Whole Blood Metal Levels in the Setting of an Oncologic Endoprosthesis: Is There Cause for Concern?

BACKGROUND: Massive modular endoprostheses have become a primary means of reconstruction after oncologic resection of a lower extremity tumor. These implants are commonly made with cobalt-chromium alloys that can undergo wear and corrosion, releasing cobalt and chromium ions into the surrounding tissue and blood. However, there are few studies about the blood metal levels in these patients. QUESTION/PURPOSE: What is the whole blood cobalt and chromium ion level in patients with massive modular endoprostheses? METHODS: We performed a cross-sectional study of our total joints registry to identify patients with a history of an endoprosthetic reconstruction performed at our institution. Patients who were alive at the time of our review in addition to those undergoing an endoprosthetic reconstruction after an oncologic resection were included. Whole blood samples were obtained from 27 (14 male and 13 female) patients with a history of a lower extremity oncologic endoprosthesis. The median time from surgery to blood collection was 8 years (range 6 months to 32 years). Blood samples were collected and stored in metal-free ethylenediaminetetraacetic acid tubes. Samples were analyzed on an inductively coupled plasma mass spectrometer in an International Organization for Standardization seven-class clean room using polytetrafluoroethylene-coated instruments to reduce the risk of metal contamination. The analytical measuring range was 1 to 200 ng/mL for chromium and cobalt. Cobalt and chromium levels were considered elevated when the blood level was ≥ 1 ppb. RESULTS: Cobalt levels were elevated in 59% (16 of 27) of patients, and chromium levels were elevated in 26% (seven of 27). In patients with elevated metal ion values, 15 of 17 patients had a reconstruction using a Stryker/Howmedica Global Modular Replacement System implant. CONCLUSION: Blood metal levels were elevated in patients who received reconstructions using modular oncology endoprostheses Future work is needed to establish appropriate follow-up routines and determine whether and when systemic complications occur because of elevated metal levels and how to potentially address these elevated levels when complications occur. Prospective and retrospective collaboration between multiple centers and specialty societies will be necessary to address these unknown questions in this potentially vulnerable patient group. LEVEL OF EVIDENCE: Level IV, therapeutic study.

THA-Net: A Deep Learning Solution for Next-Generation Templating and Patient-specific Surgical Execution.

BACKGROUND: This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either unconditionally (algorithm chooses implants) or conditionally (surgeon chooses implants). METHODS: The THA-Net is a deep learning algorithm which receives an input preoperative radiograph and subsequently replaces the target hip joint with THA implants to generate a synthetic yet realistic postoperative radiograph. We trained THA-Net on 356,305 pairs of radiographs from 14,357 patients from a single institution's total joint registry and evaluated the validity (quality of surgical execution) and realism (ability to differentiate real and synthetic radiographs) of its outputs against both human-based and software-based criteria. RESULTS: The surgical validity of synthetic postoperative radiographs was significantly higher than their real counterparts (mean difference: 0.8 to 1.1 points on 10-point Likert scale, P < .001), but they were not able to be differentiated in terms of realism in blinded expert review. Synthetic images showed excellent validity and realism when analyzed with already validated deep learning models. CONCLUSION: We developed a THA next-generation templating tool that can generate synthetic radiographs graded higher on ultimate surgical execution than real radiographs from training data. Further refinement of this tool may potentiate patient-specific surgical planning and enable technologies such as robotics, navigation, and augmented reality (an online demo of THA-Net is available at: https://demo.osail.ai/tha_net).

THA-AID: Deep Learning Tool for Total Hip Arthroplasty Automatic Implant Detection With Uncertainty and Outlier Quantification.

