Efficacy of the Pre-operative Three-Dimensional (3D) CT Scan Templating in Predicting Accurate Implant Size and Alignment in Robot Assisted Total Knee Arthroplasty.

An alternative method for predicting size of femoral component of Oxford partial knee replacement

Inter-observer reliability of measurements performed on digital long-leg standing radiographs and assessment of validity compared to 3D CT-scan

BackgroundPreoperative 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 methodsA 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.ResultsPatient 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.ConclusionsA 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 evidenceProspective cohort, level II.

Patient dissatisfaction after total knee arthroplasty (TKA) is a concern. Surgical error is a common, avoidable cause of failed TKA. Correct femoral and tibial component sizing improves implant longevity, clinical outcomes, knee balance, and pain scores. We hypothesized that preoperative three-dimensional (3D) templating for robot-assisted TKA (RA-TKA) is more accurate than two-dimensional (2D) digital templating. Prospectively collected data from 31 RA-TKAs were assessed to determine accuracy pertaining to implant sizing and positioning. All cases undergoing RA-TKA undergo preoperative CT-scans as per protocol. Three blinded observers retrospectively templated these knees for TKA using standard radiographs. We compared whether 2D templating was as accurate as CT-guided templating. Postoperative radiographs were then evaluated for sizing and positioning. Intraclass correlation coefficients (ICCs) and the effect of learning curve were assessed. Preoperative femoral component 3D templating and retrospective blinded 2D templating accuracies were 96.6% and 52.9%, respectively (χ 2: 17.965; odds ratio [OR]: 24.957, 3.250-191.661; p < 0.001). Tibial component 3D and 2D templating accuracies were 93.1% and 28.7%, respectively (χ 2: 36.436; OR: 33.480, 7.400-151.481; p < 0.001). ICC for the three radiograph observers was 0.920 (95% confidence interval [CI]: 0.652-0.890; p < 0.001) for the femur and 0.833 (0.717-0.911; p < 0.001) for the tibia, showing excellent agreement. We conclude that preoperative CT-based templating for RA-TKA more accurately predicts the size of implants compared with traditional 2D digital templating. This may improve operating room efficiency and cost containment.

