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

The accuracy of preoperative implant size prediction achieved by digital templating in total knee arthroplasty is not affected by the quality of lateral knee radiographs

Background

Digital templating software can be used for preoperative implant size prediction in total knee arthroplasty (TKA). However, the accuracy of its prediction is reported to be low, and the impact of radiograph quality is unclear.

Purpose

To investigate on the application of lateral knee radiograph quality criteria for knee rotation (KR) and knee abduction/adduction (KA) and their impact on the accuracy of final implant size prediction achieved by preoperative digital templating for TKA.

Methods

A total of 191 radiographs of patients undergoing TKA were allocated into four groups according to their KR as measured at the posterior femoral condyles and their KA as measured at the distal femoral condyles on lateral knee radiographs: group A (KR ≤ 5 mm, KA ≤ 5 mm), B1 (KR > 5 mm, KA ≤ 5 mm), B2 (KR ≤ 5 mm, KA > 5 mm) and B3 (KR > 5 mm, KA > 5 mm). Preoperative templating of femoral and tibial implant size using digital templating software was carried out by two observers. Correlation coefficients (CCs) between planned and final implant size, percentage of cases with planned to final size match as well as percentage of cases within ±1 and ±2 of planned to final size were reported according to groups.

Results

Group A showed the highest percentage of cases with matching planned to final femoral implant size (45%) and the highest percentage of cases with ±1 planned to final implant size (86%) as compared to B1 (match 28%, ±1 84%), B2 (match 41%, ±1 84%) and B3 (match 35%, ±1 78%). CCs for planned to final implant size were reported at >0.75 in all groups. No statistically significant difference in the CCs of planned to final implant size amongst groups was found.

Conclusion

The accuracy of implant size prediction achieved by preoperative digital templating for TKA is neither affected by KR nor KA on lateral knee radiographs.

Level of evidence

Level III.

Predictability of implant sizes during cruciate-retaining total knee arthroplasty using an image-free hand-held robotic system

The use of appropriately sized implants is critical for achieving optimal gap balance following total knee arthroplasty (TKA). Inappropriately sized implants could result in several complications. Robot-assisted TKA (RA-TKA) using CT-based pre-operative planning predicts implant sizes with high accuracy. There is scant literature describing the accuracy of image-free RA-TKA in predicting implant sizes. The purpose of this study was to assess the accuracy of an image-free robotic system in predicting implant sizes during RA-TKA. Patients who underwent cruciate-retaining RA-TKA for primary osteoarthritis, using an image-free hand-held robotic system were studied. The predicted and implanted sizes of the femoral component, tibial component and polyethylene insert, for 165 patients, were recorded. Agreement between robot-predicted and implanted component sizes was assessed in percentages, while reliability was assessed using Cohen's weighted kappa coefficient. The accuracy of the robotic system was 63% (weighted-kappa = 0.623, P < 0.001), 94% (weighted-kappa = 0.911, P < 0.001) and 99.4% (weighted-kappa = 0.995, P < 0.001), in predicting exact, ± 1 and ± 2 sizes of the femoral component, respectively. For the tibial component, an accuracy of 15.8% (weighted-kappa = 0.207, P < 0.001), 55.8% (weighted-kappa = 0.378, P < 0.001) and 76.4% (weighted-kappa = 0.568, P < 0.001) was noted, for predicting exact, ± 1 and ± 2 sizes respectively. An accuracy of 88.5%, 98.2% and 100%, was noted for predicting exact, ± 1 and ± 2 sizes of the polyethylene insert respectively. Errors in predicting accurate implant sizes could be multi-factorial. Though the accuracy of image-free RA-TKA with respect to alignment and component positioning is established, the surgeon's expertise should be relied upon while deciding appropriate implant sizes.

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 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.

Is There a Scope for an Open Source Philosophy in Robotic Joint Replacement?: Why and How to Make Robotic Platforms Common and Universal for All Implants !

