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Demographic Data Reliably Predicts Total Hip Arthroplasty Component Size

Published:January 27, 2022DOI:https://doi.org/10.1016/j.arth.2022.01.051

      Abstract

      Background

      Preoperative radiographic templating for total hip arthroplasty (THA) has been shown to be inaccurate, although essential for streamlining operating room efficiency. Although demographic data have shown to predict total knee arthroplasty component sizes, the unique contour and design among femoral stem implants have limited a similar application for hip arthroplasty. The purpose of this study was to determine whether demographic data may predict cementless THA size independent of the stem design.

      Methods

      A consecutive series of 1,653 index cementless metaphyseal-fitting THAs were reviewed between 2007 and 2019. This included 12 unique femoral component designs, 6 acetabular component designs, 60 femur size-design combinations, and 23 acetabular size-design combinations. Implanted component sizes and patient demographic data were collected, including gender, height, weight, laterality, age, race, and ethnicity. Multivariate linear regressions were formulated to predict implanted femur and acetabular component sizes from the demographic data.

      Results

      There was a significant linear correlation between gender, implant model, age, height, and weight for femur (R2 = 0.778; P < .001) and acetabular (R2 = 0.491; P < .001) sizes. Calculated femur and acetabular component sizes averaged within 0.97 and 0.95 sizes of those implants, respectively. Femur and acetabular sizes were predicted within 1 size 79.1% and 78.2% and within 2 sizes 94.3% and 94.6% of the time, respectively.

      Conclusions

      Multivariate regression models were created based on specific demographics data to predict femur and acetabular component sizes. The model allows for simplified preoperative planning and potential cost savings implementation. A free phone application named EasyTJA was constructed for ease of implementation.

      Keywords

      Accurate templating in total hip arthroplasty (THA) is important for preoperative planning and streamlining operative room efficiency [
      • Kniesel B.
      • Konstantinidis L.
      • Hirschmuller A.
      • Sudkamp N.
      • Kelwig P.
      Digital templating in total knee and hip replacement: an analysis of planning accuracy.
      ]. Appropriate preoperative planning minimizes complications, may allow for cost savings, and helps the surgeon accurately recreate leg length and offset [
      • Zhao X.
      • Zhu Z.A.
      • Zhao J.
      • Li M.Q.
      • Wang G.
      • Yu D.G.
      • et al.
      The utility of digital templating in total hip arthroplasty with crowe type II and III dysplastic hips.
      ,
      • Bozic K.J.
      • Kamath A.F.
      • Ong K.
      • Lau E.
      • Kurtz S.
      • Chan V.
      • et al.
      Comparative epidemiology of revision arthroplasty: failed THA poses greater clinical and economic burdens than failed TKA.
      ]. In an era of value-based health care and steadily decreasing reimbursement, delivery of effective patient care in an accurate, timely, and affordable manner will prove paramount [
      • Mayfield C.K.
      • Haglin J.M.
      • Levine B.
      • Della Valle C.
      • Lieberman J.R.
      • Heckmann N.
      Medicare reimbursement for hip and knee arthroplasty from 2000 to 2019: an Unsustainable Trend.
      ,
      • Keswani A.
      • Koenig K.M.
      • Bozic K.J.
      Value-based healthcare: Part 1-designing and implementing integrated practice units for the management of musculoskeletal disease.
      ,
      • Keswani A.
      • Koenig K.M.
      • Ward L.
      • Bozic K.J.
      Value-based healthcare: Part 2-addressing the obstacles to implementing integrated practive units for the management of musculoskeletal disease.
      ].
      Traditional templating methods have demonstrated variable accuracy, with prediction to within one size ranging from 44% to 93% [
      • Kniesel B.
      • Konstantinidis L.
      • Hirschmuller A.
      • Sudkamp N.
      • Kelwig P.
      Digital templating in total knee and hip replacement: an analysis of planning accuracy.
      ,
      • The B.
      • Dierckes R.L.
      • van Ooijen P.M.A.
      • van Horn J.R.
      Comparison of analog and digital preoperative planning in total hip and knee arthroplasties.
      ,
      • Levine B.
      • Fabi D.
      • Deirmengian C.
      Digital templating in primary total hip and knee arthroplasty.
      ]. Additional studies have reported increased difficulty templating for THA when compared with total knee arthroplasty (TKA), demonstrating the need for more reliable and time-efficient methods [
      • Iorio R.
      • Siegel J.
      • Specht L.M.
      • Tilzey J.F.
      • Hartman A.
      • Healy W.L.
      A comparison of acetate vs digital templating for preoperative planning of total hip arthroplasty: is digital templating accurate and safe?.
      ,
      • Kosashvili Y.
      • Shasha N.
      • Olschewski E.
      • Safir O.
      • White L.
      • Gross A.
      • et al.
      Digital versus conventional templating techniques in preoperative planning for total hip arthroplasty.
      ].
      Several studies have explored the accuracy of templating TKA component sizes from demographic data [
      • Murphy M.P.
      • Wallace S.J.
      • Brown N.M.
      Prospective comparison of available primary total knee arthroplasty sizing equations.
      ,
      • Sershon R.A.
      • Li J.
      • Calkins T.E.
      • Courtney P.M.
      • Nam D.
      • Gerlinger T.L.
      • et al.
      Prospective validation of a demographically based primary total knee arthroplasty size calculator.
      ,
      • Ren A.N.
      • Neher R.E.
      • Bell T.
      • Grimm J.
      Using patient demographics and statistical modeling to predict knee tibia component sizing in total knee arthroplasty.
      ]. To the knowledge of the authors, the same has not been shown for THA components. The unique contour and design differences between THA implants contribute to the difficulty in predicting sizes. Notably, TKA implants allow model sizes to be converted between the proprietary design and its corresponding anterior-posterior and medial-lateral dimensions (eg, Size 1 may correlate to 53 mm in the anterior-posterior direction and 59 mm in the medial-lateral direction). THA implants lack this standard measure among manufacturers and thus limit the ability to identify a singular equation that may be applied between designs. Thus, predictive models are forced to be specific for a given implant rather than allowing for a general equation that may be applied more broadly.
      The purpose of this study was to determine whether femur and acetabular component sizes for index THA can be predicted based on the patient’s demographic data alone. Second, this study attempts to identify a common measure among THA femoral stem designs, allowing proprietary sizing to be compared between implant designs. Finally, this study aims to provide a generalized predictive equation that may calculate femoral stem and acetabular cup THA sizes using demographic data alone and may be applied to the implant of the user’s choosing.