Where Are We Now? To justify widespread adoption of any classification scheme, a high degree of inter- and intraobserver reliability must be demonstrated. The reliability of assessing proximal humerus fracture patterns using widely-held classification systems such as that of Neer and Hertel based on plain radiographs has been fairly low, though the addition of advanced imaging such as two-dimensional (2-D) and three-dimensional (3-D) CT scans appears to improve both inter- and intraobserver reliability [4]. More recently, using 3D printed models alone in the surgical planning process has been found to improve interobserver reliability over plain radiographs and both 2D and 3D CT scans using the Neer system, though the observed agreement with the printed models was only moderate [2]. In these studies, where the kappa values using the guidelines of Landis and Koch were reported as "substantial" and "moderate," respectively, we must recognize that a high proportion of cases will still be misclassified. As a consequence, clinical outcomes research based on these classifications may result in misleading results. A recent meta-analysis [7] based on randomized trials comparing fracture fixation of various anatomic sites both with and without the use of 3D-printed models determined that blood loss, surgical time, fluoroscopy use, clinical outcomes, and achievement of anatomic reduction all favored 3D modeling. A limitation of this study was the inclusion of multiple fracture types and the small number of patients in several included studies. Furthermore, the effect size for many of the surgical outcome measures could be quite small based on the reported 95% confidence intervals. The only included study involving complex three- and four-part proximal humerus fractures [8] found a reduction in operative time, blood loss, and fluoroscopy time, though the clinical outcomes at final follow-up were similar. It is not clear, however, whether the mean 15-minute decreased operative time and approximately 55 cc decreased blood loss with 3D models is clinically significant. A retrospective study [3] comparing conventional preoperative planning using plain radiographs and both 2D and 3D CT scans with both computer-assisted virtual planning and 3D-printed models found shorter operative time, less blood loss, and less fluoroscopy in the latter groups compared to the conventional group. Planning time was shorter in the computer-assisted planning group compared with the 3D model group. Once again, the reported differences in the surgical parameters were small. Whether these differences justify the direct and indirect costs of routine use of 3D models, including the creation (personnel, software, hardware), storage, and potential sterilization, remain unclear [5]. In their current study, Spek and colleagues [6] examined 20 adult patients with complex three- and four-part proximal humerus fractures that were deemed difficult to classify and determined that the addition of 3D-printed handheld models to a series of plain radiographs and both 2D and 3D CT scan images did not improve interobserver reliability for the majority of fracture characteristics being studied. Additionally, the handheld models did not improve fracture classification using either the Neer or Hertel system. There was also no difference in agreement between residents and attending orthopaedic surgeons as to whether the 3D models aided in fracture pattern classification. These findings suggest that the routine use of 3D-printed models may not be beneficial for classifying proximal humeral fracture patterns beyond the information gained from currently available imaging modalities. Specifically, use of these models as the sole determinant for recommending surgical intervention based on fracture displacement should probably be avoided at this time based on the results of the current study. What is particularly concerning about the findings of the current study is that the addition of the 3D-printed models did not improve the ability of attending surgeons to identify particular fracture characteristics and classify patterns above that of the surgeons in-training. This would seem to indicate that a level of subjectivity exists within the classification systems themselves. Based on the results of the study, we should invest fewer resources determining whether handheld models improve preoperative fracture classification. The answer, according to Spek et al. [6], is a resounding "no." Where Do We Need To Go? The current study raises some important questions that warrant further study, namely: (1) In what capacity does the use of the 3D-printed model provide benefit to care for patients with proximal humerus fractures who have already been indicated for surgery? (2) What is the potential role of preoperative computer-assisted virtual surgical planning for proximal humerus fractures both with and without 3D model printing? In the only published randomized study that I am aware of assessing the surgical utility of 3D modeling for three- and four-part proximal humerus fractures [8], patients underwent preoperative planning using either two orthogonal radiographs and a thin-cut 2D CT scan versus plain radiographs, a 3D CT reconstruction with simulated fracture reduction using specialized software, and a handheld 3D-printed model. The use of 2D CT images in the control group represents a difference from the current paper, though a prior study [1] found that the use of 3D CT did not offer improvement in classification or treatment recommendations over 2D CT, except among junior residents. Regardless, to fully demonstrate the positive influence of the handheld models independently, researchers should ensure that both study groups are provided with all of the imaging modalities generally available today, including 3D CT images. Furthermore, future studies should determine whether these improvements can be replicated among surgeons of all levels of experience or if those with less experience would demonstrate greater benefit. Finally, we need a better understanding of the costs associated with the computer-assisted software and the model creation in light of the minimal—14-minute—surgical time difference reported. Computer-assisted planning can involve virtual reduction of the fracture and selection/placement of implants even without the use of 3D handheld models. One study [3] reported improved operative parameters for the virtual planning and 3D model group compared to the conventional planning group, though it is not entirely clear whether the differences are clinically significant. From a cost perspective, more data are needed to determine whether the 30 minutes of virtual planning is cost-efficient with the 18 minutes of reduced operative time. Computer planning time may be even higher for surgeons performing a lower volume of proximal humerus fracture surgery. How Do We Get There? The primary potential advantage of 3D-printed models likely will be realized in more complex proximal humerus fracture patterns that have already been indicated for surgical intervention. Specifically, the 3D models can provide the surgeon with a tactile modality for planning fracture reductions and correct placement of hardware. Future studies for determining the utility of the 3D models in the clinical realm should be designed based on objective surgical parameters such as operative time, duration of fluoroscopy use, estimated blood loss, adequacy of fracture reduction, and perhaps most importantly, on patient outcomes. Given the dearth of available evidence, the utility of 3D models versus computer-assisted fracture planning alone needs to be validated. The reported differences in these parameters have been fairly small in the literature so far and, therefore, justification for utilizing either technology necessarily requires demonstrating larger, more clinically relevant differences. Furthermore, future studies must assess whether surgeons with extensive experience with proximal humerus fracture fixation will derive any meaningful benefit from these technologies. A comparative study of this type needs to be performed in a high-volume Level 1 trauma center to achieve sufficient patient numbers. Only three-part and four-part fractures should be included and should be randomized either to planning through the use of standard imaging including 2D and 3D CT or to planning with additional use of the 3D-printed model versus computer-assisted planning. To determine which surgeons would most benefit from either the 3D model or computer-assisted planning, surgical data need to be stratified for surgeon volume and/or clinical experience. There will be a learning curve for use of the planning software, which should be taken into account regarding planning time. Innovation can often be costly, and cost benefits with both of these technologies must be demonstrated, either by calculating operating time savings compared with increased planning time and/or by reduced intraoperative implant wastage. As there are no currently defined minimal clinically important differences for surgical parameters such as intraoperative blood loss, surgical time, and use of fluoroscopy, any potential benefit must be considered in light of a rigorous cost-benefit analysis. Finally, any comparison of patient-reported functional outcomes should be viewed in light of minimal clinically important differences.