Background

Standardisation and open source technologies has transformed our world by levelling the field for innovation and improvisation. In the field of arthroplasty, we are seeing robotic technology make giant strides in terms of wide spread adoption across geographies. The benefits of consistency, reduced intra-surgeon and inter-surgeon variability as well as decreased dependence on complex instrumentation sets and large implant inventories, is a step in the right direction. However they suffer from a very significant drawback; today's robotic systems are essentially closed systems and do not offer cross platform and cross implant compatibility.

Materials and Methods

This point of view dwells on why it is important that robotics become open source and how this can be achieved. A universal system of implant sizing and nomenclature is proposed. This may enable the use of Robotic platform across various commercially available implants seamlessly.

Results

As of today, scientific literature and also the marketing literature provides no verifiable rationale for use of varied implant sizing terminology. The proposed universal implant sizing and nomenclature can be based on the Anteroposterior and mediolateral size data obtained from various anthropometric studies down across varied races.

Conclusion

TBy building a consensus on the universal implant sizing nomenclature, the field of arthroplasty will achieve a major milestone. It will have benefits including easier documentation, storage and transmission of data. Most importantly, it will be the right step in direction of making the Robotic Technology - open source and thus making it available, accessible and affordable to all.

CAN SHOE SIZE CORRECTLY PREDICT THE SIZE OF COMPONENTS OF TOTAL KNEE REPLACEMENT PRE-OPERATIVELY

Objective: To ascertain the correlation between shoe size and sizes of femoral and tibial components of total knee replacement preoperatively in patients undergoing total knee arthroplasty. Study design: Prospective cohort study Study settings and duration: This study was conducted at department of orthopedic surgery, Shifa International Hospital, Islamabad from July 2020 – December 2020. Material and methods: Sample size was calculated using WHO calculator and it was 43 patients in total. Patients were approached through non-probability consecutive sampling. Shoe size of patients was measured using a Brannock device. During surgery, Implant model and sizes of the femoral and tibial components implanted during knee replacement were noted. Data was analyzed with the help of SPSS version 24. We applied Pearson’s correlation cofficeint. P value ≤ 0.05 was considered significant. Results: Out of 43, there were 9(20.9%) male and female 34(79.1%). Mean age of patients was 51.7±6.8 (SD). We found good positive correlation between shoe size and tibial component (p=<0.001). Positive co relation was found between femoral component and shoe size (p=0.001). Shoe size predict 72% of Tibial component and 65% femoral component. Conclusion: Shoe size is effective and safe predictors of total knee replacement components pre-operatively. This procedure is more accurate and less labor intensive. Accurate templating result in less surgical duration and provide several benefits to patients and health care providers.    

A computational tool for automatic selection of total knee replacement implant size using X-ray images

Purpose: The aim of this study was to outline a fully automatic tool capable of reliably predicting the most suitable total knee replacement implant sizes for patients, using bi-planar X-ray images. By eliminating the need for manual templating or guiding software tools via the adoption of convolutional neural networks, time and resource requirements for pre-operative assessment and surgery could be reduced, the risk of human error minimized, and patients could see improved outcomes.

Methods: The tool utilizes a machine learning-based 2D—3D pipeline to generate accurate predictions of subjects’ distal femur and proximal tibia bones from X-ray images. It then virtually fits different implant models and sizes to the 3D predictions, calculates the implant to bone root-mean-squared error and maximum over/under hang for each, and advises the best option for the patient. The tool was tested on 78, predominantly White subjects (45 female/33 male), using generic femur component and tibia plate designs scaled to sizes obtained for five commercially available products. The predictions were then compared to the ground truth best options, determined using subjects’ MRI data.

Results: The tool achieved average femur component size prediction accuracies across the five implant models of 77.95% in terms of global fit (root-mean-squared error), and 71.79% for minimizing over/underhang. These increased to 99.74% and 99.49% with ±1 size permitted. For tibia plates, the average prediction accuracies were 80.51% and 72.82% respectively. These increased to 99.74% and 98.98% for ±1 size. Better prediction accuracies were obtained for implant models with fewer size options, however such models more frequently resulted in a poor fit.