BACKGROUND: Revision total hip arthroplasty (THA) requires preoperatively identifying in situ implants, a time-consuming and sometimes unachievable task. Although deep learning (DL) tools have been attempted to automate this process, existing approaches are limited by classifying few femoral and zero acetabular components, only classify on anterior-posterior (AP) radiographs, and do not report prediction uncertainty or flag outlier data. METHODS: This study introduces Total Hip Arhtroplasty Automated Implant Detector (THA-AID), a DL tool trained on 241,419 radiographs that identifies common designs of 20 femoral and 8 acetabular components from AP, lateral, or oblique views and reports prediction uncertainty using conformal prediction and outlier detection using a custom framework. We evaluated THA-AID using internal, external, and out-of-domain test sets and compared its performance with human experts. RESULTS: THA-AID achieved internal test set accuracies of 98.9% for both femoral and acetabular components with no significant differences based on radiographic view. The femoral classifier also achieved 97.0% accuracy on the external test set. Adding conformal prediction increased true label prediction by 0.1% for acetabular and 0.7 to 0.9% for femoral components. More than 99% of out-of-domain and >89% of in-domain outlier data were correctly identified by THA-AID. CONCLUSIONS: The THA-AID is an automated tool for implant identification from radiographs with exceptional performance on internal and external test sets and no decrement in performance based on radiographic view. Importantly, this is the first study in orthopedics to our knowledge including uncertainty quantification and outlier detection of a DL model.

Surgical Approach and Body Mass Index Impact Risk of Wound Complications Following Total Hip Arthroplasty.

BACKGROUND: Previous studies have suggested that wound complications may differ by surgical approach after total hip arthroplasty (THA), with particular attention toward the direct anterior approach (DAA). However, there is a paucity of data documenting wound complication rates by surgical approach and the impact of concomitant patient factors, namely body mass index (BMI). This investigation sought to determine the rates of wound complications by surgical approach and identify BMI thresholds that portend differential risk. METHODS: This multicenter study retrospectively evaluated all primary THA patients from 2010 to 2023. Patients were classified by skin incision as having a laterally based approach (posterior or lateral approach) or DAA (longitudinal incision). We identified 17,111 patients who had 11,585 laterally based (68%) and 5,526 (32%) DAA THAs. The mean age was 65 years (range, 18 to 100), 8,945 patients (52%) were women, and the mean BMI was 30 (range, 14 to 79). Logistic regression and cut-point analyses were performed to identify an optimal BMI cutoff, overall and by approach, with respect to the risk of wound complications at 90 days. RESULTS: The 90-day risk of wound complications was higher in the DAA group versus the laterally based group, with an absolute risk of 3.6% versus 2.6% and a multivariable adjusted odds ratio of 1.5 (P < .001). Cut-point analyses demonstrated that the risk of wound complications increased steadily for both approaches, but most markedly above a BMI of 33. CONCLUSIONS: Wound complications were higher after longitudinal incision DAA THA compared to laterally based approaches, with a 1% higher absolute risk and an adjusted odds ratio of 1.5. Furthermore, BMI was an independent risk factor for wound complications regardless of surgical approach, with an optimal cut-point BMI of 33 for both approaches. These data can be used by surgeons to help consider the risks and benefits of approach selection. LEVEL OF EVIDENCE: Level III.

The Impact of Leg Length and Offset Change on Dislocation Risk Following Primary Total Hip Arthroplasty.

INTRODUCTION: Soft tissue management in total hip arthroplasty (THA) includes appropriate restoration and/or alteration of leg length and offset to re-establish natural hip biomechanics. The purpose of this study was to evaluate the effect of leg length and offset-derived variables in a multivariable survival model for dislocation. METHODS: Clinical, surgical, and radiographic data was retrospectively acquired for 12,582 patients undergoing primary THA at a single institution from 1998 to 2018. There were twelve variables derived from preoperative and postoperative radiographs related to leg length and offset that were measured using a validated automated algorithm. These measurements, as well as other modifiable and non-modifiable surgical, clinical, and demographic factors, were used to determine hazard ratios (HR) for dislocation risk. RESULTS: None of the leg length or offset variables conferred significant risk or protective benefit for dislocation risk. By contrast, all other variables included in the multivariable model demonstrated a statistically significant effect on dislocation risk with a minimum effect size of 28% (range 0.72 to 1.54) (sex, surgical approach, acetabular liner type, femoral head size, neurologic disease, spine disease, and prior spine surgery). CONCLUSION: Contrary to traditional teaching and our hypothesis, operative changes in leg length and offset did not demonstrate any clinically or statistically significant effect in this large and well-characterized cohort. This does not imply that these variables are not important in individual cases, but rather suggests the overall impact of leg length and offset changes is relatively minor for dislocation risk compared to other variables that were found to be highly clinically and statistically significant in this population. These results may also suggest that surgeons do a good job of restoring native leg length and offset for patients, which may mitigate their analyzed impact.

