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.

Association of socio-demographic characteristics with alcohol use initiation among never users during the COVID-19 pandemic: a longitudinal study.

BACKGROUND: In this longitudinal cohort study, we examined the socio-demographic and psychological predictors of alcohol use initiation during the COVID-19 pandemic in a sample of never alcohol users aged ≥21 prior to COVID-19. METHODS: Our study population consisted of 56 930 patients aged ≥21, as of 30 March 2019 were collected from a pre-COVID period of 1 year before 31 March 2020, and during-COVID, a period between 1 April 2020 and 30 March 2021. Univariable and multivariable logistic regression models were utilized to examine the roles of socio-demographic variables (gender, age, education, Area Deprivation Index and rural residence) changes in anxiety and depression severity as predictors of alcohol use initiation. RESULTS: Age, gender, race, ethnicity, education and rural status were significant predictors in multivariable analysis. A subgroup analysis showed neither anxiety nor depression had a significant association with alcohol use initiation. CONCLUSION: Women, younger individuals, those living in a rural area and people who smoke cigarettes were more likely to initiate alcohol use during the pandemic. Our study has public health and clinical implications such as the need for targeted alcohol use screening and intervention for vulnerable individuals.

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.

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.

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.

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 Distinguish Correlation From Causation in Orthopaedic Research.

Correlations in observational studies are commonly misinterpreted as causation. Although correlation is necessary to establish a causal relationship between two variables, correlations may also arise due to chance, reverse causality, or confounding. There are several methods available to orthopaedic researchers to determine whether the observed correlations are causal. These methods depend on the key components of the study including, but not limited to, study design and data availability on confounders. In this article, we illustrate the main concepts surrounding correlation and causation using intuitive real-world examples from the orthopaedic literature. Please visit the following https://www.youtube.com/watch?v=WW7pFudZbHA&t=52s for a video that explains the highlights of the paper in practical terms.

Nuts and Bolts of Patient-Reported Outcomes in Orthopaedics.

Patient-reported outcomes (PROs) are commonly used in orthopaedic clinical practice, comparative effectiveness research (CER), and label claims. In this paper, we provide an overview of PROs, their development, validation, and use in orthopaedic research with examples and conclude with practical guidelines for researchers and reviewers. We discuss considerations for conceptual framework, validity, reliability, factor analysis, and measurement of change with Knee Injury and Osteoarthritis Outcome score (KOOS), as an example. We also describe advantages of instruments developed based on item response theory and statistical analyses for data collected using PRO measures. Please visit the following (https://www.youtube.com/watch?v=4p-DtZgUHOA&t=354s) 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.

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.

Association of Rurality and Neighborhood Level Socioeconomic Deprivation with Perioperative Health Status in Total Joint Arthroplasty Patients: Analysis from a Large, Tertiary Care Hospital.

BACKGROUND: Individual socioeconomic status (SES) is associated with disparities in access to care and worse outcomes in total joint arthroplasty (TJA). Neighborhood-level SES measures are sometimes used as a proxy for individual-level SES, but the validity of this approach is unknown. We examined neighborhood level SES and rurality on perioperative health status in TJA. METHODS: The study population comprised 46,828 TJA surgeries performed at a tertiary care hospital. Community area deprivation index (ADI) was derived from the 2015 American Census Survey. Logistic regression was used to examine perioperative characteristics by ADI and rurality. RESULTS: Compared to patients from the least deprived neighborhoods, patients from the most deprived neighborhoods were likely to be female (odds ratioOR 1.46, 95% confidence interval CI: 1.33-1.61), non-white (OR 1.36, 95% CI: 1.13-1.64), with education high school or less (OR 4.85, 95% CI: 4.35-5.41), be current smokers (OR 2.20, 95% CI: 1.61-2.49), have BMI>30 kg/m2 (OR 1.43, 95% CI: 1.30-1.57), more limitation on instrumental activities of daily living (OR 1.75, 95% CI: 1.55-1.97) and American Society of Anesthesiologists (ASA) score > II (OR 2.0, 95% CI: 1.11-1.37). There was a progressive association between the degree of area level deprivation with preexisting comorbidities. Patients from rural communities were more likely to be male, white, have body mass index (BMI)>30 kg/m2 and lower education levels. However, rurality was either not associated or negatively associated with comorbidities. CONCLUSION: TJA patients from lower SES neighborhoods have worse behavioral risk factors and higher comorbidity burden than patients from higher SES neighborhoods. Patients from rural communities have worse behavioral risk factors but not comorbidities.

P-Values and Power in Orthopedic Research: Myths and Reality.