      Materials and Methods

      After approval from the institutional review board, a consecutive series of 1,653 primary THAs (1,653 patients) from a single institution were identified between January 1, 2007, and December 31, 2019. In cases of patients receiving THA bilaterally within the study period, only the first operation was included to avoid duplicate input from the same patient. Cases involving revision THA were excluded. Demographic information obtained included gender, height, weight, body mass index, ethnicity, race, and laterality. Implant data collected included manufacturer, design of the prosthesis, and component size.
      This study involved 12 metaphyseal-fitting cementless press-fit implants: Accolade TMZF (Stryker Corp, Kalamazoo, MI), Accolade II (Stryker Corp), Summit (DePuy Orthopaedics, Warsaw, IN), Corail standard offset (DePuy Orthopaedics), Corail high offset collarless (DePuy Orthopaedics), Corail coxa vara collared (DePuy Orthopaedics), Tri-Lock BPS (DePuy Orthopaedics), Actis (DePuy Orthopaedics), AML (DePuy Orthopaedics), Secur-Fit (Stryker Corp), Omnifit-HA (Stryker Corp), and the M/L Taper Hip Prosthesis (Zimmer Biomet, Inc, Warsaw, IN). The study involved 6 acetabular component models: Trident Tritanium (Stryker Corp), Trident II Tritanium (Stryker Corp), Pinnacle Porocoat (DePuy Orthopaedics), Pinnacle Gription (DePuy Orthopaedics), Continuum (Zimmer Biomet, Inc), and Trilogy (Zimmer Biomet, Inc). Among these implants, there were 60 femur size-design combinations and 23 acetabular size-design combinations in the study series.

      Statistical Analysis

      An a priori power analysis was first performed to determine the appropriate sample size for this study. To generate a multivariate general linear model to detect a small effect size (f2 = 0.01) in component sizes, a power analysis with power of 0.80 and type I error 0.05 revealed the minimum number of patients needed for this study would be 787 patients.
      A multivariate linear regression was modeled to determine the correlation between patient demographics and component sizes. Using a backward selection procedure, variables with P values >0.10 were removed to improve model parsimony. This study compares the predicted size to that implanted and deemed to be accurate if within 1 size (one size greater or less) of that implanted. As an example, a predicted cup size of 54 mm would be within 1 size of an implanted 56 mm cup, with the same being true for a predicted 58 mm cup. Wilcoxon signed-rank test was used to compare the regression models in their ability to predict true intraoperative implanted femur and acetabular component size. Statistical analysis was set at P < .05 and performed with IBM SPSS, version 26 (Armonk, NY).

