Citations

Background: When missing data are present in clinical outcomes studies, complete-case analysis (CCA) is often performed, whereby patients with missing data are excluded. While simple, CCA analysis may impart selection bias and reduce statistical power, leading to erroneous statistical results in some cases. However, there exist more rigorous statistical approaches, such as single and multiple imputation, which approximate the associations that would have been present in a full dataset and preserve the study’s power. The purpose of this study is to evaluate how statistical results differ when performed after CCA analysis versus imputation methods. Methods: This simulation study analyzed a sample dataset consisting of 2204 shoulders, with complete datapoints from a larger multicenter total shoulder arthroplasty database. From the sampled dataset of demographics, surgical characteristics, and clinical outcomes, we created five test datasets, ranging from 100 to 2000 shoulders, and simulated 10–50% missingness in the postoperative American Shoulder and Elbow Surgeons (ASES) score and range of motion in four planes in missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) patterns. Missingness in outcomes was remedied using CCA, three single imputation techniques, and two multiple imputation techniques. The imputation performance was evaluated relative to the native complete dataset using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). We also compared the mean and standard deviation (SD) of the postoperative ASES score and the results of multivariable linear and logistic regression to understand the effects of imputation on the study results. Results: The average overall RMSE and MAPE were similar for MCAR (22.6 and 27.2%) and MAR (19.2 and 17.7%) missingness patterns, but were substantially poorer for NMAR (37.5 and 79.2%); the sample size and the percentage of data missingness minimally affected RMSE and MAPE. Aggregated mean postoperative ASES scores were within 5% of the true value when missing data were remedied with CCA, and all candidate imputation methods for nearly all ranges of sample size and data missingness when data were MCAR or MAR, but not when data were NMAR. When data were MAR, CCA resulted in overestimates of the SD. When data were MCAR or MAR, the accuracy of the regression estimate (β or OR) and its corresponding 95% CI varied substantially based on the sample size and proportion of missing data for multivariable linear regression, but not logistic regression. When data were MAR, the width of the 95% CI was up to 300% larger when CCA was used, whereas most imputation methods maintained the width of the 95% CI within 50% of the true value. Single imputation with k-nearest neighbor (kNN) method and multiple imputation with predictive mean matching (MICE-PMM) best-reproduced point estimates and intervariable relationships resembling the native dataset. Availability of correlated outcome scores improved the RMSE, MAPE, accuracy of the mean postoperative ASES score, and multivariable linear regression model estimates. Conclusions: Complete-case analysis can introduce selection bias when data are MAR, and it results in loss of statistical power, resulting in loss of precision (i.e., expansion of the 95% CI) and predisposition to false-negative findings. Our data demonstrate that imputation can reliably reproduce missing clinical data and generate accurate population estimates that closely resemble results derived from native primary shoulder arthroplasty datasets (i.e., prior to simulated data missingness). Further study of the use of imputation in clinical database research is critical, as the use of CCA may lead to different conclusions in comparison to more rigorous imputation approaches.

Background: Total ankle arthroplasty (TAA) using patient-specific instrumentation (PSI) has increased in popularity with evidence for decreased operative duration, fluoroscopy usage, and increased implant placement accuracy. To date, no comparisons have verified the accuracy of PSI in vivo using preoperative and postoperative weightbearing computed tomography (WBCT). Our study aims to supplement the literature by quantifying the accuracy and precision of PSI-guided implant positioning using WBCT. The primary study outcome is to identify positioning deviations in degrees or millimeters in any plane for talus and tibial PSI-guided resections and subsequent implant placement. Secondary outcomes were correlation and regression analyses performed to identify variables that correlate to resection or implant placement deviation, as well as comparison to existing radiography-based PSI techniques. Methods: This was a single-surgeon, single-implant consecutive patient retrospective study where preoperative and postoperative WBCTs were obtained. TAA was performed by the senior author with the same low-profile implant for all cases. Talus and tibial resection analysis was performed in computer-automated fashion on postoperative segmented WBCTs and is described as 3 possible deviations from plan (cut height in millimeters, sagittal slope, varus/valgus deviation). Overall deformity in this group was not severe. Implant placement factors independent of PSI guides are described as center-of-mass translations (mm) and axial rotation (degrees). Desired accuracy for the PSI system was within ±2 mm or ±2 degrees of the preoperative plan. Statistical analysis of data collected included Student t test and linear regression analyses. Results: Thirty patients were included. Four talus implants were excluded per intraoperative surgeon discretion (deviation from PSI plan, use of conventional instruments). All postoperative tibial measurements were within the desired limits, except for mediolateral center of mass positioning ( P = .003) and slope ( P = .013). Two of six talar parameters also diverged from preoperative measurements: axial rotation ( P = .015) and anteroposterior positioning ( P = .002). In addition, no correlations exceeding r = 0.5 were noted between preoperative deformity measurements and postoperative positioning. Conclusion: For the 26 ankles that did not require an intraoperative deviation from PSI plan and/or use of conventional instruments, total ankle replacement performed with PSI using WBCT results in component placement with relatively little deviation from the preoperative plan. In addition, the lack of correlation between preoperative deformity and implant placement suggests that the magnitude of preoperative deformity in this group was not associated with the accuracy of PSI-guided component positioning. Level of Evidence: Level III, retrospective cohort study.

Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires careful consideration of factors beyond predictive accuracy. We provide an overview of essential considerations for developing clinically applicable ML models, including methods for assessing and improving calibration, selecting appropriate decision thresholds, enhancing model explainability, identifying and mitigating bias, as well as methods for robust validation. We also discuss strategies for improving accessibility to ML models and performing real-world testing. Highlights This tutorial provides clinicians with a comprehensive guide to implementing machine-learning classification models in clinical practice. Key areas covered include model calibration, threshold selection, explainability, bias mitigation, validation, and real-world testing, all of which are essential for the clinical deployment of machine-learning models. Following these guidance can help clinicians bridge the gap between machine-learning model development and real-world application and enhance patient care outcomes.

Background: Periacetabular osteotomy (PAO) is a complex procedure historically requiring inpatient stay. However, there is increased emphasis for other complex orthopaedic procedures (eg, arthroplasty) to be performed on an outpatient basis by insurance companies. Increasing resistance to insurance approval of inpatient listing for PAO patients at the participating institution suggests that this changing tide is affecting PAO surgery. Purpose: To (1) investigate the proportion of PAOs various surgical listing classifications (outpatient, outpatient-overnight, inpatient, etc), (2) determine the incidence of subsequent denial/request for additional documentation for approval of inpatient stay after PAO, and (3) characterize the accuracy of preoperative patient listing classification as well as mean length of stay (LOS) and incidence of successful same-day discharge after PAO at a single institution. Study Design: Case series; Level of evidence, 3. Methods: A retrospective chart review was performed to identify all PAOs, performed by 4 participating surgeons, at a single academic institution over a 2-year period. The initial listing status as an outpatient-overnight or inpatient procedure was identified. Whether a preoperative peer review was required for approval of inpatient listings as well as if additional documentation was necessary to convert outpatient-overnight listings to inpatient stays were also recorded. Results: A total of 140 PAOs among 117 patients were performed with 25 (17.9%) initially listed as an inpatient stay and 115 (82.1%) listed as outpatient-overnight. Of the 25 PAOs listed as inpatient, 2 (8.0%) required a preoperative peer review process to justify or clarify listing status. The mean LOS was 1.9 ± 1.4 days with 55.7% (78/140) of PAOs staying ≥2 nights in the hospital. Only 6 PAOs (4.3%) went home the same day as surgery. Patients who underwent PAO with concomitant hip arthroscopy stayed on average 2.4 ± 1.2 days in comparison with 1.6 ± 1.4 days for those who underwent PAO alone ( P < .001). Of the 115 PAOs listed as an outpatient-overnight, 53 (46.1%) converted to an inpatient stay, all of which required service documentation to support conversion to inpatient status. Conclusion: Over half of PAOs performed resulted in an inpatient stay with patients spending ≥2 nights in the hospital. Additionally, all conversions from outpatient-overnight to inpatient required additional service documentation to support status conversion. Understanding trends in postoperative hospitalizations and LOS may allow for better informed partnerships between surgeons and insurance companies creating more efficient preauthorizations, billing practices, and expected patterns of patient care and discharge.

Abstract Objective To examine the effectiveness of treatment strategies for medication‐overuse headache (MOH) in children and adolescents. Background There are no guidelines for the treatment of adult or pediatric patients with MOH. However, research pertaining to MOH in adults is far outpacing research in children and adolescents. Methods A systematic review of evidence on treatment for MOH in children and adolescents was performed via PubMed, Embase, Web of Science, PsycINFO, and Cochrane from database inception to October 2024 for articles with the subject headings and keywords for MOH and pediatric, children, and/or adolescent. Inclusion criteria were: (i) randomized controlled trials and retrospective and prospective observational studies, (ii) children and adolescents aged <18 years with chronic headaches and with medication overuse (MO) or MOH, and (iii) interventions addressing MO or MOH, including pharmacologic and behavioral interventions. At least two independent reviewers screened citations and full‐text articles independently. Results A total of 16 studies (one randomized control trial, six prospective observational studies and nine retrospective observational studies) were identified as meeting final inclusion criteria, resulting in 7978 total participants with a subset of those having MO, MOH or probable MOH. Participants ranged in mean age from 10 to 14.7 years, with 7.1%–46.2% male participants. All the studies recruited patients from an outpatient setting. Interventions used in the studies included withdrawal of the overused acute medication, initiation of preventive medication, patient education, physiotherapy and psychological treatment, and motivational interviewing. Multimodal therapy was studied more than single therapeutic intervention. There was vast heterogeneity across the studies, including study design, diagnostic criteria, intervention type, and outcomes. Conclusion From the data available, there does not seem to be a singular superior treatment approach. Accordingly, the most appropriate treatment modality will need to be tailored to the patient and their caregivers. Further research is required regarding management of MOH in children and adolescents.

