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.

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.

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.

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.

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.

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.

Pubic Symphysis to Sacrococcygeal Joint: A Poor Correlate to Other Spinopelvic Measurements.

BACKGROUND: The hip-spine relationship is increasingly recognized as critical for optimizing stability following total hip arthroplasty (THA). However, these measurements are not routinely obtained during THA workup. It has been suggested that insight can be gained from supine antero-posterior pelvis radiograph, measuring the distance from the superior border of the pubic symphysis to the sacro-coccygeal joint (PSCD). This study assessed the correlation between PSCD and lateral lumbar radiographic metrics in a cohort of preoperative THA patients. METHODS: We retrospectively evaluated 250 consecutive patients who underwent THA with preoperative supine antero-posterior pelvis and lateral lumbar radiographs. The mean age was 68 years (range, 42 to 89), 61% were women, and the mean body mass index was 30 kg/m2 (range, 19 to 52). Two reviewers measured PSCD, pelvic tilt (PT), sacral slope (SS), pelvic incidence (PI), and lumbar lordosis (LL). Inter-observer reliability was calculated for all measurements, and correlation coefficients were calculated for PSCD with respect to PT, SS, PI, and LL. RESULTS: Correlations between PSCD and lumbar radiographic metrics were all statistically significant, except for PI in men but graded as "weak" or "very weak" for men and women, respectively, as follows: PT = -0.30 (P < .01) and -0.46 (P < .01); SS = 0.27 (P < .01) and 0.22 (P < .01); PI = -0.04 (P = .70) and -0.19 (P = .02); and LL = 0.45 (P < .01) and 0.30 (P < .01). Inter-observer reliability was graded as "strong" for every metric. CONCLUSION: The PSCD was weakly correlated with all evaluated lateral lumbar radiographic metrics in both sexes, despite strong inter-observer reliability. Therefore, PSCD cannot reliably serve as a proxy for evaluating the hip-spine relationship.

Application of Natural Language Processing in Total Joint Arthroplasty: Opportunities and Challenges.

Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a large majority of this data is concealed in electronic health records only accessible by manual extraction, which takes extensive time and resources. Natural language processing (NLP), a field within artificial intelligence, may offer a viable alternative to manual extraction. Using NLP, a researcher can analyze written and spoken data and extract data in an organized manner suitable for future research and clinical use. This article will first discuss common subtasks involved in an NLP pipeline, including data preparation, modeling, analysis, and external validation, followed by examples of NLP projects. Challenges and limitations of NLP will be discussed, closing with future directions of NLP projects, including large language models.

Educational Overview of the Concept and Application of Computer Vision in Arthroplasty.

Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret and manage that data. Computer Vision (CV) has grown in popularity as a discipline for better understanding visual data. Computer Vision has become a powerful tool for imaging analytics in orthopedic surgery, allowing computers to evaluate large volumes of image data with greater nuance than previously possible. Nevertheless, even with the growing number of uses in medicine, literature on the fundamentals of CV and its implementation is mainly oriented toward computer scientists rather than clinicians, rendering CV unapproachable for most orthopedic surgeons as a tool for clinical practice and research. The purpose of this article is to summarize and review the fundamental concepts of CV application for the orthopedic surgeon and musculoskeletal researcher.

Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.

Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional data analysis. Therefore, researchers are increasingly using machine learning (ML)-based methods to better handle these more challenging datasets for the discovery of hidden patterns in patients' data and for classification and predictive purposes. This article describes commonly used ML methods in structured data analysis with examples in orthopedic surgery. We present practical considerations in starting an ML project and appraising published studies in this field.

An Overview of Machine Learning in Orthopedic Surgery: An Educational Paper.

The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.

A Deep Learning Tool for Automated Landmark Annotation on Hip and Pelvis Radiographs.

BACKGROUND: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single deep learning model to annotate certain anatomical structures and landmarks on antero-posterior (AP) pelvis radiographs. METHODS: A total of 1,100 AP pelvis radiographs were manually annotated by 3 reviewers. These images included a mix of preoperative and postoperative images as well as a mix of AP pelvis and hip images. A convolutional neural network was trained to segment 22 different structures (7 points, 6 lines, and 9 shapes). Dice score, which measures overlap between model output and ground truth, was calculated for the shapes and lines structures. Euclidean distance error was calculated for point structures. RESULTS: Dice score averaged across all images in the test set was 0.88 and 0.80 for the shape and line structures, respectively. For the 7-point structures, average distance between real and automated annotations ranged from 1.9 mm to 5.6 mm, with all averages falling below 3.1 mm except for the structure labeling the center of the sacrococcygeal junction, where performance was low for both human and machine-produced labels. Blinded qualitative evaluation of human and machine produced segmentations did not reveal any drastic decrease in performance of the automatic method. CONCLUSION: We present a deep learning model for automated annotation of pelvis radiographs that flexibly handles a variety of views, contrasts, and operative statuses for 22 structures and landmarks.

