Multimorbidity in Psoriasis as a Risk Factor for Psoriatic Arthritis: A Population-Based Study.

OBJECTIVES: To examine multimorbidity in psoriasis and its association with the development of PsA. METHODS: A retrospective cohort study was performed using the Rochester Epidemiology Project. Population-based incidence (2000-2009) and prevalence (Jan 1, 2010) cohorts of psoriasis were identified by manual chart review. A cohort of individuals without psoriasis (comparators) were identified (1:1 matched on age, sex, and county). Morbidities were defined using ≥2 Clinical Classification Software codes ≥30 days apart within prior five years. PsA was defined using ClASsification of Psoriatic ARthritis (CASPAR) criteria. χ2 and rank-sum tests were used to compare morbidities, and age-, sex-, and race-adjusted Cox models to examine the association of baseline morbidities in psoriasis with development of PsA. RESULTS: Among 817 incident psoriasis patients, the mean age was 45.2 years with 52.0% females, and 82.0% moderate/severe psoriasis. No multimorbidity differences were found between incident psoriasis patients and comparators. However, in the 1,088 prevalent psoriasis patients, multimorbidity was significantly more common compared with 1,086 comparators (OR : 1.35 and OR : 1.48 for ≥2 and ≥5 morbidities, respectively). Over a median 13.3-year follow-up, 23 patients (cumulative incidence: 2.9% by 15 years) developed PsA. Multimorbidity (≥2 morbidities) was associated with a 3-fold higher risk of developing PsA. CONCLUSION: Multimorbidity was more common in the prevalent but not incident cohort of psoriasis compared with the general population, suggesting patients with psoriasis may experience accelerated development of multimorbidity. Moreover, multimorbidity at psoriasis onset significantly increased the risk of developing PsA, highlighting the importance of monitoring multimorbid psoriasis patients for the development of PsA.

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.

Comparative Survival of Contemporary Cementless Acetabular Components Following Revision Total Hip Arthroplasty.

BACKGROUND: The advent of highly porous ingrowth surfaces and highly crosslinked polyethylene has been expected to improve implant survivorship in revision total hip arthroplasty. Therefore, we sought to evaluate the survival of several contemporary acetabular designs following revision total hip arthroplasty. METHODS: Acetabular revisions performed from 2000 to 2019 were identified from our institutional total joint registry. We studied 3,348 revision hips, implanted with 1 of 7 cementless acetabular designs. These were paired with highly crosslinked polyethylene or dual-mobility liners. A historical series of 258 Harris-Galante-1 components, paired with conventional polyethylene, was used as reference. Survivorship analyses were performed. For the 2,976 hips with minimum 2-year follow-up, the median follow-up was 8 years (range, 2 to 35 years). RESULTS: Contemporary components with adequate follow-up had survivorship free of acetabular rerevision of ≥95% at 10-year follow-up. Relative to Harris-Galante-1 components, 10-year survivorship free of all-cause acetabular cup rerevision was significantly higher in Zimmer Trabecular Metarevision (hazard ratio (HR) 0.3, 95% confidence interval (CI) 0.2-0.45), Zimmer Trabecular MetaModular (HR 0.34, 95% CI 0.13-0.89), Zimmer Trilogy (HR 0.4, 95% CI 0.24-0.69), DePuy Pinnacle Porocoat (HR 0.24, 95% CI 0.11-0.51), and Stryker Tritanium revision (HR 0.46, 95% CI 0.24-0.91) shells. Among contemporary components, there were only 23 rerevisions for acetabular aseptic loosening and no rerevisions for polyethylene wear. CONCLUSION: Contemporary acetabular ingrowth and bearing surfaces were associated with no rerevisions for wear and aseptic loosening was uncommon, particularly with highly porous designs. Therefore, it appears that contemporary revision acetabular components have dramatically improved upon historical results at available follow-up.

Depression and anxiety are associated with an increased risk of infection, revision, and reoperation following total hip or knee arthroplasty.