ABSTRACTBackground/ObjectivesImmune‐mediated skin disorders, such as immunobullous dermatoses and leukocytoclastic vasculitis, rarely affect children. While direct immunofluorescence (DIF) biopsy is a standard diagnostic tool, limited data exist on pediatric DIF patterns, rates of positivity, pretest diagnostic concordance, and the relevance of biopsy site. This study sought to address these gaps.MethodsDIF data from all skin and mucosal specimens interpreted at Mayo Clinic's reference immunodermatology laboratory for patients aged 0–18 years (August 22, 2017 to November 30, 2023) were reviewed. DIF results were classified as positive if a characteristic pattern was seen and negative if the findings were negative or nondiagnostic.ResultsOf 986 pediatric DIF studies, 153 (15.5%) were positive and comparable to adult positivity rates (20.9%) during the same period. The most frequent DIF patterns were IgA‐predominant vasculitis (N = 85/153; 55.5%) and lichenoid tissue reaction (N = 21/153;13.7%). Concordance between pretest diagnosis and positive DIF results was highest for linear IgA bullous dermatosis (N = 7/7; 100%) and dermatitis herpetiformis (N = 6/6; 100%). Excluding these entities, DIF changed the pretest diagnosis in 16.7% (N = 19/114) of cases. While lower extremity biopsies were initially more likely to yield positive DIF results, this association disappeared when IgA vasculitis cases were excluded.ConclusionThe most frequent DIF pattern in children was that of IgA‐predominant vasculitis. Pediatric DIF positivity rates closely mirrored those of adults, supporting similar biopsy thresholds. DIF results differed from the pretest impression in a substantial percentage, supporting the value of DIF in select situations in the pediatric population. After controlling for IgA vasculitis, biopsy site was not associated with DIF positivity.

Abstract Introduction Limited data exist on the natural history and treatment persistence in ulcerative colitis (UC) patients on 5-aminosalicylic acid (5-ASA) in the era of biologic agents and small molecules. Methods This was a retrospective cohort study utilizing the US Collaborative Network in patients with UC who were initiated on 5-ASA between 2015 and 2019. The 1-, 3-, and 5-year incidence rates of advanced therapy use and colectomy were calculated. Predictors of advanced therapy use and colectomy within 5 years were analyzed and expressed as adjusted odds ratios (aORs) with 95% confidence intervals (CIs) after 1:1 propensity score matching (PSM). Results Among 15 177 patients in the UC 5-ASA cohort (mean age 47.2 ± 19.1 years old, 49.2% female and 70.3% White), the 1-, 3- and 5-year incidence of advanced therapy use was 5.37%, 11.56% and 15.8%, respectively. After PSM, age &lt; 40 years old (aOR 1.93, 95% CI, 1.73-2.15), male sex (aOR 1.13, 1.02-1.25), obesity (aOR 1.18, 95% CI, 1.05-1.31), pancolitis (aOR 1.18, 95% CI, 1.03-1.36), steroid use (aOR 1.79, 95% CI, 1.53-2.09), and CRP ≥ 12 mg/L (aOR 1.39, 95% CI, 1.05-1.83) prior to 5-ASA initiation were associated with advanced therapy use within 5 years. The 1-, 3- and 5-year incidence of colectomy was 0.74%, 1.28%, and 1.64% respectively. After PSM, male sex (aOR 1.36, 95% 1.06-1.74), obesity (aOR 1.32, 95% CI, 1.02-1.69), primary sclerosing cholangitis (aOR 2.37, 95% CI, 1.11-5.06), pancolitis (aOR 1.90, 95% CI, 1.29-2.78), and steroid use (aOR 1.96, 95% CI, 1.35-2.84) were associated with colectomy within 5 years. Conclusion Our study provides updated estimates of advanced therapy utilization and colectomy as well as predictors of disease progression in patients with UC on 5-ASA in the biologic era.

Parkinson’s disease (PD) is the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current clinical methods for the diagnosis and severity assessment of PD rely on the visual and physical examination of subjects and identifying key disease motor signs and symptoms such as bradykinesia, rigidity, tremor, and postural instability. In the present study, we developed a method for the diagnosis and severity assessment of PD using Gramian Angular Fields (GAFs) in combination with deep Convolutional Neural Networks (CNNs). Our model was applied to PD gait signals captured using pressure sensors embedded into insoles. Our results indicated an accuracy of 98.6%, a true positive rate (TPR) of 99.2%, and a true negative rate (TNR) of 98.5%, showcasing superior classification performance for PD diagnosis compared to the methods used in recent studies in the literature. The estimation of disease severity scores using gait signals showed a high accuracy for the Hoehn and Yahr score as well as the Timed Up and Go (TUG) test score (R2 > 0.8), while we achieved a lower prediction performance for the Unified Parkinson’s Disease Rating Scale (UPDRS) and its motor component (UPDRSM) scores (R2 < 0.2). These results were achieved using gait signals recorded in time windows as small as 10 s, which may pave the way for shorter, more accessible assessment tools for diagnosis and severity assessment of PD.