Artificial intelligence-based three-dimensional templating for total joint arthroplasty planning: a scoping review.

PURPOSE: The purpose of this review is to evaluate the current status of research on the application of artificial intelligence (AI)-based three-dimensional (3D) templating in preoperative planning of total joint arthroplasty. METHODS: This scoping review followed the PRISMA, PRISMA-ScR guidelines, and five stage methodological framework for scoping reviews. Studies of patients undergoing primary or revision joint arthroplasty surgery that utilised AI-based 3D templating for surgical planning were included. Outcome measures included dataset and model development characteristics, AI performance metrics, and time performance. After AI-based 3D planning, the accuracy of component size and placement estimation and postoperative outcome data were collected. RESULTS: Nine studies satisfied inclusion criteria including a focus on computed tomography (CT) or magnetic resonance imaging (MRI)-based AI templating for use in hip or knee arthroplasty. AI-based 3D templating systems reduced surgical planning time and improved implant size/position and imaging feature estimation compared to conventional radiographic templating. Several components of data processing and model development and testing were insufficiently covered in the studies included in this scoping review. CONCLUSIONS: AI-based 3D templating systems have the potential to improve preoperative planning for joint arthroplasty surgery. This technology offers more accurate and personalized preoperative planning, which has potential to improve functional outcomes for patients. However, deficiencies in several key areas, including data handling, model development, and testing, can potentially hinder the reproducibility and reliability of the methods proposed. As such, further research is needed to definitively evaluate the efficacy and feasibility of these systems.

Identifying Modifiable Cost Drivers of Outpatient Unicompartmental Knee Arthroplasty With Machine Learning.

BACKGROUND: Implementing tools that identify cost-saving opportunities for ambulatory orthopaedic surgeries can improve access to value-based care. We developed and internally validated a machine learning (ML) algorithm to predict cost drivers of total charges after ambulatory unicompartmental knee arthroplasty (UKA). METHODS: We queried the New York State Ambulatory Surgery and Services database to identify patients who underwent ambulatory, defined as <24 hours of care before discharge, elective UKA between 2014 and 2016. A total of 1,311 patients were included. The median costs after ambulatory UKA were $14,710. Patient demographics and intraoperative parameters were entered into 4 candidate ML algorithms. The most predictive model was selected following internal validation of candidate models, with conventional linear regression as a benchmark. Global variable importance and partial dependence curves were constructed to determine the impact of each input parameter on total charges. RESULTS: The gradient-boosted ensemble model outperformed all candidate algorithms and conventional linear regression. The major differential cost drivers of UKA identified (in decreasing order of magnitude) were increased operating room time, length of stay, use of regional and adjunctive periarticular analgesia, utilization of computer-assisted navigation, and routinely sending resected tissue to pathology. CONCLUSION: We developed and internally validated a supervised ML algorithm that identified operating room time, length of stay, use of computer-assisted navigation, regional primary anesthesia, adjunct periarticular analgesia, and routine surgical pathology as essential cost drivers of UKA. Following external validation, this tool may enable surgeons and health insurance providers optimize the delivery of value-based care to patients receiving outpatient UKA. LEVEL OF EVIDENCE: III.

Health and Socioeconomic Risk Factors for Unplanned Hospitalization Following Ambulatory Unicompartmental Knee Arthroplasty: Development of a Patient Selection Tool Using Machine Learning.

BACKGROUND: Identifying ambulatory surgical candidates at risk for adverse surgical outcomes can optimize outcomes. The purpose of this study was to develop and internally validate a machine learning (ML) algorithm to predict contributors to unexpected hospitalizations after ambulatory unicompartmental knee arthroplasty (UKA). METHODS: A total of 2,521 patients undergoing UKA from 2006 to 2018 were retrospectively evaluated. Patients admitted overnight postoperatively were identified as those who had a length of stay ≥ 1 day were analyzed with four individual ML models (ie, random forest, extreme gradient boosting, adaptive boosting, and elastic net penalized logistic regression). An additional model was produced as a weighted ensemble of the four individual algorithms. Area under the receiver operating characteristics (AUROC) compared predictive capacity of these models to conventional logistic regression techniques. RESULTS: Of the 2,521 patients identified, 103 (4.1%) required at least one overnight stay following ambulatory UKA. The ML ensemble model achieved the best performance based on discrimination assessed via internal validation (AUROC = 87.3), outperforming individual models and conventional logistic regression (AUROC = 81.9-85.7). The variables determined most important by the ensemble model were cumulative time in the operating room, utilization of general anesthesia, increasing age, and patient residency in more urban areas. The model was integrated into a web-based open-access application. CONCLUSION: The ensemble gradient-boosted ML algorithm demonstrated the highest performance in identifying factors contributing to unexpected hospitalizations in patients receiving UKA. This tool allows physicians and healthcare systems to identify patients at a higher risk of needing inpatient care after UKA.