The aim of this study was to determine the prevalence of depressive and anxiety disorders prior to total hip (THA) and total knee arthroplasty (TKA) and to assess their impact on the rates of any infection, revision, or reoperation. Between January 2000 and March 2019, 21,469 primary and revision arthroplasties (10,011 THAs; 11,458 TKAs), which were undertaken in 15,504 patients at a single academic medical centre, were identified from a 27-county linked electronic medical record (EMR) system. Depressive and anxiety disorders were identified by diagnoses in the EMR or by using a natural language processing program with subsequent validation from review of the medical records. Patients with mental health diagnoses other than anxiety or depression were excluded. Depressive and/or anxiety disorders were common before THA and TKA, with a prevalence of 30% in those who underwent primary THA, 33% in those who underwent revision THA, 32% in those who underwent primary TKA, and 35% in those who underwent revision TKA. The presence of depressive or anxiety disorders was associated with a significantly increased risk of any infection (primary THA, hazard ratio (HR) 1.5; revision THA, HR 1.9; primary TKA, HR 1.6; revision TKA, HR 1.8), revision (THA, HR 1.7; TKA, HR 1.6), re-revision (THA, HR 2.0; TKA, HR 1.6), and reoperation (primary THA, HR 1.6; revision THA, HR 2.2; primary TKA, HR 1.4; revision TKA, HR 1.9; p < 0.03 for all). Patients with preoperative depressive and/or anxiety disorders were significantly less likely to report "much better" joint function after primary THA (78% vs 87%) and primary TKA (86% vs 90%) compared with those without these disorders at two years postoperatively (p < 0.001 for all). The presence of depressive or anxiety disorders prior to primary or revision THA and TKA is common, and associated with a significantly higher risk of infection, revision, reoperation, and dissatisfaction. This topic deserves further study, and surgeons may consider mental health optimization to be of similar importance to preoperative variables such as diabetic control, prior to arthroplasty.

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.

Is the American Joint Replacement Registry Able to Correctly Classify Revision Total Knee Arthroplasty Procedural Diagnoses?

BACKGROUND: The American Joint Replacement Registry (AJRR) is a powerful tool for the study of revision total knee arthroplasty (rTKA). The AJRR uses International Classification of Diseases-10 (ICD-10-CM) codes for recording surgical diagnoses. However, the validity of this methodology is unknown. The purpose of this study was to determine the accuracy of ICD-10-CM codes, as used by AJRR, in classifying rTKA diagnoses. METHODS: There were 988 rTKAs performed from 2015 to 2021 identified in our institutional total joint registry (TJR). Revision diagnoses were obtained from TJR, in which trained abstractors prospectively record diagnoses independent of ICD-10-CM data. The ICD-10-CM diagnosis codes submitted to AJRR were retrieved for the same procedures. The accuracy of ICD-10-CM codes for classifying rTKA diagnoses as septic versus aseptic, aseptic loosening, instability, and periprosthetic fracture was assessed using Cohen's Kappa statistics, sensitivities, and specificities. RESULTS: Concordance between AJRR-submitted codes and TJR was excellent (97.3%, k = 0.9) for identifying septic versus aseptic revisions. Agreement for aseptic diagnoses varied from very good for loosening (k = 0.65) and instability (k = 0.64) to fair for periprosthetic fracture (k = 0.36). Specificity was high (> 94%) for all three diagnoses, but sensitivity was lower at 71%, 63%, and 28% for loosening, instability, and periprosthetic fracture, respectively. CONCLUSION: The AJRR submitted ICD-10-CM diagnosis codes correctly classified rTKA cases as septic or aseptic with remarkable accuracy, but accuracy for more granular diagnoses varied. These data demonstrate the potential for diagnosis-specific limitations when using administrative claims data for registry reporting and have important implications for researchers using ICD-10-CM data.

Propensity Scores: Confounder Adjustment When Comparing Nonrandomized Groups in Orthopaedic Surgery.

