Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model

Ramkumar, Prem N.; Karnuta, Jaret M.; Navarro, Sergio M.; Haeberle, Heather S.; Scuderi, Giles R.; Mont, Michael A.; Krebs, Viktor E.; Patterson, Brendan M.

doi:10.1016/j.arth.2019.05.034

Cited by 76 publications

(67 citation statements)

References 19 publications

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“…At the process level, AI and DL applied to different preoperative variables are shown to predict length of stay, inpatient charges, and discharge disposition prior to undergoing primary TKR 56 . Facilitating dialog through greater patient involvement in the decision‐making process may also mean lengthier discussions and even greater utilization 57 .…”

Section: Discussionmentioning

confidence: 99%

Advanced decision‐making using patient‐reported outcome measures in total joint replacement

Jayakumar

Bozic

2020

Journal Orthopaedic Research

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Up to one‐third of total joint replacement (TJR) procedures may be performed inappropriately in a subset of patients who remain dissatisfied with their outcomes, stressing the importance of shared decision‐making. Patient‐reported outcome measures capture physical, emotional, and social aspects of health and wellbeing from the patient's perspective. Powerful computer systems capable of performing highly sophisticated analysis using different types of data, including patient‐derived data, such as patient‐reported outcomes, may eliminate guess work, generating impactful metrics to better inform the decision‐making process. We have created a shared decision‐making tool which generates personalized predictions of risks and benefits from TJR based on patient‐reported outcomes as well as clinical and demographic data. We present the protocol for a randomized controlled trial designed to assess the impact of this tool on decision quality, level of shared decision‐making, and patient and process outcomes. We also discuss current concepts in this field and highlight opportunities leveraging patient‐reported data and artificial intelligence for decision support across the care continuum.

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Section: Discussionmentioning

confidence: 99%

Advanced decision‐making using patient‐reported outcome measures in total joint replacement

Jayakumar

Bozic

2020

Journal Orthopaedic Research

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“…The most recent and analogous study to this study was performed by Ramkumar et al [ 19 ]. The authors trained an ANN using the Nationwide Inpatient Sample database and externally validated it using a local prospective institutional database (Orthopaedic Minimal Data Set Episode of Care).…”

Section: Discussionmentioning

confidence: 99%

“…After external validation of their model with a local prospective database, the AUC and accuracy were 69.2% and 64.4%, respectively. The model in the present study trained on local institutional data performed similar to the model of Ramkumar et al [ 19 ] trained on Nationwide Inpatient Sample data in their respective validation cohorts ( Table 3 ). This is an interesting finding because it is commonly accepted that to optimize ML performance, more data are thought to be better.…”

Section: Discussionmentioning

confidence: 99%

“… Study lead author Discharge tool Cohort Accuracy AUC % Of cohort discharge to SNFs The present study ANN Retrospective Institutional 91.7% in the validation cohort 0.97 in the validation cohort 25% in the training cohort 61.3% in the testing cohort 0.80 in the testing cohort 6.7% in the testing cohort Ramkumar et al. [ 19 ] ANN NIS—training 69.4% in validation cohort 0.76 in the validation cohort 45.6% OME—validation 64.4% in testing cohort 0.69 in the testing cohort 47.1% Gholson et al. [ 12 ] ACS-NSQIP Retrospective multicenter n/a 0.70 (not validated) n/a Menendez et al.…”

Section: Discussionmentioning

confidence: 99%

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An Electronic Medical Record–Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning

et al. 2020

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Background Determining discharge disposition after total joint arthroplasty (TJA) has been a challenge. Advances in machine learning (ML) have produced computer models that learn by example to generate predictions on future events. We hypothesized a trained ML algorithm’s diagnostic accuracy will be better than that of current predictive tools to predict discharge disposition after primary TJA. Methods This study was a retrospective cohort study from a single, tertiary referral center for primary TJA. We trained and validated an artificial neural network (ANN) based on 4368 distinct surgical encounters between 1/1/2013 and 6/28/2016. The ANN’s ability to identify discharge disposition was then tested on 1452 distinct surgical encounters between 1/3/17 and 11/30/17. Results The area under the curve and accuracy achieved during model validation were 0.973 and 91.7%, respectively, with 25% of patients being discharged to skilled nursing facilities (SNFs). Within our testing data set, 6.7% of patients went to SNFs. The performance in the testing set included an area under the curve of 0.804, accuracy of 61.3%, sensitivity of 28.9%, and specificity of 93.8%. Conclusions This is the first prediction tool using an electronic medical record–integrated ANN to predict discharge disposition after TJA based on locally generated data. Dramatically reduced numbers of patients discharged to SNFs due to implementation of a bundled payment model lead to poor recall in the testing model. This model serves as a proof of concept for developing an ML prediction tool using a relatively small data set and subsequent integration into the electronic medical record.

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“…Machine learning has demonstrated considerable potential in the context of value-based health care [12]. By developing advanced prognostic tools for outcome prediction, machine learning overcomes the limitations inherent in traditional regression analyses such as dependence on predefined relationships and collinearity [13][14][15][16]. As such, machine learning is particularly poised to be applied to predictive models in orthopedic surgery.…”

Section: Introductionmentioning

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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Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model

Cited by 76 publications

References 19 publications

Advanced decision‐making using patient‐reported outcome measures in total joint replacement

Advanced decision‐making using patient‐reported outcome measures in total joint replacement

An Electronic Medical Record–Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning

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

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