Preoperative planning is an important phase of total joint arthroplasty. Current template programs and methods only provide moderate accuracy for implant size prediction. Recently, a relationship between shoe size and implant size was found. We hypothesized that shoe size shows a high percentage of agreement for implant size of both femoral and tibial component size in primary total knee arthroplasty (TKA). The aim of this study was to investigate the correlation and agreement between shoe size and TKA implant size. We performed a retrospective cohort study. Of all patients, who underwent primary TKA between September 2013 and March 2016, shoe size and knee implant sizes were collected. Cross-tabulation was used to determine the correlation and agreement between shoe size and implant size. A total of 489 patients (498 TKA) were included. The correlation coefficient for femoral and tibial component with shoe size was 0.751 and 0.759, respectively. When a deviation of ± 1 component size was allowed, shoe size gave at least 94% agreement score for femoral component and at least 86% agreement score in tibial component. We conclude that both femoral and tibial component size have a good correlation with shoe size. Therefore, shoe size may be used as a valuable predictor in preoperative implant size planning for primary TKA. The level of evidence for this study was Level IV.

IntroductionAccurate glenoid component placement is crucial for anatomic (TSA) or reverse (RSA) total shoulder arthroplasty. Preoperative glenoid assessment by using CT scans with or without planning software seems to be the established method to plan implant positions. MRI scans can also display the glenoid bone for preoperative assessment while reducing radiation exposure. Therefore, the objective of this study was to manually assess the glenoid version and inclination in 2D MRI and CT scans in cases with degenerative shoulder pathologies. The results were compared to those of an automated 3D planning software to validate the imaging modality for preoperative glenoid assessment. MethodsMRI and CT scans of 146 patients (n=41 aTSA; n=105 RSA) were included in this retrospective, single-center study. Glenoid version and inclination were measured manually according to Friedman et al and Maurer et al on CT and MRI scans by two observers. Subsequently, the results were compared to the automated measurements performed by a planning software. A repeated-measures analysis of variance (ANOVA) was performed to compare the measured angles and interobserver and intraobserver reliability was calculated using the intraclass correlation coefficients. The level of significance was set p<0.05. ResultsThe average glenoid inclination measured in CT scans was 7.94°±7.33°, in MRI scans 8.56°±7.34° and in automated planning software 7.87°±7.60°. The ANOVA analysis revealed significant differences in mean inclination between 2D MRI and 2D CT (p<0.0005) and between MRI and automated software (p=0.011). No significant difference was found between 2D CT scans and automated planning software (p=1.000). Mean glenoid version measured in 2D CT scans was -7.94°±10.86°, in 2D MRI scans it was -8.04°±10.80° and -8.32°±11.53° by the automated planning software. There was no significant difference in between measurement methods (p = 0.339). Interobserver error analysis showed no statistical differences between the two observers. All measurements had excellent intraobserver reliability. ConclusionPreoperative assessment of glenoid version and inclination is crucial in ensuring precise implant positioning and orientation in TSA and RSA. This study observed a significant level of concordance between manual and automated measuring techniques utilizing MRI and CT scans. Mean glenoid inclination exhibited a statistically significant difference of less than 1° across the assessment modalities and no difference for glenoid version was noted. It seems to be questionable if this finding is clinically relevant. MRI may serve as a viable and safe option for assessing glenoid morphology, version and inclination if CT scans are not available.