      Generalized Sizing Data

      The unique design and proprietary nature of THA femoral stem sizing required a novel approach to compare implant models. This is a critical step both in interpreting the data among implants and applying the results more broadly to implants outside those used in this study. Similar to methods successfully implemented for TKA predictive model [
      • Murphy M.P.
      • Wallace S.J.
      • Brown N.M.
      Prospective comparison of available primary total knee arthroplasty sizing equations.
      ,
      • Sershon R.A.
      • Li J.
      • Calkins T.E.
      • Courtney P.M.
      • Nam D.
      • Gerlinger T.L.
      • et al.
      Prospective validation of a demographically based primary total knee arthroplasty size calculator.
      ,
      • Ren A.N.
      • Neher R.E.
      • Bell T.
      • Grimm J.
      Using patient demographics and statistical modeling to predict knee tibia component sizing in total knee arthroplasty.
      ], this study first attempts to identify a unit of measure the regression model may calculate. As performed in these studies of reference, the subsequent step would involve converting this unit of measure to the corresponding proprietary size specific to the implant chosen by the surgeon.
      To identify this universal measure among implants, the authors first measured stem designs at several points: the widest segment of the metaphyseal porous-coated region at the proximal-most aspect, the intersection of the neck and shaft angle, the lower third of the porous coating, and at the distal-most aspect of the porous-coated region in units of millimeters. Finally, a normalized implant size was calculated for each stem model by dividing by the model’s mean size implanted. Any larger or smaller size predicted from this normalized size was adjusted for the standard deviation (SD) of that implant observed in this patient population. A regression model was constructed to predict this normalized implant size.
      To determine if the dimensional measures were an appropriate method for generalizing between implant models, the implant model was included in the multivariate regression model analysis. The dimensional measures were considered appropriate if the multivariate regression model identified no statistically significant relationship between the dimensional size implanted and its stem design (P > .10).