Irrigation and Debridement With Chronic Antibiotic Suppression for the Management of Acutely Infected Aseptic Revision Total Joint Arthroplasties.

BACKGROUND: Most data on irrigation and debridement with component retention (IDCR) as a treatment for acute periprosthetic joint infections (PJIs) focuses on primary total joint arthroplasties (TJAs). However, the incidence of PJI is greater after revisions. We investigated the outcomes of IDCR with suppressive antibiotic therapy (SAT) following aseptic revision TJAs. METHODS: Through our total joint registry, we identified 45 aseptic revision TJAs (33 hips, 12 knees) performed from 2000 to 2017 that were treated with IDCR for acute PJI. Acute hematogenous PJI was present in 56%. Sixty-four percent of PJIs involved Staphylococcus. All patients were treated with 4 to 6 weeks of intravenous antibiotics with the intention to treat with SAT (89% received SAT). The mean age was 71 years (range, 41 to 90), with 49% being women and a mean body mass index of 30 (range, 16 to 60). The mean follow-up was 7 years (range, 2 to 15). RESULTS: The 5-year survivorships free from re-revision for infection and reoperation for infection were 80% and 70%, respectively. Of the 13 reoperations for infection, 46% involved the same species as the initial PJI. The 5-year survivorships free from any revision and any reoperation were 72% and 65%, respectively. The 5-year survivorship free from death was 65%. CONCLUSION: At 5 years following IDCR, 80% of implants were free from re-revision for infection. As the penalty for implant removal is often high in revision TJAs, IDCR with SAT is a viable option for acute infection after revision TJAs in select patients. LEVEL OF EVIDENCE: IV.

Creating High Fidelity Synthetic Pelvis Radiographs Using Generative Adversarial Networks: Unlocking the Potential of Deep Learning Models Without Patient Privacy Concerns.

BACKGROUND: In this work, we applied and validated an artificial intelligence technique known as generative adversarial networks (GANs) to create large volumes of high-fidelity synthetic anteroposterior (AP) pelvis radiographs that can enable deep learning (DL)-based image analyses, while ensuring patient privacy. METHODS: AP pelvis radiographs with native hips were gathered from an institutional registry between 1998 and 2018. The data was used to train a model to create 512 × 512 pixel synthetic AP pelvis images. The network was trained on 25 million images produced through augmentation. A set of 100 random images (50/50 real/synthetic) was evaluated by 3 orthopaedic surgeons and 2 radiologists to discern real versus synthetic images. Two models (joint localization and segmentation) were trained using synthetic images and tested on real images. RESULTS: The final model was trained on 37,640 real radiographs (16,782 patients). In a computer assessment of image fidelity, the final model achieved an "excellent" rating. In a blinded review of paired images (1 real, 1 synthetic), orthopaedic surgeon reviewers were unable to correctly identify which image was synthetic (accuracy = 55%, Kappa = 0.11), highlighting synthetic image fidelity. The synthetic and real images showed equivalent performance when they were assessed by established DL models. CONCLUSION: This work shows the ability to use a DL technique to generate a large volume of high-fidelity synthetic pelvis images not discernible from real imaging by computers or experts. These images can be used for cross-institutional sharing and model pretraining, further advancing the performance of DL models without risk to patient data safety. LEVEL OF EVIDENCE: Level III.

External Validation of Natural Language Processing Algorithms to Extract Common Data Elements in THA Operative Notes.