Defining Clinically Meaningful Subgroups for Risk Stratification in Patients Undergoing Revision Total Hip Arthroplasty: A Combined Unsupervised and Supervised Machine Learning Approach.

BACKGROUND: Studies developing predictive models from large datasets to risk-stratify patients under going revision total hip arthroplasties (rTHAs) are limited. We used machine learning (ML) to stratify patients undergoing rTHA into risk-based subgroups. METHODS: We retrospectively identified 7,425 patients who underwent rTHA from a national database. An unsupervised random forest algorithm was used to partition patients into high-risk and low-risk strata based on similarities in rates of mortality, reoperation, and 25 other postoperative complications. A risk calculator was produced using a supervised ML algorithm to identify high-risk patients based on preoperative parameters. RESULTS: There were 3,135 and 4,290 patients identified in the high-risk and low-risk subgroups, respectively. Each group significantly differed by rate of 30-day mortalities, unplanned reoperations/readmissions, routine discharges, and hospital lengths of stay (P < .05). An Extreme Gradient Boosting algorithm identified preoperative platelets < 200, hematocrit > 35 or < 20, increasing age, albumin < 3, international normalized ratio > 2, body mass index > 35, American Society of Anesthesia class ≥ 3, blood urea nitrogen > 50 or < 30, creatinine > 1.5, diagnosis of hypertension or coagulopathy, and revision for periprosthetic fracture and infection as predictors of high risk. CONCLUSION: Clinically meaningful risk strata in patients undergoing rTHA were identified using an ML clustering approach. Preoperative labs, demographics, and surgical indications have the greatest impact on differentiating high versus low risk. LEVEL OF EVIDENCE: III.

Femoral Head Length Impact on Outcomes Following Total Hip Arthroplasty in 36 Millimeter Cobalt Chrome-on-Highly Crosslinked Polyethylene Articulations.

BACKGROUND: Despite concerns for corrosion, dislocation, and periprosthetic femur fractures, minimal literature has investigated the effect of adjusting femoral head length on outcomes after primary total hip arthroplasty (THA). Therefore, we aimed to investigate the effect of femoral head length on the risk of any revision and reoperation following cobalt chromium (CoCr)-on-highly crosslinked polyethylene (HXLPE) THAs. METHODS: Between 2004 and 2018, we identified 1,187 primary THAs with CoCr-on-HXLPE articulations using our institutional total joint registry. The mean age at THA was 71 years (range, 19-97), 40% were women, and mean body mass index was 30 (range, 10-68). All THAs using 36 mm diameter femoral heads were included. Neutral (0 mm), positive, or negative femoral head lengths were used in 42, 31, and 27% of the THAs, respectively. Kaplan-Meier survivorship was assessed. The mean follow-up was 7 years (range, 2-16). RESULTS: The 10-year survivorships free of any revision or reoperation were 94 and 92%, respectively. A total of 47 revisions were performed, including periprosthetic femur fracture (17), periprosthetic joint infection (8), dislocation (7), aseptic loosening of either component (6), corrosion (4), and other (5). Nonrevision reoperations included wound revision (11), open reduction and internal fixation of periprosthetic femur fracture (4), and abductor repair (2). Multivariable analyses found no significant associations between femoral head length and revision or reoperation. CONCLUSION: Altering femoral head lengths in 36 mm CoCr-on-HXLPE THAs did not affect outcomes. Surgeons should select femoral head lengths that optimize hip stability and center of rotation. LEVEL OF EVIDENCE: III.