Many studies in arthroplasty research are based on nonrandomized, retrospective, registry-based cohorts. In these types of studies, patients belonging to different treatment or exposure groups often differ with respect to patient characteristics, medical histories, surgical indications, or other factors. Consequently, comparisons of nonrandomized groups are often subject to treatment selection bias and confounding. Propensity scores can be used to balance cohort characteristics, thus helping to minimize potential bias and confounding. This article explains how propensity scores are created and describes multiple ways in which they can be applied in the analysis of nonrandomized studies. Please visit the following (https://www.youtube.com/watch?v=sqgxl_nZWS4&t=3s) for a video that explains the highlights of the paper in practical terms.

How to Develop and Validate Prediction Models for Orthopedic Outcomes.

Prediction models are common in medicine for predicting outcomes such as mortality, complications, or response to treatment. Despite the growing interest in these models in arthroplasty (and orthopaedics in general), few have been adopted in clinical practice. If robustly built and validated, prediction models can be excellent tools to support surgical decision making. In this paper, we provide an overview of the statistical concepts surrounding prediction models and outline practical steps for prediction model development and validation in arthroplasty research. Please visit the followinghttps://www.youtube.com/watch?v=9Yrit23Rkicfor a video that explains the highlights of the paper in practical terms.

Patient-specific Hip Arthroplasty Dislocation Risk Calculator: An Explainable Multimodal Machine Learning-based Approach.

Purpose: To develop a multimodal machine learning-based pipeline to predict patient-specific risk of dislocation following primary total hip arthroplasty (THA). Materials and Methods: This study retrospectively evaluated 17 073 patients who underwent primary THA between 1998 and 2018. A test set of 1718 patients was held out. A hybrid network of EfficientNet-B4 and Swin-B transformer was developed to classify patients according to 5-year dislocation outcomes from preoperative anteroposterior pelvic radiographs and clinical characteristics (demographics, comorbidities, and surgical characteristics). The most informative imaging features, extracted by the mentioned model, were selected and concatenated with clinical features. A collection of these features was then used to train a multimodal survival XGBoost model to predict the individualized hazard of dislocation within 5 years. C index was used to evaluate the multimodal survival model on the test set and compare it with another clinical-only model trained only on clinical data. Shapley additive explanation values were used for model explanation. Results: The study sample had a median age of 65 years (IQR: 18 years; 52.1% [8889] women) with a 5-year dislocation incidence of 2%. On the holdout test set, the clinical-only model achieved a C index of 0.64 (95% CI: 0.60, 0.68). The addition of imaging features boosted multimodal model performance to a C index of 0.74 (95% CI: 0.69, 0.78; P = .02). Conclusion: Due to its discrimination ability and explainability, this risk calculator can be a potential powerful dislocation risk stratification and THA planning tool.Keywords: Conventional Radiography, Surgery, Skeletal-Appendicular, Hip, Outcomes Analysis, Supervised Learning, Convolutional Neural Network (CNN), Gradient Boosting Machines (GBM) Supplemental material is available for this article. © RSNA, 2022.

Long-term Cognitive Trajectory After Total Joint Arthroplasty.

Importance: Individuals with total joint arthroplasty (TJA) have long-term exposure to metal-containing implants; however, whether long-term exposure to artificial implants is associated with cognitive function is unknown. Objective: To compare long-term cognitive trajectories in individuals with and without TJA. Design, Setting, and Participants: This population-based cohort study assessed serial cognitive evaluations of 5550 participants (≥50 years of age) from the Mayo Clinic Study of Aging between November 1, 2004, and December 31, 2020. Exposures: Total joint arthroplasty of the hip or the knee. Main Outcomes and Measures: Linear mixed-effects models were used to compare the annualized rate of change in global and domain-specific cognitive scores in participants with and without TJA, adjusting for age, sex, educational level, apolipoprotein E ε4 carrier status, and cognitive test practice effects. Results: A total of 5550 participants (mean [SD] age at baseline, 73.04 [10.02] years; 2830 [51.0%] male) were evaluated. A total of 952 participants had undergone at least 1 TJA of the hip (THA, n = 430) or the knee (TKA, n = 626) before or after entry into the cohort. Participants with TJA were older, more likely to be female, and had a higher body mass index than participants without TJA. No difference was observed in the rate of cognitive decline in participants with and without TJA until 80 years of age. A slightly faster cognitive decline at 80 years or older and more than 8 years from surgery was observed (b = -0.03; 95% CI, -0.04 to -0.02). In stratified analyses by surgery type, the faster decline was observed primarily among older participants with TKA (b = -0.04; 95% CI, -0.06 to -0.02). Conclusions and Relevance: In this cohort study, long-term cognitive trajectories in individuals with and without TJA were largely similar except for a slightly faster decline among the oldest patients with TKA; however, the magnitude of difference was small and of unknown clinical significance.

