Prospective Validation of a Demographically Based Primary Total Knee Arthroplasty Size Calculator

Sershon, Robert A.; Li, Jefferson; Calkins, Tyler E.; Courtney, P. Maxwell; Nam, Denis; Gerlinger, Tad L.; Sporer, Scott M.; Levine, Brett R.

doi:10.1016/j.arth.2019.02.048

Cited by 20 publications

(18 citation statements)

References 18 publications

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“…They were able to predict the tibial component size in 87% (n ¼ 412/474) and 76% (n ¼ 360/474) of femoral components [9]. This study has been further validated by the same group, and an electronic application has been developed for clinical use [13]. Considering the relatively small number (n ¼ 21) of the systems chosen for our study included in this validation study by the same group leaves some questions regarding the application of their study to multiple TKA systems and at other institutions.…”

Section: Introductionmentioning

confidence: 83%

“…Considering our institution performs a high volume of TKA, the primary purpose of this study was to investigate the ability to predict the tibial and femoral component size in a single implant system from patient characteristics, such as sex, weight, and height. A secondary goal was to compare the predicted tibial and femoral component sizes from our statistical model with the electronic application designed by Sershon (Arthroplasty Size Predictor, Apple App Store, 2017) [13]. Being able to accurately predict component sizes for those undergoing primary TKA surgery may improve operating room efficiency, decrease the total operative time, decrease operative waste, and decrease facility cost.…”

Section: Introductionmentioning

confidence: 99%

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Patient Demographics and Anthropometric Measurements Predict Tibial and Femoral Component Sizing in Total Knee Arthroplasty

et al. 2020

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Background: Accurate sizing is critical for the overall success of a total knee arthroplasty (TKA). This study's primary purpose was to investigate the ability to predict the tibial and femoral component size in a single implant system from patient demographics and anthropometric data. A secondary goal was to compare the predicted tibial and femoral component sizes from our statistical model with a previously validated electronic application used to predict the implant size. Methods: A consecutive series of 484 patients undergoing a primary TKA at a single institution was reviewed. Data on height, weight, body mass index, sex, age, and component size were collected. A proportional odds model was developed to predict tibial and femoral component sizes. The relationship between the proportional odds model predictions was also compared with the component sizes determined by the Arthroplasty Size Predictor electronic application. Results: Weight, height, and sex predicted the implanted component size with an accuracy of 54.0% (n ¼ 247/484) for the tibia and 51.1% (n ¼ 231/484) for the femur. The accuracy improved to 94.4% (n ¼ 457/ 484) for the tibia and 93.4% (n ¼ 452/484) for the femur within ±1 component size. Our data are highly correlated to the Arthroplasty Size Predictor for the predicted tibial component size (r ¼ 0.91, P < .001) and femoral component size (r ¼ 0.89, P < .001). Conclusions: Our novel templating model may improve operative efficiency for a single TKA system. Our findings have a high concordance with a widely available electronic application used to predict implant sizes for a variety of TKA systems.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Patient Demographics and Anthropometric Measurements Predict Tibial and Femoral Component Sizing in Total Knee Arthroplasty

et al. 2020

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“…Recently, several studies have been published that support predicting TKA component size based on demographic data. All authors report R 2 values ranging 0.50 to 0.79 and accuracy within one size in 85% to 100% of cases [8][9][10][11]24]. Recently, the equations presented by Bhowmik-Stoker et al were shown to be most accurate when applied to a unique patient population, still averaging within one size 88 and 92% of the time for femoral and tibial sizes, respectively [25].…”

Section: Discussionmentioning

confidence: 99%

Demographic data is more predictive of component size than digital radiographic templating in total knee arthroplasty

Wallace

Murphy

Schiffman

et al. 2020

Knee Surg & Relat Res

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Background Preoperative radiographic templating for total knee arthroplasty (TKA) has been shown to be inaccurate. Patient demographic data, such as gender, height, weight, age, and race, may be more predictive of implanted component size in TKA. Materials and methods A multivariate linear regression model was designed to predict implanted femoral and tibial component size using demographic data along a consecutive series of 201 patients undergoing index TKA. Traditional, two-dimensional, radiographic templating was compared to demographic-based regression predictions on a prospective 181 consecutive patients undergoing index TKA in their ability to accurately predict intraoperative implanted sizes. Surgeons were blinded of any predictions. Results Patient gender, height, weight, age, and ethnicity/race were predictive of implanted TKA component size. The regression model more accurately predicted implanted component size compared to radiographically templated sizes for both the femoral (P = 0.04) and tibial (P < 0.01) components. The regression model exactly predicted femoral and tibial component sizes in 43.7 and 43.7% of cases, was within one size 90.1 and 95.6% of the time, and was within two sizes in every case. Radiographic templating exactly predicted 35.4 and 36.5% of cases, was within one size 86.2 and 85.1% of the time, and varied up to four sizes for both the femoral and tibial components. The regression model averaged within 0.66 and 0.61 sizes, versus 0.81 and 0.81 sizes for radiographic templating for femoral and tibial components. Conclusions A demographic-based regression model was created based on patient-specific demographic data to predict femoral and tibial TKA component sizes. In a prospective patient series, the regression model more accurately and precisely predicted implanted component sizes compared to radiographic templating. Level of evidence Prospective cohort, level II.

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“…Height, weight, body mass index, age, gender, and American Society of Anesthesiologists scores were extracted for all patients and used as the combination of features incorporated into algorithm training (Table 1). These variables were selected based off of prior studies investigating prediction of TKA component sizes, which have demonstrated that these demographic variables hold the greatest predictive value [8,9,20]. American Society of Anesthesiologists score was included as it has demonstrated statistical associations with BMI and may therefore have an influence on these parameters that influence component size [21].…”

Section: Covariates and Missing Datamentioning

confidence: 99%

“…The variability in the accuracy of standard preoperative templating, in addition to the costs associated with preoperative imaging and patient-specific instrumentation, [6] poses a challenge to this end and necessitates more efficient and accurate planning methods [5,7]. Previous attempts at demographic-based final implant prediction have demonstrated acceptable accuracy ranging between 71% and 97%, although these models were designed to predict implant ranges of one size under or over the actual implant size [8,9]. Given the range in both the accuracy and variability of implant size predictions in such models, there remains a need for a more accurate and consistent prediction model.…”

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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Prospective Validation of a Demographically Based Primary Total Knee Arthroplasty Size Calculator

Cited by 20 publications

References 18 publications

Patient Demographics and Anthropometric Measurements Predict Tibial and Femoral Component Sizing in Total Knee Arthroplasty

Patient Demographics and Anthropometric Measurements Predict Tibial and Femoral Component Sizing in Total Knee Arthroplasty

Demographic data is more predictive of component size than digital radiographic templating in total knee arthroplasty

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

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