma. The purpose of the present study was to apply machine learning (ML) using preoperative demographic variables to predict mismatch between templating and final component size in primary total knee arthroplasty (TKA) cases. Methods: This was a retrospective case-control study of primary TKA patients between September 2012 and April 2018. The primary outcome was mismatch between the templated and final implanted component sizes extracted from the operative database. The secondary outcome was mismatch categorized as undersized and oversized. Five supervised ML algorithms were trained using 6 demographic features. Prediction accuracies were obtained as a metric of performance for binary mismatch (yes/no) and multilevel (undersized/correct/oversized) classifications. Results: A total of 1801 patients were included. For binary classification, the best-performing algorithm for predicting femoral and tibial mismatch was the stochastic gradient boosting model (area under the curve: 0.76/0.72, calibration intercepts: 0.05/0.05, calibration slopes: 0.55/0.7, and Brier scores: 0.20/0.21). For multiclass classification, the best-performing algorithms had accuracies of 83.9% and 82.9% for predicting the concordance/mismatch of the femoral and tibial implant, respectively. Model predictions of greater than 51.0% and 47.9% represented high-risk thresholds for femoral and tibial sizing mismatch, respectively. Conclusions: ML algorithms predicted templating mismatch with good accuracy. External validation is necessary to confirm the performance and reliability of these algorithms. Predicting sizing mismatch is the first step in using ML to aid in the prediction of final TKA component sizes. Further studies to optimize parameters and predictions for the algorithms are ongoing.

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Utilizing AI to Improve Orthopedic Care

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Artificial Intelligence for Bone Disorder

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Development of Machine Learning Algorithms to Predict Patient Dissatisfaction After Primary Total Knee Arthroplasty

Cited by 62 publications

References 22 publications

Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis

Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty

Utilizing AI to Improve Orthopedic Care

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