Avoiding Systematic Bias in Orthopedics Research Through Informed Variable Selection: A Discussion of Confounders, Mediators, and Colliders.

There are 3 common variable types in orthopedic research-confounders, colliders, and mediators. All 3 types of variables are associated with both the exposure (eg, surgery type, implant type, body mass index) and outcome (eg, complications, revision surgery) but differ in their temporal ordering. To reduce systematic bias, the decision to include or exclude a variable in an analysis should be based on the variable's relationship with the exposure and outcome for each research question. In this article, we define 3 types of variables with case examples from orthopedic research. Please visit the followinghttps://youtu.be/V-grpgB1ShQfor videos that explain the highlights of the article in practical terms.

Creation of a Total Hip Arthroplasty Patient-Specific Dislocation Risk Calculator.

BACKGROUND: Many risk factors have been described for dislocation 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: In this study, 29,349 THAs, including 21,978 primary and 7371 revision cases, performed between 1998 and 2018 were evaluated. During a mean 6-year follow-up, 1521 THAs were followed by a dislocation. Patients were characterized, through individual-chart review, according to non-modifiable factors (demographics, indication for THA, spine disease, prior spine surgery, and neurologic disease) and modifiable operative decisions (operative approach, femoral head diameter, and type of acetabular liner [standard, elevated, constrained, or dual-mobility]). Multivariable regression models and nomograms were developed with dislocation as a binary outcome at 1 year and 5 years postoperatively. RESULTS: Dislocation risk, based on patient-specific comorbidities and operative decisions, was wide-ranging-from 0.3% to 13% at 1 year and from 0.4% to 19% at 5 years after primary THA, and from 2% to 32% at 1 year and from 3% to 42% at 5 years after revision THA. In the primary-THA group, the direct anterior approach (hazard ratio [HR] = 0.27) and lateral approach (HR = 0.58) decreased the dislocation risk compared with the posterior approach. After adjusting for the approach in that group, the combination of a ≥36-mm-diameter femoral head and an elevated liner yielded the largest decrease in dislocation risk (HR = 0.28), followed by dual-mobility constructs (HR = 0.48). In the patients who underwent revision THA, the adjusted risk of dislocation was most markedly decreased by the use of a dual-mobility construct (HR = 0.40), followed by a ≥36-mm femoral head and an elevated liner (HR = 0.88). The adjusted risk of dislocation after revision THA was decreased by acetabular revision (HR = 0.58), irrespective of whether other components were revised. CONCLUSIONS: Our patient-specific dislocation risk calculator, which was strengthened by our use of a robust multivariable model that accounted for comorbidities associated with instability, demonstrated wide-ranging patient-specific risks based on comorbidity profiles. The resultant nomograms can be used as a screening tool to identify patients at high risk for dislocation following THA and to individualize operative decisions for evidence-based risk mitigation. LEVEL OF EVIDENCE: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

Neighborhood-Level Socioeconomic Deprivation, Rurality, and Long-Term Outcomes of Patients Undergoing Total Joint Arthroplasty: Analysis from a Large, Tertiary Care Hospital.