      Results

      A frequency distribution for femoral stem and acetabular cup sizes implanted are shown in Figures 1 and 2, respectively. The mean age in the patient population was 63.4 years (SD 11.6 years), 57.8% were female, the mean height was 66.6 (SD 4.2) inches, the mean weight was 195.9 (SD 50.7) pounds, and 51.5% of hips were performed on the left side.
      Figure thumbnail gr1
      Fig. 1Frequency distribution of implanted femoral sizes among implant models. A normal distribution is shown for reference. The sizes plotted here represent those after being normalized.
      Figure thumbnail gr2
      Fig. 2Frequency distribution of implanted acetabular cup sizes among the patient population with a normal distribution also shown for reference.
      All demographic variables (age, gender, race/ethnicity, height, and weight) were predictive of femoral stem and acetabular cup size implanted, details in Table 1. The femoral stem coefficient of determination (R2) was as follows: for the normalized implant size (0.778), the most proximal aspect of the porous coating dimension (0.272), lower third dimension (0.225), neck-shaft angle dimension (0.241), and diaphyseal dimension (0.197), all P < .001.
      Table 1Multivariate Regression Model Output Including Only Remographic Data.
      General Linear Model Including for Demographic Data OnlyNormalized Stem (R2 = .778, P < .001)Proximal (R2 = .272, P < .001)Lower Third (R2 = .225, P < .001)NSA (R2 = .241, P < .001)Diaphysis (R2 = .197, P < .001)Acetabular Cup (R2 = .491, P < .001)
      Age.013 (<.001).044 (<.001).031 (<.001).041 (<.001).023 (<.001).042 (<.001)
      Height (inches).096 (<.001).258 (<.001).200 (<.001).229 (<.001).177 (<.001).377 (<.001)
      Weight (lbs).001 (.018).003 (.047)(>.10).003 (.050)(>.10).006 (<.001)
      Gender (value true if female)−.307 (<.001)−.804 (.001)−.556 (<.001)−.595 (.001)−.482 (.001)−2.048 (<.001)
      Race/ethnicityAfrican American = −.263 (<.001)African American = −.656 (.046)(>.10)African American = −.578 (.001)(>.10)African American = −.577 (.004)
      Standardized coefficients (beta) and P values for each variable is represented in the rows, while the columns represent the different femoral stem multivariate regression models performed with respective coefficient of determination.
      The regression models predicted within 1 size 79.1%, 73.3%, 69.8%, 68.1%, and 64.7% for the normalized implant size, proximal-most dimension, lower third dimension, neck-shaft angle dimension, and diaphyseal dimension. The regression models predicted within 2 sizes 94.3%, 91.3%, 87.2%, 89.3%, and 91.9% for the normalized implant size, proximal-most dimension, lower third dimension, neck-shaft angle dimension, and diaphyseal dimension. The acetabular cup coefficient of determination was 0.491 (P < .001). The acetabular cup regression model predicted within 1 size 78.2% and within 2 sizes 94.6% of the time (see Table 2 for details).
      Table 2Distribution and Accuracy of the Demographic-Only Femoral Stem and Acetabular Cup Multivariate Regression Models.
      Femoral Stem±0±1±2±3±4±5±6±7±8
      Normalized femoral stem31.2% (515)48.0% (793)16.3% (252)4.8% (74)0.8% (14)0.3% (5)0.0% (0)0.0% (0)0.0% (0)
      Proximal measurement on femoral stem25.7% (425)47.6% (787)17.9% (296)5.3% (88)2.4% (39)0.4% (7)0.5% (9)0.1% (1)0.0% (0)
      Neck-shaft angle measurement on femoral stem24.4% (403)43.7% (723)19.1% (315)7.0% (115)3.7% (61)1.1% (18)0.8% (13)0.2% (3)0.1% (1)
      Lower third measurement on femoral stem23.8% (393)46.0% (761)19.5% (323)7.2% (118)2.2% (37)0.8% (13)0.3% (4)0.3% (4)0.0% (0)
      Diaphyseal measurement on femoral stem21.7% (393)42.9% (710)22.0% (364)8.7% (145)3.1% (51)0.7% (12)0.4% (7)0.3% (4)0.1% (1)
      Acetabular cup33.0% (546)45.2% (747)16.4% (271)4.8% (56)0.4% (4)0.3% (3)0.0% (0)0.0% (0)0.0% (0)
      Columns represent the number of sizes away from the size implanted.
      The regression models averaged within 0.97, 1.14, 1.29, 1.23, and 1.34 sizes from those implanted for the normalized femoral stem implant size, proximal-most dimension, lower third dimension, neck-shaft angle dimension, and diaphyseal dimension, respectively. The regression model for the acetabular cup averaged within 0.95 sizes from that implanted.
      The normalized model and proximal dimension model were statistically superior to all other femoral stem models (all P < .001, except normalized stem vs neck-shaft angle dimension P = .012). The final equations of greatest accuracy are presented in Figure 3. The mean and SDs used to calculate the normalized femur stem size are presented in Table 3.
      Figure thumbnail gr3
      Fig. 3Final equations of each femoral stem and acetabular multivariate regression models.
      Table 3Mean Size and Standard Deviation for Each Femoral Stem Implanted for the Study Population.
      Femoral StemMean SizeStandard Deviation
      Accolade TMZF3.40.9
      Accolade II3.81.5
      Actis5.32.2
      AML15.02.1
      Corail11.81.9
      Omnifit-HA6.50.6
      M/L Taper11.82.5
      Secur-Fit7.91.4
      Summit4.01.5
      Tri-Lock BPS4.11.9

