Predicting implant size in total knee arthroplasty using demographic variables

Global mapping of institutional and hospital-based (Level II-IV) arthroplasty registries: a scoping review

PURPOSE

Four joint arthroplasty registries (JARs) levels exist based on the recorded data type. Level I JARs are national registries that record primary data. Hospital or institutional JARs (Level II-IV) document further data (patient-reported outcomes, demographic, radiographic). A worldwide list of Level II-IV JARs must be created to effectively assess and categorize these data.

METHODS

Our study is a systematic scoping review that followed the PRISMA guidelines and included 648 studies. Based on their publications, the study aimed to map the existing Level II-IV JARs worldwide. The secondary aim was to record their lifetime, publications' number and frequency and recognise differences with national JARs.

RESULTS

One hundred five Level II-IV JARs were identified. Forty-eight hospital-based, 45 institutional, and 12 regional JARs. Fifty JARs were found in America, 39 in Europe, nine in Asia, six in Oceania and one in Africa. They have published 485 cohorts, 91 case-series, 49 case-control, nine cross-sectional studies, eight registry protocols and six randomized trials. Most cohort studies were retrospective. Twenty-three per cent of papers studied patient-reported outcomes, 21.45% surgical complications, 13.73% postoperative clinical and 5.25% radiographic outcomes, and 11.88% were survival analyses. Forty-four JARs have published only one paper. Level I JARs primarily publish implant revision risk annual reports, while Level IV JARs collect comprehensive data to conduct retrospective cohort studies.

CONCLUSIONS

This is the first study mapping all Level II-IV JARs worldwide. Most JARs are found in Europe and America, reporting on retrospective cohorts, but only a few report on studies systematically.

Development and validation of multiple linear regression models for predicting total hip arthroplasty acetabular prosthesis

PURPOSE

To establish a multivariate linear equation to predict the diameter (outer diameter) of the acetabular prosthesis used in total hip arthroplasty.

METHODS

A cohort of 258 individuals who underwent THA at our medical facility were included in this study. The independent variables encompassed the patients' height, weight, foot length, gender, age, and surgical access. The dependent variable in this study was the diameter of the acetabular prosthesis utilized during the surgical procedure. The entire cohort dataset was randomly partitioned into a training cohort and a validation cohort, with a ratio of 7:3, employing the SPSS 26.0 software. Pearson correlation analysis was conducted to examine the relationships between the patients' height, weight, foot length, gender, age, surgical access, and the diameter of the acetabular prosthesis in the training cohort. Additionally, a multiple linear regression equation was developed using the independent variables from the training cohort and the diameter of the acetabular prosthesis as the dependent variable. This equation aimed to predict the diameter of the acetabular prosthesis based on the patients' characteristics. The accuracy of the equation was evaluated by substituting the data of the validation cohort into the multiple linear equation. The predicted acetabular prosthesis diameters were then compared with the actual diameters used in the operation.

RESULTS

The correlation analysis conducted on the training cohort revealed that surgical access (r = 0.054) and age (r = -0.120) exhibited no significant correlation with the diameter of the acetabular prosthesis utilized during the intraoperative procedure. Conversely, height (r = 0.687), weight (r = 0.654), foot length (r = 0.687), and sex (r = 0.354) demonstrated a significant correlation with the diameter of the acetabular prosthesis used intraoperatively. Furthermore, a predictive equation, denoted as Y (acetabular prosthesis diameter in mm) = 20.592 + 0.548 × foot length (cm) + 0.083 × height (cm) + 0.077 × weight (kg), was derived. This equation accurately predicted the diameter within one size with an accuracy rate of 64.94% and within two sizes with an accuracy rate of 94.81%.

CONCLUSION

Anthropometric data can accurately predict the diameter of acetabular prosthesis during total hip arthroplasty.

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.