Objective: To assess the impact of neighborhood-level socioeconomic status factors (area deprivation index [ADI] and rural classification) and their interaction with individual-level socioeconomic status (education-level) on long-term outcomes following total joint arthroplasty (TJA) surgery. Patients and Methods: This was a cohort study of 46,828 TJA surgeries performed on patients at a tertiary care hospital between January 1, 2000 and December 31, 2019. Cox proportional hazards models were used to examine the association between ADI and rurality and their interaction with individual-level education on the risk of periprosthetic joint infections, revision surgery, and mortality. Results: At the time of surgery, 2589 (6%) patients lived in the most deprived neighborhoods (ADI quintile >80%) and 10,728 (23%) lived in small isolated rural towns. Patients from the most deprived neighborhoods were more likely to experience revision surgery (hazard ratio, [HR], 1.39; 95% CI, 1.10-1.76) and mortality (HR, 1.24; 95% CI, 1.09-1.42). Patients from small rural towns were also more likely to undergo revision surgery (HR, 1.14; 95% CI, 1.01-1.28). The mortality risk was 13%, 18%, and 24% higher for patients in the 3 highest ADI quintiles than those from the lowest quintile. Education gradient was more notable in the least deprived neighborhoods than in the most deprived neighborhoods. Conclusion: Neighborhood disadvantage and rurality are negatively associated with the risk of revision surgery and both independently and in interaction with individual-level education with the risk of mortality. There is a need for population-level health interventions to mitigate area-based socioeconomic disadvantages in TJA.

Applying Deep Learning to Establish a Total Hip Arthroplasty Radiography Registry: A Stepwise Approach.

BACKGROUND: Establishing imaging registries for large patient cohorts is challenging because manual labeling is tedious and relying solely on DICOM (digital imaging and communications in medicine) metadata can result in errors. We endeavored to establish an automated hip and pelvic radiography registry of total hip arthroplasty (THA) patients by utilizing deep-learning pipelines. The aims of the study were (1) to utilize these automated pipelines to identify all pelvic and hip radiographs with appropriate annotation of laterality and presence or absence of implants, and (2) to automatically measure acetabular component inclination and version for THA images. METHODS: We retrospectively retrieved 846,988 hip and pelvic radiography DICOM files from 20,378 patients who underwent primary or revision THA performed at our institution from 2000 to 2020. Metadata for the files were screened followed by extraction of imaging data. Two deep-learning algorithms (an EfficientNetB3 classifier and a YOLOv5 object detector) were developed to automatically determine the radiographic appearance of all files. Additional deep-learning algorithms were utilized to automatically measure the acetabular angles on anteroposterior pelvic and lateral hip radiographs. Algorithm performance was compared with that of human annotators on a random test sample of 5,000 radiographs. RESULTS: Deep-learning algorithms enabled appropriate exclusion of 209,332 DICOM files (24.7%) as misclassified non-hip/pelvic radiographs or having corrupted pixel data. The final registry was automatically curated and annotated in <8 hours and included 168,551 anteroposterior pelvic, 176,890 anteroposterior hip, 174,637 lateral hip, and 117,578 oblique hip radiographs. The algorithms achieved 99.9% accuracy, 99.6% precision, 99.5% recall, and a 99.6% F1 score in determining the radiograph appearance. CONCLUSIONS: We developed a highly accurate series of deep-learning algorithms to rapidly curate and annotate THA patient radiographs. This efficient pipeline can be utilized by other institutions or registries to construct radiography databases for patient care, longitudinal surveillance, and large-scale research. The stepwise approach for establishing a radiography registry can further be utilized as a workflow guide for other anatomic areas. LEVEL OF EVIDENCE: Diagnostic Level IV . See Instructions for Authors for a complete description of levels of evidence.

Redefining the 3D Topography of the Acetabular Safe Zone: A Multivariable Study Evaluating Prosthetic Hip Stability.