      Discussion

      Demographic variables including age, gender, race/ethnicity, height, and weight were predictive of implanted femur and acetabular component sizes. The results of this study support the use of demographic variables to predict femoral stem and acetabular cup sizes within 1 size of that implanted 79.1% and 78.2% or within 2 sizes 94.3% and 94.6% of the time. The proprietary and unique femoral stem design among implants required a novel approach to compare between implant designs. The femur model of greatest performance was that which predicted a normalized femoral stem implant size. This normalized size was then converted to the implant-specific sizing.
      Radiographs offer essential information for diagnosis and anticipation of surgical needs. Knowledge of a patient’s unique anatomy or pathologic changes can facilitate a streamlined surgery. Despite the widespread use of radiographs for templating and surgical preparation, radiographic templating has shown variable accuracy, ranging 44%-93% accuracy in predicting within 1 size among studies [
      • Kniesel B.
      • Konstantinidis L.
      • Hirschmuller A.
      • Sudkamp N.
      • Kelwig P.
      Digital templating in total knee and hip replacement: an analysis of planning accuracy.
      ,
      • The B.
      • Dierckes R.L.
      • van Ooijen P.M.A.
      • van Horn J.R.
      Comparison of analog and digital preoperative planning in total hip and knee arthroplasties.
      ,
      • Levine B.
      • Fabi D.
      • Deirmengian C.
      Digital templating in primary total hip and knee arthroplasty.
      ]. Meanwhile, the concept of maximizing value in health care has grown with several studies examining modes for more efficient and affordable delivery of patient care [
      • Keswani A.
      • Koenig K.M.
      • Ward L.
      • Bozic K.J.
      Value-based healthcare: Part 2-addressing the obstacles to implementing integrated practive units for the management of musculoskeletal disease.
      ,
      • Levine B.
      • Fabi D.
      • Deirmengian C.
      Digital templating in primary total hip and knee arthroplasty.
      ].
      Prior work has shown demographic data may reliably predict TKA sizing [
      • Murphy M.P.
      • Wallace S.J.
      • Brown N.M.
      Prospective comparison of available primary total knee arthroplasty sizing equations.
      ,
      • Sershon R.A.
      • Li J.
      • Calkins T.E.
      • Courtney P.M.
      • Nam D.
      • Gerlinger T.L.
      • et al.
      Prospective validation of a demographically based primary total knee arthroplasty size calculator.
      ,
      • Ren A.N.
      • Neher R.E.
      • Bell T.
      • Grimm J.
      Using patient demographics and statistical modeling to predict knee tibia component sizing in total knee arthroplasty.
      ]. Applying this approach to THA femoral stems becomes challenging, as femoral stems do not have a global measure to compare sizes between implant designs. Current literature supports the concept that femoral stems obtain cortical fit via 3 points of fixation [
      • Worlicek M.
      • Weber M.
      • Worner M.
      • Schwarz T.
      • Zeman F.
      • Joachim G.
      • et al.
      The final implant position of a commonly used collarless straight tapered stem design (Corail) does not correlate with femoral neck resection height in cement-free total hip arthroplasty: a retrospective computed tomography analysis.
      ,
      • Wada H.
      • Mishima H.
      • Sugaya H.
      • Nishino T.
      • Yamazaki M.
      Three-dimensional analysis of the contact pattern between the cortical bone and femoral prosthesis after cementless total hip arthroplasty.
      ,
      • Riviere C.
      • Grappiolo G.
      • Engh C.A.
      • Vidalain J.P.
      • Chen A.F.
      • Boehler N.
      • et al.
      Long-term bone remodelling around ‘legendary’ cementless femoral stems.
      ], although the definitive location of these points may vary among implants. Thus, this study attempted to measure the femoral stems at several locations to identify a measured reference for comparison of implants: the widest segment of the metaphyseal porous-coated region at the proximal-most aspect, the intersection of the neck and shaft angle, the lower third of the porous coating, and at the distal-most aspect of the porous-coated region in units of millimeters. The results of this study showed a model predicting any of these measurements was not of greatest performance. The lack of performance among these models is likely related to the unique contour among femoral stems. As an example, one femoral stem design may have a greater distal taper and thus rely on more proximal stem measurements when compared with a stem design with less of a taper. Therefore, having a model that may accurately predict a specific region within the femur (eg, the widest segment of the metaphyseal porous-coated region) may not prove accurate for all stem designs. Meanwhile, the normalized model allows the femoral stems to be compared irrespective of where the femoral stem sees most fit in the femur. As a result, the normalized model was of greatest performance.
      The conclusion that the normalized stem design was the best to compare between implants relies on several assumptions. Importantly, this calculation may only be accurate if the distributions of the femoral stem sizes implanted are similar, which may not be true. Although we expect femur dimensions among the patient population to have a normal and nonskewed distribution, this may not correspond to implanted femoral stem sizes. Specifically, it is possible the surgeon may upsize or downsize the femoral stem based on their broaching technique or may adjust the size to affect leg length or offset. This clinical decision-making may also rely on manufacturer-specific choices, such as among what sizes and to what extent femoral stem offset is changed when adjusting size. Thus, the distribution of the implanted femur sizes may differ among femoral stem designs and introduce error to the calculated model. Based on the results of this study showing accuracy of stem designs at 79.1% and 94.3% within 1 and 2 sizes, respectively, this error is likely small. However, this study included only 12 femoral stem designs; meanwhile, supported these stems may see greatest fit within the femur at different locations. Although the assessment of how these relationships may impact clinical decision-making is beyond the scope of the research presented here, future studies assessing this may prove beneficial.