BACKGROUND: Natural language processing (NLP) systems are distinctive in their ability to extract critical information from raw text in electronic health records (EHR). We previously developed three algorithms for total hip arthroplasty (THA) operative notes with rules aimed at capturing (1) operative approach, (2) fixation method, and (3) bearing surface using inputs from a single institution. The purpose of this study was to externally validate and improve these algorithms as a prerequisite for broader adoption in automated registry data curation. METHODS: The previous NLP algorithms developed at Mayo Clinic were deployed and refined on EHRs from OrthoCarolina, evaluating 39 randomly selected primary THA operative reports from 2018 to 2021. Operative reports were available only in PDF format, requiring conversion to "readable" text with Adobe software. Accuracy statistics were calculated against manual chart review. RESULTS: The operative approach, fixation technique, and bearing surface algorithms all demonstrated perfect accuracy of 100%. By comparison, validated performance at the developing center yielded an accuracy of 99.2% for operative approach, 90.7% for fixation technique, and 95.8% for bearing surface. CONCLUSION: NLP algorithms applied to data from an external center demonstrated excellent accuracy in delineating common elements in THA operative notes. Notably, the algorithms had no functional problems evaluating scanned PDFs that were converted to "readable" text by common software. Taken together, these findings provide promise for NLP applied to scanned PDFs as a source to develop large registries by reliably extracting data of interest from very large unstructured data sets in an expeditious and cost-effective manner.

Frank Stinchfield Award: Creation of a Patient-Specific Total Hip Arthroplasty Periprosthetic Fracture Risk Calculator.

BACKGROUND: Many risk factors have been described for periprosthetic femur fracture (PPFFx) following total hip arthroplasty (THA), yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to develop a high-dimensional, patient-specific risk-stratification nomogram that allows dynamic risk modification based on operative decisions. METHODS: We evaluated 16,696 primary nononcologic THAs performed between 1998 and 2018. During a mean 6-year follow-up, 558 patients (3.3%) sustained a PPFFx. Patients were characterized by individual natural language processing-assisted chart review on nonmodifiable factors (demographics, THA indication, and comorbidities), and modifiable operative decisions (femoral fixation [cemented/uncemented], surgical approach [direct anterior, lateral, and posterior], and implant type [collared/collarless]). Multivariable Cox regression models and nomograms were developed with PPFFx as a binary outcome at 90 days, 1 year, and 5 years, postoperatively. RESULTS: Patient-specific PPFFx risk based on comorbid profile was wide-ranging from 0.4-18% at 90 days, 0.4%-20% at 1 year, and 0.5%-25% at 5 years. Among 18 evaluated patient factors, 7 were retained in multivariable analyses. The 4 significant nonmodifiable factors included the following: women (hazard ratio (HR) = 1.6), older age (HR = 1.2 per 10 years), diagnosis of osteoporosis or use of osteoporosis medications (HR = 1.7), and indication for surgery other than osteoarthritis (HR = 2.2 for fracture, HR = 1.8 for inflammatory arthritis, HR = 1.7 for osteonecrosis). The 3 modifiable surgical factors were included as follows: uncemented femoral fixation (HR = 2.5), collarless femoral implants (HR = 1.3), and surgical approach other than direct anterior (lateral HR = 2.9, posterior HR = 1.9). CONCLUSION: This patient-specific PPFFx risk calculator demonstrated a wide-ranging risk based on comorbid profile and enables surgeons to quantify risk mitigation based on operative decisions. LEVEL OF EVIDENCE: Level III, Prognostic.

Changing Surgical Approach From Primary to Revision Total Hip Arthroplasty Is Not Associated With Increased Risk of Dislocation or Re-Revisions.

BACKGROUND: No prior studies have examined outcomes based on approach concordance between primary and revision total hip arthroplasty (THA). There is theoretical concern that performing surgery through multiple planes could potentiate dislocation risk. This study aimed to assess the impact of utilizing concordant vs discordant surgical approaches between primary and revision THA on incidence of dislocation, re-revision, reoperation, and nonoperative complications. METHODS: Between 2000 and 2018, 705 revision THAs were retrospectively identified in patients who underwent primary THA at the same academic center. Surgical approach was determined for primary and revision THA from operative notes with dislocations, re-revisions, reoperations, and complications determined from our total joint registry. Complication rates were compared between those with concordant and discordant surgical approaches. Mean age was 65 years, 50% were female, mean body mass index was 31 kg/m2, and mean follow-up was 4 years. RESULTS: Surgical approach discordance occurred in 97 cases (14%), which was more frequent when the direct anterior approach was used for primary THA (72%, P < .001) compared to lateral (12%) or posterior (10%) approaches. There were no statistically significant differences in the incidence of dislocations, re-revisions, reoperations, and nonoperative complications among those with concordant and discordant approaches for the overall cohort and when analyzed by primary approach (P > .05 for all). CONCLUSION: Comparable dislocation and complication rates were observed among revision THAs with concordant and discordant approaches between primary and revision THA. These data provide reassurance that changing vs maintaining the surgical approach from primary to revision THA does not significantly increase dislocation or re-revision risk. LEVEL OF EVIDENCE: IV.