Conclusion: A fully automatic tool was developed and found to enable higher prediction accuracies than generally reported for manual templating techniques, as well as similar computational methods.

Machine learning algorithms predict within one size of the final implant ultimately used in total knee arthroplasty with good-to-excellent accuracy

PURPOSE

To develop a novel machine learning algorithm capable of predicting TKA implant sizes using a large, multicenter database.

METHODS

A consecutive series of primary TKA patients from two independent large academic and three community medical centers between 2012 and 2020 was identified. The primary outcomes were final tibial and femoral implant sizes obtained from an automated inventory system. Five machine learning algorithms were trained using six routinely collected preoperative features (age, sex, height, weight, and body mass index). Algorithms were validated on an independent set of patients and evaluated through accuracy, mean absolute error (MAE), and root mean-squared error (RMSE).

RESULTS

A total of 11,777 patients were included. The support vector machine (SVM) algorithm had the best performance for femoral component size(MAE = 0.73, RMSE = 1.06) with accuracies of 42.2%, 88.3%, and 97.6% for predicting exact size, ± one size, and ± two sizes, respectively. The elastic-net penalized linear regression (ENPLR) algorithm had the best performance for tibial component size (MAE 0.70, RMSE = 1.03) with accuracies of 43.8%, 90.0%, and 97.7% for predicting exact size, ± one size, and ± two sizes, respectively.

CONCLUSION

Machine learning algorithms demonstrated good-to-excellent accuracy for predicting within one size of the final tibial and femoral components used for TKA. Patient height and sex were the most important factors for predicting femoral and tibial component size, respectively. External validation of these algorithms is imperative prior to use in clinical settings.

LEVEL OF EVIDENCE

Case-control, III.

Preoperative radiographic parameters in the case of using a narrow-version femoral implant in total knee arthroplasty

BACKGROUND

Recently, total knee arthroplasty (TKA) designs that allow the use of narrow-version femoral implants have been introduced to avoid femoral overhang. The purpose of this study was to investigate the frequency of the use of narrow-version femoral implants and identify the difference in radiographic parameters between using a narrow-version femoral implant and a standard-version femoral implant in TKA.

METHODS

A retrospective study was conducted on 504 primary TKAs using a TKA system (Anthem or Persona) that allowed narrow-version femoral implants. Anteroposterior (AP) dimension, mediolateral (ML) dimension, and modified aspect percentage ratio (ML/AP dimension) of the distal femur in preoperative radiographs were compared between a standard-version group (n = 275) and a narrow-version group (n = 229). A cut-off value of a modified aspect percentage ratio indicating the need for a narrow-version femoral implant was determined using the receiver operating characteristic (ROC) curve.

RESULTS

Mean ML dimension was 80.9 ± 6.1 mm in the standard-version group and 77.3 ± 4.4 mm in the narrow-version group (p < 0.001). Mean modified aspect percentage ratio was 138.8 ± 8.1% in the standard-version group and 131.7 ± 6.3% in the narrow-version group (p < 0.001). The optimum cut-off point of the modified aspect percentage ratio for narrow-version femoral implants was 135.4% (sensitivity: 72.0%; specificity: 66.7%) for Anthem and 133.3% (sensitivity: 75.9%, specificity: 76.4%) for Persona.

CONCLUSION

In the narrow-version femoral implant group, the ML dimension and the mean modified aspect percentage ratio were smaller than in the standard-version femoral implant group. A smaller modified aspect percentage ratio of the distal femur in preoperative radiographs could predict the need for narrow-version femoral implants in TKA. It was suggested that the cut-off point could be suggested as 135.4% for Anthem TKA design and 133.3% for Persona TKA design. These radiographic parameters are cost-effective and easily applicable for planning a TKA.A smaller modified aspect percentage ratio of the distal femur in preoperative radiographs could predict the need for narrow-version femoral implants in TKA. The cut-off point was 135.4% for Anthem TKA design and 133.3% for Persona TKA design.