      Currently, there exist limited opportunities to confirm appropriate size of the femoral stem or acetabular cup intraoperatively. Meanwhile, the literature supports these sizes to be related to clinical outcomes. Studies have shown that broaching and/or reaming the femoral canal with less residual cancellous bone may decrease aseptic loosening [
      • Ebramzadeh E.
      • Sarmiento A.
      • Mc-Kellop H.A.
      • Llinas A.
      • Gogan W.
      The cement mantle in total hip arthroplasty: analysis of long-term radiographic results.
      ], improve implant-bone interface with a decreased rate of implant failure [
      • Johanson N.A.
      • Bullough P.G.
      • Wilson Jr., P.D.
      • Salvati E.D.
      • Ranawat C.S.
      The microscopic anatomy of the bone-cement interface in failed total hip arthroplasties.
      ], and may offer improved stem alignment [
      • Markolf K.L.
      • Amstutz H.C.
      A comparative experimental study of stresses in femoral total hip replacement components: the effects of prosthesis orientation and acrylic fixation.
      ]. However, these come at the expense of an increased risk for perioperative fracture [
      • Hartford J.M.
      • Knowles S.B.
      Risk factors for perioperative femoral fractures: cementless femoral implants and the direct anterior approach using a fracture table.
      ]. Thus, the benefit of offering an additional mode to confirm proper implant sizing, in addition to radiographic templating, will prove beneficial to streamline operative room efficiency.
      Although the presented accuracy of predicting THA implant sizes from demographic data has promising clinical implications, demographically predicted sizes are not intended to replace radiographic templating. Rather, this model provides a convenient method of validating traditional templating methods and may be done so in a time-efficient manner without additional expense or radiation exposure. Notably, patients having dysplasia, cysts, differing bone quality, fracture, or additional hardware will certainly require an attentive approach to templating. Although assessment of how these confounding factors influence demographically predicted sizing is beyond the scope of this study, the authors encourage a combined approach to preoperative planning to include traditional radiographic templating. In patients having unique boney morphology or having surrounding implants, the authors expect decreased accuracy with demographically predicted implant sizing.
      To the knowledge of the authors, this study is the first to use demographic variables for predicting implant size in THA. The authors do not suggest THA templating based on demographic variables and radiographic measurements alone, as these methods do not account for the anatomic variants, deformity, and other factors unique to each patient. Rather, the described models provide a safe, convenient, and reliable method for validating templated sizes with the potential to account for bony anomalies on a rudimentary level. The authors believe the models provided may streamline operative procedures with accurate inventory prediction.
      This study has several limitations. First, only 12 femoral stem implant models were used in this study. The application of the results of this study to more stems outside those used in this study requires further investigation. This study attempted to identify a singular point on the femoral stem that may be measured and used to readily compare between stem designs by assessing 5 separate points on the stem. Furthermore, a normalized stem size was calculated. The results of the study supported the normalized stem size to have highest predictive performance. The inability to directly measure the stem and correlate to the implanted size speaks to the unique fit among implants, although it also presents a challenge for the study to be applied more broadly. In an effort to standardize the results and reduce implant-related sizing bias, this study involved metaphyseal-fitting stems. Stems of a separate Mont group classification may see different results [
      • Khanuja H.S.
      • Vakil J.J.
      • Goddard M.S.
      • Mont M.A.
      Cementless femoral fixation in total hip arthroplasty.
      ].
      In addition, this study evaluated the predicted component size to the reported component size implanted during surgery. This assumes the implanted component size was ideal for the patient. No postoperative measures, imaging techniques, or computerized techniques were assessed to gauge whether implants were oversized or undersized.

      Conclusions

      The results of this study validate the use of demographic data and select radiographic measurements for predicting final THA implant sizes within one size for several manufacturers. Metaphyseal dimensions at the medial- and lateral-most proximal aspect of the femoral stem porous coating showed greatest correlation with demographic variables in predicting implant sizes. Surgeons may use this model to streamline operative procedures and reduce necessary inventory and potential cost.
      For ease of implementation, a free phone application called EasyTJA is available for download and use. This application is not meant to replace current standards in preoperative planning and does not accept responsibility for improper sizing, fit, or other patient outcomes. The authors are happy to adjust the application with feedback from the community, including any errors observed, recommended improvements, and addition of implants. With any recommendations, kindly reach out to the corresponding author.

      Appendix A. Supplementary Data

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