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.

Fracture Risk Among Living Kidney Donors 25 Years After Donation.

Importance: Living kidney donors may have an increased risk of fractures due to reductions in kidney mass, lower concentrations of serum 1,25-dihydroxyvitamin D, and secondary increases in serum parathyroid hormone. Objective: To compare the overall and site-specific risk of fractures among living kidney donors with strictly matched controls from the general population who would have been eligible to donate a kidney but did not do so. Design, Setting, and Participants: This survey study was conducted between December 1, 2021, and July 31, 2023. A total of 5065 living kidney donors from 3 large transplant centers in Minnesota were invited to complete a survey about their bone health and history of fractures, and 16 156 population-based nondonor controls without a history of comorbidities that would have precluded kidney donation were identified from the Rochester Epidemiology Project and completed the same survey. A total of 2132 living kidney donors and 2014 nondonor controls responded to the survey. Statistical analyses were performed from May to August 2023. Exposure: Living kidney donation. Main Outcomes and Measures: The rates of overall and site-specific fractures were compared between living kidney donors and controls using standardized incidence ratios (SIRs). Results: At the time of survey, the 2132 living kidney donors had a mean (SD) age of 67.1 (8.9) years and included 1245 women (58.4%), and the 2014 controls had a mean (SD) age of 68.6 (7.9) years and included 1140 women (56.6%). The mean (SD) time between donation or index date and survey date was 24.2 (10.4) years for donors and 27.6 (10.7) years for controls. The overall rate of fractures among living kidney donors was significantly lower than among controls (SIR, 0.89; 95% CI, 0.81-0.97). However, there were significantly more vertebral fractures among living kidney donors than among controls (SIR, 1.42; 95% CI, 1.05-1.83). Conclusions and Relevance: This survey study found a reduced rate of overall fractures but an excess of vertebral fractures among living kidney donors compared with controls after a mean follow-up of 25 years. Treatment of excess vertebral fractures with dietary supplements such as vitamin D3 may reduce the numbers of vertebral fractures and patient morbidity.

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence.

The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.

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.

Correlation of Blood Metal Concentrations with Cognitive Scores and Neuroimaging Findings in Patients with Total Joint Arthroplasty.

Total joint arthroplasty (TJA) implants are composed of metals, ceramics, and/or polyethylene. Studies suggest that the debris released from metal implants may possess neurotoxic properties with reports of neuropsychiatric symptoms and memory deficits, which could be relevant to Alzheimer's disease and related dementias. This exploratory study examined the cross-sectional correlation of blood metal concentrations with cognitive performance and neuroimaging findings in a convenience sample of 113 TJA patients with history of elevated blood metal concentrations of either titanium, cobalt and/or chromium. Associations with neuroimaging measures were observed but not with cognitive scores. Larger studies with longitudinal follow-up are warranted.

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.

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.

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.

Cross-sectional association between systemic metal concentrations and immune markers in patients with total joint arthroplasty.

Total joint arthroplasty (TJA) implants are composed of metal components. Although they are regarded safe, the long-term immunological effects of chronic exposure to the specific implant materials are unknown. We recruited 115 hip and/or knee TJA patients (mean age 68 years) who provided a blood draw for measurement of chromium, cobalt, titanium concentrations, inflammatory markers and systemic distribution of immune cells. We examined differences between the immune markers and the systemic concentrations of chromium, cobalt and titanium. CD66-b neutrophils, early natural killer cells (NK), and eosinophils were present in higher percentages in patients with chromium and cobalt concentrations greater than the median. The opposite pattern was observed with titanium where the percentages of CD66-b neutrophils, early NK, and eosinophils were higher in patients with undetectable titanium. Cobalt concentrations were positively correlated with a higher percentage of gamma delta T cells. Both chromium and cobalt concentrations were positively correlated with higher percentages of plasmablasts. Titanium concentrations were positively correlated with higher CD4 effector memory T cells, regulatory T cell count and Th1 CD4 helper cells. In this exploratory study, we observed altered distribution of immune cells in TJA patients with elevated systemic metal concentrations. Although these correlations were not strong, these exploratory findings warrant further investigation into the role of increased metals circulating in blood and its role in immune modulation.

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.

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.

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.

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.

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.

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.