Overall Accuracy of Radiological Digital Planning for Total Hip Arthroplasty in a Specialized Orthopaedics Hospital

Preoperative radiological planning is a key factor in the prediction of implant size and positioning that influences surgical time, the risk of complications, and functional outcomes. We have tested the accuracy of the digital templating performed in our hospital for a sample of 215 patients that underwent total hip arthroplasty. We assessed the accuracy of correctly predicting implant size for the femoral and acetabular components, as well as the stem neck length. We found that our method of templating proved accurate (within one size) in 95.8% of cases for the stem and 94.9% for the cup when using the anteroposterior view only, while the lateral view was accurate in 95.8% of cases for the stem and 97.2% for the cup. Exact prediction of the stem size was obtained in 77.7% of cases using the anteroposterior view and 67.0% of cases on the lateral view, and 73.0% and 74.4% of cases for the cup on the AP and LL views, respectively. Stem neck size was predicted exactly in 75.35% of cases and within one size in 93.49% of cases. We concluded that our method of digital templating using dedicated software is highly effective in accurately predicting implant size.

Time for return to sport following total knee arthroplasty: a meta-analysis

INTRODUCTION

The frequency of total knee arthroplasty (TKA) is increasing, particularly in younger and more active patients. In these patients, there may be greater functional demands, with an expectation to return to sporting activities (RTS) following TKA. There is a paucity of data on the time to RTS following TKA and the aim of this meta-analysis is to determine the time to RTS following TKA.

METHODS

Using the PRISMA guidelines, an electronic search of PUBMED, MEDLINE, EMBASE, and the Cochrane Library for trails was performed on TKA and RTS in English language, published since the inception of the database to 31st October 2020. Data evaluating the time to RTS and functional outcomes were recorded by two authors independently that were included in the analysis. Pooled analysis using random effect model on overall proportions at the different time intervals and at the end of the follow-up was carried out for all studies.

RESULTS

In total, 1,611 studies were retrieved from literature search. Of these, nine studies met the inclusion criteria with 1,307 patients. Two studies with 148 patients demonstrated an overall pooled proportion of 18.7% (95% CI 8.2-32.3%) of patients RTS at 3 month post-TKA; Three studies reported RTS rate at 6 months 70.% (95% CI 48-88.4). Two studies with 123 patients demonstrated an overall pooled proportion of 84.0% (95% CI 77.1-89.9%) patients RTS at 12 months. 986 patients returned to sport from total of 1307, with an overall adjusted proportion return to sport of 87.9 (95% CI 80.5-93.8%) at the end of follow-up; mean 14 months (range 3-36 months).

CONCLUSION

Patients undergoing TKA were found to successfully RTS, pooled proportion analysis showed an increasing rate of RTS with time, at a mean of 14 months following TKA, where 87.9% of patients had returned to sports. The findings of this study will enable more informed discussions and rehabilitation planning between patients and clinicians on RTS following TKA.

Predicting Implant Size in Total Hip Arthroplasty

Background

Efficient resource management is becoming more important as the demand for total hip arthroplasty (THA) increases. The purpose of this study is to evaluate the ability of linear regression and Bayesian statistics in predicting implant size for THA using patient demographic variables.

Material and methods

A retrospective, single-institution joint-replacement registry review was performed on patients who underwent primary THA from 2005 to 2019. Demographic information was obtained along with primary THA implant data. A total of 11,730 acetabular and 8536 femoral components were included. A multivariable regression model was created on a training cohort of 80% of the sample and applied to the validation cohort (remaining 20%). Bayesian posterior probability methods were applied to the training cohort and then tested in the validation cohort to determine the 1%, 5%, and 10% error tolerance thresholds.

Results

The most predictive regression model included height, weight, and sex (cup: R² = 0.57, all P < .001; stem mediolateral size [M/L]: R² = 0.32, all P < .001). Removing weight had a minimal effect and resulted in a more parsimonious model (cup: R² = 0.56, all P < .001; stem M/L: R² = 0.32, all P < .001). Applying the posterior probability estimate to the validation cohort in the Bayesian model using height, weight, and sex demonstrated high accuracy in predicting the range of required implant sizes (95.3% cup and 90.4% stem M/L size).

Conclusion

Implant size in THA is correlated with demographic variables to accurately predict implant size using Bayesian modeling. Predictive models such as linear regression and Bayesian modeling can be used to improve operating room efficiency, supply chain inventory management, and decrease costs associated with THA.