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

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.

Radiopacity Enhancements in Polymeric Implant Biomaterials: A Comprehensive Literature Review

Polymers as biomaterials possess favorable properties, which include corrosion resistance, light weight, biocompatibility, ease of processing, low cost, and an ability to be easily tailored to meet specific applications. However, their inherent low X-ray attenuation, resulting from the low atomic numbers of their constituent elements, i.e., hydrogen (1), carbon (6), nitrogen (7), and oxygen (8), makes them difficult to visualize radiographically. Imparting radiopacity to radiolucent polymeric implants is necessary to enable noninvasive evaluation of implantable medical devices using conventional imaging methods. Numerous studies have undertaken this by blending various polymers with contrast agents consisting of heavy elements. The selection of an appropriate contrast agent is important, primarily to ensure that it does not cause detrimental effects to the relevant mechanical and physical properties of the polymer depending upon the intended application. Furthermore, its biocompatibility with adjacent tissues and its excretion from the body require thorough evaluation. We aimed to summarize the current knowledge on contrast agents incorporated into synthetic polymers in the context of implantable medical devices. While a single review was found that discussed radiopacity in polymeric biomaterials, the publication is outdated and does not address contemporary polymers employed in implant applications. Our review provides an up-to-date overview of contrast agents incorporated into synthetic medical polymers, encompassing both temporary and permanent implants. We expect that our results will significantly inform and guide the strategic selection of contrast agents, considering the specific requirements of implantable polymeric medical devices.

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.

Preoperative Templating for Total Hip Arthroplasty: A Method for Calibrating Digital Radiographs Using Patient Demographics and Anthropometric Measurements

Background Preoperative templating aids the surgeon in estimating implant size and placement. Calibration markers are used to set the correct magnification of digital images before templating. Improper marker placement or complete absence can lead to inaccuracy or an inability to calibrate images altogether. Aims This study describes a method for calibrating images using a patient's femoral head size (FHS) predicted using demographics and anthropometric data. Materials and methods A formula predicting the FHS was derived from a cohort of 507 patients who underwent hemiarthroplasty for an intracapsular fractured neck of the femur through multivariate regression analysis. A separate validation cohort (n=50) who had undergone total hip arthroplasty (THA) had postoperative radiographs calibrated using the predicted FHS and the native contralateral hip as a surrogate calibration marker. The THA femoral head implant size was subsequently measured and compared with the actual implant size selected intraoperatively. Measurements were performed by two independent assessors to determine intra- and interobserver reliability. Results Multivariate regression analyses showed four variables significantly correlated with the size of the femoral head: gender (p < 0.001), height (p < 0.001), weight (p < 0.001), and race (Asian) (p = 0.01). Using these, a regression model to predict the FHS was obtained with an R2 value of 0.65 and a standard error of 2.18 mm. The validation cohort showed that THA head implant size could be accurately measured with an average root-mean-squared error (RMSE) of 1.41 mm (SD = 0.97 mm; %RMSE = 4.7%). The implant head size was measured to be within 5%, 10%, and 15% RMSE in 57.5%, 93.0%, and 100.0% of cases, respectively. There was excellent intraobserver (R2 = 0.94 and 0.95) and interobserver (R2 = 0.94) reliability. Conclusions The novel method proposed and validated in this study, using a predicted FHS to calibrate digital images, provides an alternative means of templating THA for fractured neck of the femur patients, in whom external calibration markers are often absent.

Preoperative predictors of implant size in patients undergoing total knee arthroplasty: a retrospective cohort study

BACKGROUND

Traditionally, the size of total knee arthroplasty (TKA) components is predicted by preoperative radiographic templating, which is of limited accuracy. This study aimed to evaluate the role of demographic data and ankle volume in predicting implant size in TKA candidates.

METHODS

In a retrospective study, 415 patients who underwent TKA at a single institution were included. The mean age of the patients was 67.5 ± 7.1 years. The mean BMI of the patients was 31.1 ± 4.7 kg/m2. TKA implants were Zimmer Biomet NexGen LPS-Flex Knee in all cases. The demographic data included age, sex, height, weight, BMI, ethnicity, and ankle volume. Ankle volume was assessed with the figure-of-eight method. Multivariate linear regression analysis was used for predicting factors of implant size.

RESULTS

Multivariate linear regression analysis showed that the Sex (β:1.41, P < 0.001), height (β:0.058, P < 0.001), ankle volume (β:0.11, P < 0.001), and Age (β:0.017, P = 0.004) were significant predictors of tibial component size. Sex (β:0.89, P < 0.001), height (β:0.035, P < 0.001), and ankle volume(β:0.091, P < 0.001) were significant predictors of femoral component size in the multivariate analysis.

CONCLUSION

Demographic data, adjunct with the ankle volume, could provide a promising model for preoperative prediction of the size of tibial and femoral components in TKA candidates.

Validation and performance of a machine-learning derived prediction guide for total knee arthroplasty component sizing

INTRODUCTION

Anticipation of patient-specific component sizes prior to total knee arthroplasty (TKA) is essential to avoid excessive cost associated with additional surgical trays and morbidity associated with imperfect sizing. Current methods of size prediction, including templating, are inconsistent and time-consuming. Machine learning (ML) algorithms may allow for accurate TKA component size prediction with the ability to make predictions in real-time.

METHODS

Consecutive patients receiving primary TKA between 2012 and 2020 from two large tertiary academic and six community hospitals were identified. The primary outcomes were the final femoral and tibial component sizes extracted from automated inventory systems. Five ML algorithms were trained with routinely corrected demographic variables (age, height, weight, body mass index, and sex) using 80% of the study population and internally validated on an independent set of the remaining 20% of patients. Algorithm performance was evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE).

RESULTS

A total of 17,283 patients that received one of 9 TKA implants from independent manufacturers were included. The SGB model accuracy for predicting ± 4-mm of the true femoral anteroposterior diameter was 83.6% and for ± 1 size of the true femoral component size was 95.0%. The SGB model accuracy for predicting ± 4-mm of the true tibial medial/lateral diameter was 83.0% and for ± 1 size of the true tibial component size was 97.8%. Patient sex was the most influential feature in terms of informing the SGB model predictions for both femoral and tibial component sizing. A TKA implant sizing application was subsequently created.

CONCLUSION

Novel machine learning algorithms demonstrated good to excellent performance for predicting TKA component size. Patient sex appears to contribute an important role in predicting TKA size. A web-based real-time prediction application was created capable of integrating patient specific data to predict TKA size, which will require external validation prior to clinical use.