Corrigendum: Electronic application for rabies management improves surveillance, data quality, and investigator experience in Haiti

[This corrects the article DOI: 10.3389/fvets.2023.1052349.].

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.

Can Demographic Variables Accurately Predict Component Sizing in Primary Total Knee Arthroplasty?

BACKGROUND

As health care reform drives providers to reduce costs and improve efficiencies without compromising patient care, preoperative planning has become imperative. The purpose of this study is to determine whether height, weight, and gender can accurately predict total knee arthroplasty (TKA) sizing.

METHODS

A consecutive series of 3491 primary TKAs performed by 2 surgeons was reviewed. Height, weight, gender, implant, preoperative templating sizes, and final implant sizes were collected. Implant-specific dimensions were collected from vendors. Using height, weight, and gender, a multivariate linear regression was performed with and without the inclusion of preoperative templating. Accuracy of the model was reported for commonly used implants.

RESULTS

There was a significant linear correlation between height, weight, and gender for femoral (R² = 0.504; P < .001) and tibial sizes (R² = 0.610; P < .001). Adding preoperative templating to the regression analysis increased the overall model fit for both the femoral (R² = 0.756; P < .001) and tibial sizes (R² = 0.780; P < .001). Femoral and tibial sizes were accurately predicted within 1 size of the final implant 71%-92% and 81%-97% using demographics alone or 85%-99% and 90%-99% using both templating and demographics, respectively.

CONCLUSION

This novel TKA templating model allows final implants to be predicted to within 1 size. The model allows for simplified preoperative planning and potential implementation into a cost-savings program that limits inventory and trays required for each case.