BackgroundConcurrent chemoradiotherapy is considered curative intent treatment for patients with non-operative esophageal cancer. Radiation-induced heart damage receives much attention. We performed repeated four-dimensional computed tomography (4DCT) to detect changes in cardiac volume during radiotherapy for esophageal cancer patients, and explored potential factors responsible for those changes.MethodsForty-six patients with esophageal cancer underwent enhanced 4DCT and three-dimensional (3D) CT scans before radiotherapy and every 10 fractions during treatment. The heart was contoured on 3DCT images, 4DCT end expiratory (EE) images and 4DCT maximum intensity projection (MIP) images by the same radiation oncologist. Heart volumes and other relative parameters were compared by the SPSS software package, version 19.0.ResultsCompared with its initial value, heart volume was smaller at the 10th fraction (reduction = 3.27%, 4.45% and 4.52% on 3DCT, EE and MIP images, respectively, p < 0.05) and the 20th fraction (reduction = 6.05%, 5.64% and 4.51% on 3DCT, EE and MIP images, respectively, p < 0.05), but not at the 30th fraction. Systolic and diastolic blood pressures were reduced (by 16.95 ± 16.69 mmHg and 7.14 ± 11.64 mmHg, respectively, both p < 0.05) and the heart rate was elevated by 5.27 ± 6.25 beats/min (p < 0.05) after radiotherapy. None of the potential explanatory variables correlated with heart volume changes.ConclusionsCardiac volume reduced significantly from an early treatment stage and maintained the reduction until the middle stage. The heart volume changes observed on 3DCT and 4DCT were consistent during radiotherapy. The changes in heart volume, blood pressure and heart rate may be valuable indicators of cardiac impairment and target dose changes.

Component asymmetry in bilateral total knee arthroplasty in the middle eastern population

Prospective Validation of a Demographically Based Primary Total Knee Arthroplasty Size Calculator

Introduction:Reliability is the study of internal consistency, reproducibility (intraobserver and interobserver), and agreement. Reproducibility studies that classify tibial plateau fractures have used plain radiography and two-dimensional (2D) CT scans and three-dimensional (3D) printing. The objective of this study was to evaluate the reproducibility of the Luo Classification of tibial plateau factures and the surgical approaches chosen for these fractures based on 2D CT scans and 3D printing.Methods:A reliability study was performed at the Universidad Industrial de Santander, Colombia, that evaluated the reproducibility of the Luo Classification of tibial plateau fractures and the choice of surgical approaches based on 20 CT scans and 3D printing, with five evaluators.Results:For the trauma surgeon, reproducibility was better when evaluating the classification using 3D printing, with a kappa of 0.81 (95% confidence interval [CI], 0.75-0.93; P < 0.01) than when using CT scans, with a kappa of 0.76 (95% CI, 0.62-0.82; P < 0.01). When comparing the surgical decisions made by the fourth-year resident with those of the trauma surgeon, a fair reproducibility was obtained using CT, with a kappa of 0.34 (95% CI, 0.21-0.46; P < 0.01), which improved to substantial when using 3D printing, with a kappa of 0.63 (95% CI, 0.53-0.73; P < 0.01).Discussion:This study found that 3D printing provided more information than CT and decreased measurement errors, thereby improving reproducibility, as shown by the higher kappa values that were obtained.Conclusion:The use of 3D printing and its usefulness are helpful to decision making when providing emergency trauma services to patients with intraarticular fractures such as those of the tibial plateau.