The results of statistical tests in orthopedic studies are typically reported using P-values. If a P-value is smaller than the pre-determined level of significance (eg, < .05), the null hypothesis is rejected in support of the alternative. This automaticity in interpreting statistical results without consideration of the power of the study has been denounced over the years by statisticians, since it can potentially lead to misinterpretation of the study conclusions. In this paper, we review fundamental misconceptions and misinterpretations of P-values and power, along with their connection with confidence intervals, and we provide guidelines to orthopedic researchers for evaluating and reporting study results. We provide real-world orthopedic examples to illustrate the main concepts. Please visit the followinghttps://youtu.be/bdPU4luYmF0for videos that explain the highlights of the paper in practical terms.

Study Types in Orthopaedics Research: Is My Study Design Appropriate for the Research Question?

When performing orthopaedic clinical research, alternative study designs can be more appropriate depending on the research question, availability of data, and feasibility. The most common observational study designs in total joint arthroplasty research are cohort and cross-sectional studies. This article describes methodological considerations for different study designs with examples from the total joint arthroplasty literature. We highlight the advantages and feasibility of experimental and observational study designs using real-world examples. We illustrate how to avoid common mistakes, such as incorrect labeling of matched cohort studies as case-control studies. We further guide investigators through a step-by-step design of a case-control study. We conclude with considerations when choosing between alternative study designs. Please visit the followinghttps://youtu.be/Zvce61cMYi8for videos that explain the highlights of the article in practical terms.

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.

Measurement Error and Misclassification in Orthopedics: When Study Subjects are Categorized in the Wrong Exposure or Outcome Groups.

Datasets available for orthopedic research often contain measurement and misclassification errors due to errors in data collection or missing data. These errors can have different effects on the study results. Measurement error refers to inaccurate measurement of continuous variables (eg, body mass index), whereas misclassification refers to assigning subjects in the wrong exposure and/or outcome groups (eg, obesity categories). Misclassification of any type can result in underestimation or overestimation of the association between exposures and outcomes. In this article, we offer practical guidelines to avoid, identify, and account for measurement and misclassification errors. We also provide an illustrative example on how to perform a validation study to address misclassification based on real-world orthopedic data. Please visit the followinghttps://youtu.be/9-ekW2NnWrsor videos that explain the highlights of the article in practical terms.

Distribution and Correlates of Hip-Knee-Ankle Angle in Early Osteoarthritis and Preoperative Total Knee Arthroplasty Patients.

BACKGROUND: Several studies have investigated the distribution of hip-knee-ankle (HKA) angle in healthy populations; however, few have evaluated this metric in patients undergoing total knee arthroplasty (TKA). The purpose of this study is to compare HKA angle distribution in early and advanced knee osteoarthritis (OA) patients. METHODS: Full limb radiographs were used to measure HKA angle for 983 subjects from the Osteoarthritis Initiative (OAI) cohort and 4,901 pre-TKA patients from an institutional cohort. Measurements were made using a previously validated deep learning algorithm. Linear regression models were used to determine the association of HKA alignment angle with patient characteristics. RESULTS: The mean ± standard deviation HKA angle was -1.3° ± 3.2° in the OAI cohort and -4.1° ± 6.1° in the pre-TKA cohort. In the OAI cohort, normal alignment (64%) was the most common knee alignment followed by varus (29%), and valgus (7%). In pre-TKA patients, the most common alignment was varus (62%), followed by normal (27%) and valgus (11%). In pre-TKA patients, mean HKA angle in primary knee OA, post-traumatic knee OA, and rheumatoid arthritis patients were -4.3° ± 6.1°, -3.2° ± 6.4°, and -2.9° ± 6.1°, respectively. HKA angle was strongly associated (P < .001) with gender and body mass index. CONCLUSION: TKA patients have a wider alignment distribution and more severe varus and valgus alignment than individuals "at risk" for knee OA from the OAI cohort. These epidemiologic findings improve our understanding of HKA angle distribution and its correlation with demographic characteristics in early and late-stage arthritis.

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.

Immortal Time Bias in the Analysis of Time-to-Event Data in Orthopedics.

Many outcomes in arthroplasty research are analyzed as time-to-event outcomes using survival analysis methods. When comparison groups are defined after a time-delayed exposure or intervention, a period of immortal time arises and can lead to biased results. In orthopedics research, immortal time bias often arises when a minimum amount of follow-up is required for study inclusion or when comparing outcomes in staged bilateral vs unilateral arthroplasty patients. We present an explanation of immortal time and the associated bias, describe how to correctly account for it using proper data preparation and statistical techniques, and provide an illustrative example using real-world arthroplasty data. We offer practical guidelines for identifying and properly handling immortal time to avoid bias. Please visit the followinghttps://youtu.be/58p8w5o-ci4for a video that explains the highlights of the paper in practical terms.