Development of Machine Learning Algorithms to Predict Clinically Meaningful Improvement for the Patient-Reported Health State After Total Hip Arthroplasty

Kunze, Kyle N.; Karhade, Aditya V.; Sadauskas, Alex J.; Schwab, Joseph H.; Levine, Brett R.

doi:10.1016/j.arth.2020.03.019

Cited by 65 publications

(91 citation statements)

References 18 publications

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“… 20 , 37 Promising work in artificial intelligence can enhance decision making with generated personalized predictions using prior patient reported outcome measures, patient clinical risk factors, and psychosocial risk factors (depression, patient activation). 38 - 40 Personalized predictions provide an additional metric to engage patients, and guide discussions about surgical appropriateness and postoperative expectations. Importantly, engagement methods focused on decision making can be introduced into the clinical setting without impacting efficiency of the office visits, which benefits all stakeholders.…”

Section: Discussionmentioning

confidence: 99%

Patient Engagement Approaches in Total Joint Arthroplasty: A Review of Two Decades

Alokozai

Bernstein

Samuel

et al. 2021

Journal of Patient Experience

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Patient engagement is a comprehensive approach to health care where the physician inspires confidence in the patient to be involved in their own care. Most research studies of patient engagement in total joint arthroplasty (TJA) have come in the past 5 years (2015-2020), with no reviews investigating the different patient engagement methods in TJA. The primary purpose of this review is to examine patient engagement methods in TJA. The search identified 31 studies aimed at patient engagement methods in TJA. Based on our review, the conclusions therein strongly suggest that patient engagement methods in TJA demonstrate benefits throughout care delivery through tools focused on promoting involvement in decision making and accessible care delivery (eg, virtual rehabilitation, remote monitoring). Future work should understand the influence of social determinants on patient involvement in care, and overall cost (or savings) of engagement methods to patients and society.

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Section: Discussionmentioning

confidence: 99%

Patient Engagement Approaches in Total Joint Arthroplasty: A Review of Two Decades

Alokozai

Bernstein

Samuel

et al. 2021

Journal of Patient Experience

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“…Variables were included only if they had less than 30% missing data, which is a threshold demonstrated to be acceptable and previous literature using machine learning. 11 Multiple imputation was applied for variables with less than 30% missing data. 12 , 13 Potential covariates included preoperative demographic variables routinely collected in the secure clinical repository.…”

Section: Methodsmentioning

confidence: 99%

Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome

Kunze

Burnett

Lee³

et al. 2020

Arthroscopy, Sports Medicine, and Rehabilitation

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Purpose To determine factors predictive of patients who are at risk for being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome (FAIS). Methods A prospective clinical repository was queried between January 2012 and October 2017 and all patients who underwent hip arthroscopy for primary or revision FAIS with minimum 2-year follow-up were included. A total of 27 potential risk factors for loss to follow-up were available and tested for predictive value. An 80:20 random sample split of all patients was performed to create training and testing sets. Cross-validation, minimum Bayes information criteria, and adaptive machine-learning algorithms were used to develop the predictive model. The model with the best predictive performance was selected based off of the lowest postestimation deviance between the training and testing samples. The c-statistic is a measure of discrimination. It ranges from 0.5 to 1.0, with 1.0 being perfect discrimination and 0.5 indicating the model is no better than chance. A log-likelihood χ 2 test was used to evaluate the goodness-of-fit of the logistic regression model. Results A total of 2113 patients were included. Inference of minimum Bayes information criteria model indicated that male sex (odds ratio [OR] 1.82, P = .028), non-white race (African American OR 2.41, P = .013; other non-white OR 1.42, P = .042), smoking (OR 1.07, P = .021), and failure to provide a phone number (OR 1.78, P = .032) increased the risk for being lost to follow-up. Furthermore, greater preoperative International Hip Outcome Tool 12-item component questionnaire (OR 1.03, P = .004), and modified Harris Hip Score (OR 1.05, P = .014) scores increased the risk of being lost to follow-up. The c-statistic was 0.76 (95% confidence interval 0.701-0.848). The log-likelihood indicated that the regression model as a whole was statistically significant ( P = .002). Conclusions Patients who are male, non-white, smokers, fail to provide a telephone number, and have greater preoperative modified Harris Hip Score and International Hip Outcome Tool 12-item component questionnaire scores are at an increased risk for being lost to follow-up 2 years after hip arthroscopy for FAIS. Level of Evidence Level III, case control study

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“…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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Development of Machine Learning Algorithms to Predict Clinically Meaningful Improvement for the Patient-Reported Health State After Total Hip Arthroplasty

Cited by 65 publications

References 18 publications

Patient Engagement Approaches in Total Joint Arthroplasty: A Review of Two Decades

Patient Engagement Approaches in Total Joint Arthroplasty: A Review of Two Decades

Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome

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

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