BACKGROUND: Dislocation is the most common reason for early revision following total hip arthroplasty (THA). More than 40 years ago, Lewinnek et al. proposed an acetabular "safe zone" to avoid dislocation. While novel at the time, their study was substantially limited according to modern standards. The purpose of this study was to determine optimal acetabular cup positioning during THA as well as the effect of surgical approach on the topography of the acetabular safe zone and the hazard of dislocation. METHODS: Primary THAs that had been performed at a single institution from 2000 to 2017 were reviewed. Acetabular inclination and anteversion were measured using an artificial intelligence neural network; they were validated with performance testing and comparison with blinded grading by 2 orthopaedic surgeons. Patient demographics and dislocation were noted during follow-up. Multivariable Cox proportional-hazards regression, including multidimensional analysis, was performed to define the 3D topography of the acetabular safe zone and its association with surgical approach. RESULTS: We followed 9,907 THAs in 8,081 patients (4,166 women and 3,915 men; 64 ± 13 years of age) for a mean of 5 ± 3 years (range: 2 to 16); 316 hips (3%) sustained a dislocation during follow-up. The mean acetabular inclination was 44° ± 7° and the mean anteversion was 32° ± 9°. Patients who did not sustain a dislocation had a mean anteversion of 32° ± 9° (median, 32°), with the historic ideal anteversion of 15° observed to be only in the third percentile among non-dislocating THAs (p < 0.001). Multivariable modeling demonstrated the lowest dislocation hazards at an inclination of 37° and an anteversion of 27°, with an ideal modern safe zone of 27° to 47° of inclination and 18° to 38° of anteversion. Three-dimensional analysis demonstrated a similar safe-zone location but significantly different safe-zone topography among surgical approaches (p = 0.03) and sexes (p = 0.02). CONCLUSIONS: Optimal acetabular positioning differs significantly from historic values, with increased anteversion providing decreased dislocation risk. Additionally, surgical approach and patient sex demonstrated clear effects on 3D safe-zone topography. Further study is needed to characterize the 3D interaction between acetabular positioning and spinopelvic as well as femoral-sided parameters. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Analysis of patient characteristics and outcomes related to distance traveled to a tertiary center for primary reverse shoulder arthroplasty.

INTRODUCTION: The reasons for referral and travel patterns are lacking for patients undergoing reverse shoulder arthroplasty (RSA). The purpose of this study was to compare comorbidities, surgical time, cost and complications between local and distant primary RSA patients. METHODS: Between 2007 and 2015, 1,666 primary RSAs were performed at our institution. Patients were divided into two cohorts, local patients (from within Olmstead county and surrounding counties, 492 RSAs) and those from a distance (1,174 RSAs). RESULTS: Local patients were older (74 vs 71 years, p < .001), more likely to have RSA for fracture, had a higher Charlson comorbidity score (3.8 vs 3.2, p < .001) and longer hospital stays (2.0 vs 1.8 days, p < 0.001) compared to referred patients. Referral patients required longer operative times (95 vs 88 min, p = .002), had higher hospitalization costs ($19,101 vs $18,735, p < .001), and had a higher rate of prior surgery (32% vs 24%, p < .001). There were no differences between cohorts regarding complications or need for reoperation. CONCLUSIONS: Patients traveling from a distance to undergo primary RSA had longer operative times and were more likely to have had prior surgery than local patients. This may demonstrate the referral bias seen at large academic centers and should be considered when reviewing RSA outcomes, hospital performance, and calculating insurance reimbursement. LEVEL OF EVIDENCE: Level IV.

Primary and Revision Total Knee Arthroplasty in Patients With Pulmonary Hypertension: High Perioperative Mortality and Complications.