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.

Artificial intelligence in knee arthroplasty: current concept of the available clinical applications

Background

Artificial intelligence (AI) is defined as the study of algorithms that allow machines to reason and perform cognitive functions such as problem-solving, objects, images, word recognition, and decision-making. This study aimed to review the published articles and the comprehensive clinical relevance of AI-based tools used before, during, and after knee arthroplasty.

Methods

The search was conducted through PubMed, EMBASE, and MEDLINE databases from 2000 to 2021 using the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA).

Results

A total of 731 potential articles were reviewed, and 132 were included based on the inclusion criteria and exclusion criteria. Some steps of the knee arthroplasty procedure were assisted and improved by using AI-based tools. Before surgery, machine learning was used to aid surgeons in optimizing decision-making. During surgery, the robotic-assisted systems improved the accuracy of knee alignment, implant positioning, and ligamentous balance. After surgery, remote patient monitoring platforms helped to capture patients’ functional data.

Conclusion

In knee arthroplasty, the AI-based tools improve the decision-making process, surgical planning, accuracy, and repeatability of surgical procedures.

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.

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.

Lack of small tibial component size availability for females in a highly utilized total knee arthroplasty system

PURPOSE

Surgeons must rely on manufacturers to provide an appropriate distribution of total knee arthroplasty (TKA) sizes. There is a lack of literature regarding current appropriateness of tibial sizing schemes according to sex. As such, a study was devised assessing the adequacy of off-the-shelf tibial component size availability according to sex.

METHODS

A search was conducted to identify all primary TKAs between July 2012 and June 2019 performed using a single implant. Baseline patient characteristics were collected (age, weight, height, BMI, and race). Two cohorts were created according to patient sex. Tibial sizes for each cohort were collected. Tibial component bar graph and histogram were created according to component sizes. Skewness and kurtosis were calculated for each distribution. Overhang was noted and measured radiographically.

RESULTS

A total of 864 patients were identified, 38.7% males and 61.3% females. Most patients were Caucasian, and BMI was similar between cohorts. Tibial size distribution for males was as follows: 0.3% C, 4.8% D, 16.5% E, 40.1% F, 31.4% G, 6.9% H. Tibial size distribution for females was as follows: 30.8% C, 42.8% D, 23.0% E, 2.6% F, 0.8% G, 0.0% H. Histograms and normal curves demonstrated a fairly symmetric distribution of sizes for males (skewness = - 0.31, kurtosis = - 0.03). The distribution for females was positively skewed (skewness = 0.57, kurtosis = 0.12). Overall, overhang was noted in 16.6% of all size C tibias.

CONCLUSIONS

The results of this study highlight an implant-specific discrepancy in size availability affecting female patients which could result in inferior outcomes. The authors urge manufacturers to critically assess current implant size distribution availability to ensure both genders are adequately, and equally represented.

LEVEL OF EVIDENCE

IV.

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.

Compilation and Analysis of Web-Based Orthopedic Personalized Predictive Tools: A Scoping Review

Web-based personalized predictive tools in orthopedic surgery are becoming more widely available. Despite rising numbers of these tools, many orthopedic surgeons may not know what tools are available, how these tools were developed, and how they can be utilized. The aim of this scoping review is to compile and synthesize the profile of existing web-based orthopedic tools. We conducted two separate PubMed searches-one a broad search and the second a more targeted one involving high impact journals-with the aim of comprehensively identifying all existing tools. These articles were then screened for functional tool URLs, methods regarding the tool's creation, and general inputs and outputs required for the tool to function. We identified 57 articles, which yielded 31 unique web-based tools. These tools involved various orthopedic conditions (e.g., fractures, osteoarthritis, musculoskeletal neoplasias); interventions (e.g., fracture fixation, total joint arthroplasty); outcomes (e.g., mortality, clinical outcomes). This scoping review highlights the availability and utility of a vast array of web-based personalized predictive tools for orthopedic surgeons. Increased awareness and access to these tools may allow for better decision support, surgical planning, post-operative expectation management, and improved shared decision-making.

