Total knee arthroplasty component templating: a predictive model

Accuracy and clinical role of digital templating for total knee arthroplasty performed on haemophilic knees

INTRODUCTION

In total knee arthroplasty (TKA), choosing the correct implant size is important. There is lack of data on accuracy of templating on haemophilic knees. Our aim was to test the accuracy of 2D digital templating for TKA on haemophilic arthropathy (HA) of knee.

MATERIALS AND METHODS

TKAs performed on HA between January 2011 and January 2022 were screened. Osteoarthritis (OA) group was created as control group by a one-to-one matching regarding type of implant used. Intra- and interobserver correlations were measured in HA, then correlation between templated and implanted sizes was investigated in four assessments (femur AP, femur lateral, tibia AP, tibia lateral), then compared with OA group. Fifty-eight knees in each group included.

RESULTS

Regarding intraobserver correlation in HA, there was excellent correlation for femur AP [.93 (.73-.98)], femur lateral [.98 (.91-.99)], and tibia AP (1.0) templating. Regarding interobserver correlation in HA, excellent correlation was observed for femur lateral [.93 (.74-.98)] and tibia AP templating [.90 (.65-.97)]. Regarding correlation of templated and applied sizes in HA; tibia AP, tibia lateral and femur lateral templating showed good correlation [.81 (.70-.89), .86 (.77-.91), .79 (.67-.87) while femur AP templating showed moderate correlation [.67 (.50-.79)]. Comparing HA and OA, there was no difference in correlation levels regarding femur AP, femur lateral, tibia AP and tibia lateral templating (p = .056, p = .781, p = .761, p = .083, respectively).

CONCLUSION

Although 2D digital templating shows comparable correlation in HA and OA, clinical applicability of templating on HA appears to be limited in its current state.

Prediction of Total Knee Arthroplasty Sizes with Demographics, including Hand and Foot Sizes

Anticipating implant sizes before total knee arthroplasty (TKA) allows the surgical team to streamline operations and prepare for potential difficulties. This study aims to determine the correlation and derive a regression model for predicting TKA sizes using patient-specific demographics without using radiographs. We reviewed the demographics, including hand and foot sizes, of 1,339 primary TKAs. To allow for comparison across different TKA designs, we converted the femur and tibia sizes into their anteroposterior (AP) and mediolateral (ML) dimensions. Stepwise multivariate regressions were performed to analyze the data. Regarding the femur component, the patient's foot, gender, height, hand circumference, body mass index, and age was the significant demographic factors in the regression analysis (R-square 0.541, p < 0.05). For the tibia component, the significant factors in the regression analysis were the patient's foot size, gender, height, hand circumference, and age (R-square 0.608, p < 0.05). The patient's foot size had the highest correlation coefficient for both femur (0.670) and tibia (0.697) implant sizes (p < 0.05). We accurately predicted the femur component size exactly, within one and two sizes in 49.5, 94.2, and 99.9% of cases, respectively. Regarding the tibia, the prediction was exact, within one and two sizes in 53.0, 96.0, and 100% of cases, respectively. The regression model, utilizing patient-specific characteristics, such as foot size and hand circumference, accurately predicted TKA femur and tibia sizes within one component size. This provides a more efficient alternative for preoperative planning.

Can Component Size in Total Knee Arthroplasty Be Predicted Preoperatively?-An Analysis of Patient Characteristics

INTRODUCTION

 Accurately predicting component sizing in total knee arthroplasty (TKA) can ensure appropriate implants are readily available, avoiding complications from malsizing while also reducing cost by improving workflow efficiency through a reduction in instrumentation. This study investigated the utility of demographic variables to reliably predict TKA component sizes.

METHODS AND MATERIALS

 A retrospective chart review of 337 patients undergoing primary TKA was performed. Patient characteristics (age, sex, race, height, weight) were recorded along with implant and shoe size. Correlation between shoe size and TKA component size was assessed using Pearson's correlation coefficient and linear regression analysis using three models: (A) standard demographic variables, (B) shoe size, and (C) combination of both models.