Bilateral osteoarthritis of the knee is an indication for a bilateral total knee replacement (TKR) procedure. The goal of our study was to assess the sizes of the implants used during the first and second stages of TKR procedures in order to compare their size and identify the prognostic factors for the second procedure. We evaluated 44 patients who underwent staged bilateral TKR procedures. We assess the following prognostic factors from the first and second surgery: duration of anesthesia, femoral component size, tibial component size, duration of hospital stay, tibial polyethylene insert size, and the number of complications. All assessed prognostic factors did not differ statistically between the first and second TKR. A strong correlation was found between the size of femoral components and the size of tibial components used during the first and second total knee arthroplasty. The mean duration of the hospital stay associated with the first TKR surgery was 6.43 days, whereas the mean duration of the second hospital stay was 5.5 days (p = 0.211). The mean sizes of the femoral components used during the first and second procedures were 5.43 and 5.2, respectively (p = 0.54). The mean sizes of the tibial components used during the first and second TKR procedures were 5.36 and 5.25, respectively (p = 0.382). The mean sizes of the tibial polyethylene inserts used during the first and second procedures were 9.45 and 9.34 (p = 0.422), respectively. The mean duration of anesthesia during the first and second knee arthroplasty was 117.04 min and 118.06 min, respectively (p = 0.457). The mean rates of recorded complications associated with the first and second TKR procedures were 0.13 and 0.06 per patient (p = 0.371). We observed no differences between the two stages of treatment in terms of all analyzed parameters. We observed a strong correlation between the size of femoral components used during the first and second total knee arthroplasty. We noted a strong correlation between the size of tibial components used during the first and second procedure. Slightly weaker prognostic factors include the number of complications, duration of anesthesia and tibial polyethylene insert size.

Three-dimensional (3D) templating of the glenoid in anatomic shoulder arthroplasty allows for more accurate planning and more optimal positioning of the glenoid component than 2-dimensional computed tomography (2D CT) scans through an improved understanding of both the pathologic and the premorbid glenoid joint line, version, and inclination in reference to an idealized calculated glenoid position. Obtain a CT scan of the entire scapula and proximal part of the humerus with slices of ≤1 mm and a 3D reconstruction with subtraction of the humeral head, and identify the scapular and glenoid planes to define the pathologic version and inclination, which can be done in any commercially available software program while following these basic principles (Video 1). Carefully evaluate for the presence of the native glenoid, noting its version and inclination, and be careful to distinguish the true native glenoid from osteophytes (Video 2). Place the virtual glenoid component to restore the premorbid glenoid anatomy (Video 3). In the presence of bone loss from posterior glenoid wear, assess the need for an augmented glenoid component, bone graft, or eccentric reaming to achieve adequate backside seating (Video 4). Once the glenoid component has been templated, note the starting location and trajectory of the center pin used for cannulated glenoid reaming and bone preparation (Video 5). Intraoperatively, remove remaining labrum and any remaining cartilage or soft tissue, and expose the glenoid periphery to clearly define the osseous anatomy, including the base of the coracoid, such that it mirrors what the 3D CT scan and preoperative plan display (Video 6). Place the center pin for glenoid preparation in the previously templated location and trajectory to emulate the surgical plan defined in the software (Video 7). We performed a prospective, randomized controlled trial of positioning of the glenoid component in anatomic TSA using preoperative planning with 3D CT scans and standard instrumentation compared with using 3D CT preoperative planning with patient-specific instrumentation29.

Interobserver and Intraobserver Reliability of Computed Tomography–Based Three-Dimensional Preoperative Planning for Primary Total Knee Arthroplasty

To develop a novel machine learning algorithm capable of predicting TKA implant sizes using a large, multicenter database. 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). 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. 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. Case-control, III.