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.

Predicting implant size in total knee arthroplasty using demographic variables

AIMS

The purpose of this investigation was to determine the relationship between height, weight, and sex with implant size in total knee arthroplasty (TKA) using a multivariate linear regression model and a Bayesian model.

METHODS

A retrospective review of an institutional registry was performed of primary TKAs performed between January 2005 and December 2016. Patient demographics including patient age, sex, height, weight, and body mass index (BMI) were obtained from registry and medical record review. In total, 8,100 primary TKAs were included. The mean age was 67.3 years (SD 9.5) with a mean BMI of 30.4 kg/m² (SD 6.3). The TKAs were randomly split into a training cohort (n = 4,022) and a testing cohort (n = 4,078). A multivariate linear regression model was created on the training cohort and then applied to the testing cohort . A Bayesian model was created based on the frequencies of implant sizes in the training cohort. The model was then applied to the testing cohort to determine the accuracy of the model at 1%, 5%, and 10% tolerance of inaccuracy.

RESULTS

Height had a relatively strong correlation with implant size (femoral component anteroposterior (AP) Pearson correlation coefficient (ρ) = 0.73, p < 0.001; tibial component mediolateral (ML) ρ = 0.77, p < 0.001). Weight had a moderately strong correlation with implant size, (femoral component AP ρ = 0.46, p < 0.001; tibial ML ρ = 0.48, p < 0.001). There was a significant linear correlation with height, weight, and sex with implant size (femoral component R² = 0.607, p < 0.001; tibial R² = 0.695, p < 0.001). The Bayesian model showed high accuracy in predicting the range of required implant sizes (94.4% for the femur and 96.6% for the tibia) accepting a 5% risk of inaccuracy.

CONCLUSION

Implant size was correlated with basic demographic variables including height, weight, and sex. The linear regression and Bayesian models accurately predicted required implant sizes across multiple manufacturers based on height, weight, and sex alone. These types of predictive models may help improve operating room and implant supply chain efficiency. Level of Evidence: Level IV Cite this article: Bone Joint J 2020;102-B(6 Supple A):85-90.

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.

Agreement in component size between preoperative measurement, navigation and final implant in total knee replacement

Background

One of the possible causes of dissatisfaction reported by many patients after total knee replacement (TKR) is the lack of agreement between component size and bone structure. To avoid this complication and facilitate the procedure, preoperative planning with digitized templates is recommended. Surgical navigation indicates the best position and the most adequate size of arthroplasty and may therefore replace preoperative radiographic measurement. The objective of the study was to check agreement between the sizes of TKR components measured before surgery with digitized templates, the size recommended by the navigation and sizes actually implanted.

Methods

In 103 patients scheduled for TKR, preoperative full-limb radiography was performed to measure the mechanical and anatomical axes of the limb, femur and tibia. The most adequate size of the femoral and tibial components was planned by superimposing digitized templates. The size recommended in navigation and the size of the finally implanted components were also recorded.

Results

A high level of agreement was found between the sizes of femoral and tibial components measured by X-rays and in navigation (0.750 and 0.772, respectively) (intraclass correlation and Cronbach's alpha). Agreement between the sizes recommended by X-rays and navigation and those finally implanted was 0.886 for the femur and 0.891 for the tibia. Agreement levels were not different in cases with prior deformities of limb axis.

Conclusions

The high level of agreement found in component sizes between radiographic measurement with digitized templates and navigation suggests that preoperative X-ray measurement is not needed when navigation is used for placement of implants during TKR.

The translational potential of this article

Computer-assisted surgery may avoid preoperative measurement with templates in TKR.

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.