RESULTS

 Shoe size demonstrated the strongest correlation with femoral anteroposterior (FAP) (p < 0.001) followed by height (p < 0.001). Conversely, height exhibited the strongest correlation with tibial mediolateral (TML) (p < 0.001) followed by shoe size (p < 0.001). Model C was able to correctly predict both the femur and tibia within one and two sizes in 83.09 and 98.14% of cases, respectively. Individually, model C predicted the FAP within one and two sizes in 83.09 and 96.14% of cases, and the TML in 98.81 and 100% of cases, respectively.

CONCLUSION

 A patient's shoe size demonstrates a strong correlation to the TKA implant size, and when combined with standard demographic variables the predictive reliability is further increased. Here, we present a predictive model for implant sizing based solely on easily attainable demographic variables, that will be useful for preoperative planning to improve surgical efficiency.

LEVEL OF EVIDENCE

 II, Diagnostic.

Magnification assessment of radiographs for knee replacement (MARKeR) - A pilot study in a low-resource setting

Background

Selecting the correct size of implants to be used in total knee arthroplasty is critical for a successful outcome. Marker-less templating systems use an institutionally derived magnification factor for all radiographs.

Purpose

To determine the institutional magnification of knee radiographs for patients awaiting total knee arthroplasty.

Material and Methods

Eighty patients awaiting total knee arthroplasty underwent preoperative knee radiographs using a standardized protocol. A marker attached to the patients’ knees at the level of the knee joint was used to calculate the magnification factor on both anteroposterior (AP) and lateral (LAT) views. Two independent observers estimated the magnification to determine the intra and inter-observer reliability.

Results

The mean magnification of the AP (15.3%) radiograph was significantly greater than the LAT (12.1%) radiograph (p< 0.0001). Patients with absent markers on their radiographs were heavier than patients in whom the marker was visible (84.7 kgs vs. 76.6 kgs, p=0.01). No marker was visible on the radiographs in 56.3% (45/80) of patients. There was excellent inter and intra-observer reliability of both the AP and LAT measurements.

Conclusion

After standardizing the protocol for preoperative knee radiographs, our results show significantly greater institutional magnification of the anteroposterior compared with the lateral images. Accurate templating in knee arthroplasty requires both radiographic images. To reduce errors in implant sizing, we recommend surgeons use different institutional magnification factors for the anteroposterior and lateral radiographs.

Accuracy of one-dimensional templating on linear EOS radiography allows template-directed instrumentation in total knee arthroplasty

BACKGROUND

Templating for total knee arthroplasty (TKA) is routinely performed on two-dimensional standard X-ray images and allows template-directed instrumentation. To date, there is no report on one-dimensional (1D) anteroposterior (AP) templating not requiring specific templating software. We aim to describe a novel technique and explore its reliability, accuracy and potential cost-savings.

METHODS

We investigated a consecutive series of TKAs at one institution between January and July 2019. Patients with preoperative low-dose linear AP EOS radiography images were included. Implant component sizes were retrospectively templated on the AP view with the hospitals imaging viewing software by two observers who were blinded to the definitive implant size. Planning accuracy as well as inter- and intra-observer reliability was calculated. Cost-savings were estimated based on the reduction of trays indicated by the 1D templating size estimations.

RESULTS

A total of 141 consecutive TKAs in 113 patients were included. Accuracy of 1D templating was as follows: exact match in 53% femoral and 63% tibial components, within one size in 96% femoral and 98% tibial components. Overall 58% of TKA components were planned correctly and 97% within one size. Inter- and intra-rater reliability was good (κ = 0.66) and very good (κ = 0.82), respectively. This templating process can reduce instrumentation from six to three trays per case and therefore halve sterilisation costs.

CONCLUSIONS

The new 1D templating method using EOS AP imaging predicts component sizes in TKA within one size 97% of the time and can halve the number of instrumentation trays and sterilisation costs.