BACKGROUND: Although perioperative medical management during total knee arthroplasty (TKA) has improved, there is limited literature characterizing outcomes of patients with pulmonary hypertension (HTN). This study examined mortality, medical complications, implant survivorship, and clinical outcomes in this medically complex cohort. METHODS: We identified 887 patients with pulmonary HTN who underwent 881 primary TKAs and 228 revision TKAs from 2000 to 2016 at a tertiary care center. Patients were followed up at regular intervals until death, revision surgery, or last clinical follow-up. Perioperative medical complications were individually reviewed. The risk of death was examined by calculating standardized mortality ratios and Cox proportional hazards regression models. Cumulative incidence analysis was used for reporting mortality, revision, and reoperation with death as a competing risk. RESULTS: The 90-day mortality was 0.7% and 4.8% for primary and revision TKAs, respectively. The risk of death was 2-fold higher compared to primary (hazard ratio 2.54, 95% confidence interval [CI] 2.12-3.05) and revision (hazard ratio 2.16, 95% CI 1.78-2.62) TKA patients without pulmonary HTN. Rate of medical complications within 90 days from surgery was 6.5% and 14% in primary and revision TKAs. The 10-year cumulative incidence of any revision was 5% and 16% in primaries and revisions, respectively. CONCLUSION: Patients with pulmonary HTN undergoing primary and revision TKAs had excess risk of death and experience a high rate of medical complications within 90 days of surgery. Counseling of risks, medical optimization, and referral to tertiary centers should be considered. LEVEL OF EVIDENCE: Level IV.

Competing Risk Analysis: What Does It Mean and When Do We Need It in Orthopedics Research?

Most orthopedic studies involve survival analysis examining the time to an event of interest, such as a specific complication or revision surgery. Competing risks commonly arise in such studies when patients are at risk of more than one mutually exclusive event, such as death, or when the rate of an event depends on the rates of other competing events. In this article, we briefly describe the survival analysis censoring methodology, common fatal and nonfatal competing events, and define circumstances where standard survival analysis can fail in the setting of competing risks with real-world examples from orthopedics. Please visit the followinghttps://youtu.be/ifj_Mm3eGu8for a video that explains the highlights of the paper in practical terms.

Living With Survival Analysis in Orthopedics.

Time to event data occur commonly in orthopedics research and require special methods that are often called "survival analysis." These data are complex because both a follow-up time and an event indicator are needed to correctly describe the occurrence of the outcome of interest. Common pitfalls in analyzing time to event data include using methods designed for binary outcomes, failing to check proportional hazards, ignoring competing risks, and introducing immortal time bias by using future information. This article describes the concepts involved in time to event analyses as well as how to avoid common statistical pitfalls. Please visit the followinghttps://youtu.be/QNETrx8B6IUandhttps://youtu.be/8SBoTr9Jy1Qfor videos that explain the highlights of the paper in practical terms.

The Epidemiology of Psoriatic Arthritis Over Five Decades: A Population-Based Study.

OBJECTIVE: To determine the incidence of psoriatic arthritis (PsA) in a US population and describe trends in incidence and mortality over 5 decades. METHODS: The previously identified population-based cohort that included Olmsted County, Minnesota residents ≥18 years of age who fulfilled PsA criteria during 1970-1999 was extended to include patients with incident PsA during 2000-2017. Age- and sex-specific incidence rates and point prevalence, adjusted to the 2010 US White population, were reported. RESULTS: There were 164 incident cases of PsA in 2000-2017 (mean ± SD age 46.4 ± 12.0 years; 47% female). The overall age- and sex-adjusted annual incidence of PsA per 100,000 population was 8.5 (95% confidence interval [95% CI] 7.2-9.8) and was higher in men (9.3 [95% CI 7.4-11.3]) than women (7.7 [95% CI 5.9-9.4]) in 2000-2017. Overall incidence was highest in the 40-59 years age group. The incidence rate was relatively stable during 2000-2017, with no evidence of an overall increase or an increase in men only (but a modest increase of 3% per year in women), compared to 1970-1999 when a 4%-per-year increase in incidence was observed. Point prevalence was 181.8 per 100,000 population (95% CI 156.5-207.1) in 2015. The percentage of women among those with PsA increased from 39% in 1970-1999 and 41% in 2000-2009 to 54% in 2010-2017 (P = 0.08). Overall survival in PsA did not differ from the general population (standardized mortality ratio 0.85 [95% CI 0.61-1.15]). CONCLUSION: The incidence of PsA in this predominantly White US population was stable in 2000-2017, in contrast to previous years. However, an increasing proportion of women with PsA was found in this study.