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

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.

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

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.

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

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 = 247/484) for the tibia and 51.1% (n = 231/484) for the femur. The accuracy improved to 94.4% (n = 457/484) for the tibia and 93.4% (n = 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 (ρ = 0.91, P < .001) and femoral component size (ρ = 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.

Prospective Comparison of Available Primary Total Knee Arthroplasty Sizing Equations

BACKGROUND

Several studies have proposed regression equations that can increase the accuracy of predicting femur and tibia component sizes for total knee arthroplasty (TKA). This study compared available regression equations in their ability to prospectively predict component size in a unique patient series.

METHODS

Demographic data and implanted femur and tibia TKA component sizes were collected on a consecutive 382 patients undergoing index TKA. Equations by Bhowmik-Stoker et al, Ren et al, Sershon et al, and Miller et al were identified that used age, race, ethnicity, gender, height, weight, or body mass index. Equation outputs were converted to implant-corrected sizes and compared to the implanted component.

RESULTS

Femur and tibia sizes were accurately predicted within 1 size 88% and 92%, 84% and 86%, and 79% and 92% for Bhowmik-Stoker et al, Sershon et al, and Miller et al, respectively. Ren et al was within 1 tibia size 88% of the time. Adding one more common implant size improved this accuracy by an average of 9.1% and 6.6% for the femur and tibia, respectively. For femur components, Bhowmik-Stoker et al outperformed Sershon et al by 0.14 sizes (P < .001) and Miller et al by 0.21 sizes (P < .001) on average. For tibia components, Bhowmik-Stoker et al outperformed Sershon et al by 0.09 sizes (P = .028) and Ren et al by 0.11 sizes (P = .005) on average.

CONCLUSION

Equations by Bhowmik-Stoker et al more accurately predicted implanted TKA size. In cases of greater uncertainty, the practicing surgeon may err on having more common TKA sizes available.

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

BACKGROUND

Preoperative planning for total knee arthroplasty (TKA) is essential for streamlining operating room efficiency and reducing costs. Digital templating and patient-specific instrumentation have shown some value in TKA but require additional costs and resources. The purpose of this study was to validate a previously published algorithm that uses only demographic variables to accurately predict TKA tibial and femoral component sizes.

METHODS

Four hundred seventy-four consecutive patients undergoing elective primary TKA were prospectively enrolled. Four surgeons were included, three of which were unaffiliated with the retrospective cohort study. Patient sex, height, and weight were entered into our published Arthroplasty Size Prediction mobile application. Accuracy of the algorithm was compared with the actual sizes of the implanted femoral and tibial components from 5 different implant systems. Multivariate regression analysis was used to identify independent risk factors for inaccurate outliers for our model.

RESULTS

When assessing accuracy to within ±1 size, the accuracies of tibial and femoral components were 87% (412/474) and 76% (360/474). When assessing accuracy to within ±2 sizes of predicted, the tibial accuracy was 97% (461/474), and the femoral accuracy was 95% (450/474). Risk factors for the actual components falling outside of 2 predicted sizes include weight less than 70 kg (odds ratio = 2.47, 95% confidence interval [1.21-5.06], P = .01) and use of an implant system with <2.5 mm incremental changes between femoral sizes (odds ratio = 5.50, 95% confidence interval [3.33-9.11], P < .001).

CONCLUSIONS

This prospective series of patients validates a simple algorithm to predict component sizing for TKA with high accuracy based on demographic variables alone. Surgeons can use this algorithm to simplify the preoperative planning process by reducing unnecessary trays, trials, and implant storage, particularly in the community or outpatient setting where resources are limited. Further assessment of components with less than 2.5-mm differences between femoral sizes is required in the future to make this algorithm more applicable worldwide.

Using Patient Demographics and Statistical Modeling to Predict Knee Tibia Component Sizing in Total Knee Arthroplasty

BACKGROUND

Preoperative planning is important to achieve successful implantation in primary total knee arthroplasty (TKA). However, traditional TKA templating techniques are not accurate enough to predict the component size to a very close range.

METHODS

With the goal of developing a general predictive statistical model using patient demographic information, ordinal logistic regression was applied to build a proportional odds model to predict the tibia component size. The study retrospectively collected the data of 1992 primary Persona Knee System TKA procedures. Of them, 199 procedures were randomly selected as testing data and the rest of the data were randomly partitioned between model training data and model evaluation data with a ratio of 7:3. Different models were trained and evaluated on the training and validation data sets after data exploration.

RESULTS

The final model had patient gender, age, weight, and height as independent variables and predicted the tibia size within 1 size difference 96% of the time on the validation data, 94% of the time on the testing data, and 92% on a prospective cadaver data set.

CONCLUSION

The study results indicated the statistical model built by ordinal logistic regression can increase the accuracy of tibia sizing information for Persona Knee preoperative templating. This research shows statistical modeling may be used with radiographs to dramatically enhance the templating accuracy, efficiency, and quality. In general, this methodology can be applied to other TKA products when the data are applicable.

Reliability and accuracy of digital templating for the humeral component of total shoulder arthroplasty

BACKGROUND

This experimental study evaluated the interobserver reliability and accuracy of pre-operative digital templating for humeral head size, stem size and neck angle for total shoulder arthroplasty.

METHODS

Twenty-five patients underwent a total shoulder arthroplasty with a single prosthesis. Four independent, blinded surgeons (two experienced shoulder surgeons and two PGY-6 fellows) used pre-operative radiographs and templating software to generate templates of the humeral head, stem and neck for each patient. Interobserver reliability was calculated using weighted kappa (κ) analysis. Accuracy was assessed by comparing templates to actual implant sizes.

RESULTS

Interobserver reliability was fair to substantial (κ = 0.26 to 0.71) for head size, fair to substantial (κ = 0.39 to 0.72) for stem size and slight to fair (κ = 0.16 to 0.34) for neck angle. Templated head size, stem size and neck angle had accuracies of 53%, 77% and 68% within one size variation, respectively. Experience did not affect accuracy (p = 0.11 to 0.48).

CONCLUSIONS

Digital templating is not a useful guide for pre-operative surgical planning and should not be used to select a prosthesis.

Template-directed instrumentation in total knee arthroplasty: cost savings analysis

The use of digital radiography and templating software in total knee arthroplasty (TKA) continues to become more prevalent as the number of procedures performed increases every year. Template-directed instrumentation (TDI) is a novel approach to surgical planning that combines digital templating with limited intraoperative instruments. The purpose of this study was to evaluate the financial implications and radiographic outcomes of using TDI to direct instrumentation during primary TKA. Over a 1-year period, 82 consecutive TKAs using TDI were retrospectively reviewed. Patient demographics and preoperative templated sizes of predicted components were recorded, and OrthoView digital planning software (OrthoView LLC, Jacksonville, Florida was used to determine the 2 most likely tibial and femoral component sizes for each case. This sizing information was used to direct component vendors to prepare 3 lightweight instrument trays based on these sizes. The sizes of implanted components and the number of total trays required were documented. A cost savings analysis was performed to compare TDI and non-TDI surgical expenses for TKA. In 80 (97%) of 82 cases, the prepared sizes determined by TDI using 3 instrument trays were sufficient. Preoperative templating correctly predicted the size of the tibial and femoral component sizes in 90% and 83% of cases, respectively. The average number of trays used with TDI was 3.0 (range, 3-5 trays) compared with 7.5 (range, 6-9 trays) used in 82 preceding non-TDI TKAs. Based on standard fees to sterilize and package implant trays (approximately $26 based on a survey of 10 orthopedic hospitals performing TKA), approximately $9612 was saved by using TDI over the 1-year study period. Overall, digital templating and TDI were a simple and cost-effective approach when performing primary TKA.