Mesenchymal stem cells for chronic knee pain secondary to osteoarthritis: A systematic review and meta-analysis of randomized trials.

OBJECTIVE: To assess the effectiveness of mesenchymal stem cells (MSCs) for chronic knee pain secondary to osteoarthritis (OA). METHODS: We searched MEDLINE, EMBASE, CINAHL, and Cochrane Central to September 2023 for trials that (1) enrolled patients with chronic pain associated with knee OA, and (2) randomized them to MSC therapy vs. placebo or usual care. We performed random-effects meta-analysis and used Grading of Recommendations, Assessment, Development, and Evaluation to assess the certainty of evidence. RESULTS: We included 16 trials (807 participants). At 3-6 months, MSC therapy probably results in little to no difference in pain relief (weighted mean difference [WMD] -0.74 cm on a 10 cm visual analog scale [VAS], 95% confidence interval [95%CI] -1.16 to -0.33; minimally important difference [MID] 1.5 cm) or physical functioning (WMD 2.23 points on 100-point 36-item Short Form Survey (SF-36) physical functioning subscale, 95%CI -0.97 to 5.43; MID 10-points; both moderate certainty). At 12 months, injection of MSCs probably results in little to no difference in pain (WMD -0.73 cm on a 10 cm VAS, 95%CI -1.69 to 0.24; moderate certainty) and may improve physical functioning (WMD 19.36 points on 100-point SF-36 PF subscale, 95%CI -0.19 to 38.9; low certainty). MSC therapy may increase risk of any adverse events (risk ratio [RR] 2.67, 95%CI 1.19 to 5.99; low certainty) and pain and swelling of the knee joint (RR 1.58, 95%CI 1.04 to 2.38; low certainty). CONCLUSIONS: Intra-articular injection of MSCs for chronic knee pain associated with OA probably provides little to no improvement in pain or physical function.

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.

BACKGROUND: Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty. QUESTION/PURPOSE: Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty? METHODS: Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a cause-specific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 - (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error. RESULTS: Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models. CONCLUSION: Machine learning did not outperform traditional regression models. CLINICAL RELEVANCE: Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context.

A simplified, 2-question grading system for evaluating abstracts in orthopedic scientific meetings: a serial randomization study.

BACKGROUND AND PURPOSE: Efficient abstract scoring for congress presentation is important. Given the emergence of new study methodologies, a scoring system that accommodates all study designs is warranted. We aimed to assess the equivalence of a simplified, 2-question abstract grading system with a more complex currently used system in assessing abstracts submitted for orthopedic scientific meetings in a serial randomized study. METHODS: Dutch Orthopedic Association Scientific Committee (DOASC) members were randomized to grade abstracts using either the current grading system, which includes up to 7 scoring categories, or the new grading system, which consists of only 2 questions. Pearson correlation coefficient and mean abstract score with 95% confidence intervals (CI) were calculated. RESULTS: Analysis included the scoring of 195 abstracts by 12-14 DOASC members. The average score for an abstract using the current system was 60 points (CI 58-62), compared with 63 points (CI 62-64) using the new system. By using the new system, abstracts were scored higher by 3.3 points (CI 1.7-5.0). Pearson correlation was poor with coefficient 0.38 (P < 0.001). CONCLUSION: The simplified abstract grading system exhibited a poor correlation with the current scoring system, while the new system offers a more inclusive evaluation of varying study designs and is preferred by almost all DOASC members.

Short versus conventional straight stem in uncemented total hip arthroplasty: functional outcomes up to 5 years and survival up to 12 years: secondary results of a randomized controlled trial.

BACKGROUND AND PURPOSE: To date, the mid- and long-term outcomes of the Collum Femoris Preserving (CFP) stem compared with conventional straight stems are unknown. We aimed to compare physical function at a 5-year follow-up and implant survival at an average of 10-year follow-up in an randomized controlled trial (RCT). METHODS: This is a secondary report of a double-blinded RCT in 2 hospitals. Patients aged 18-70 years with hip osteoarthritis undergoing an uncemented primary THA were randomized to a CFP or a Zweymüller stem. Patient-reported outcomes, clinical tests, and radiographs were collected at baseline, 2, 3, 4, and 5 years postoperatively. Primary outcome was the Hip disability and Osteoarthritis Outcome Score (HOOS) function in activities of daily living (ADL) subscale. Secondary outcomes were other patient-reported outcomes, clinical tests, adverse events, and implant survival. Kaplan-Meier and competing risk survival analyses were performed with data from the Dutch Arthroplasty Registry. RESULTS: We included 150 patients. Mean difference between groups on the HOOS ADL subscale at 5 years was -0.07 (95% confidence interval -5.1 to 4.9). Overall survival was 92% for the CFP and 96% for the Zweymüller stem. No significant difference was found. CONCLUSION: No significant differences were found in physical function at 5-year and implant survival at 10-year follow-up between the CFP and Zweymüller stems. When taking cup revisions into account, the CFP group showed clinically inferior survival.

Comparison of 5-year cup and stem migration between ceramic-on-ceramic and ceramic-on-polyethylene bearing in press-fit total hip arthroplasty: a randomised controlled trial using radiostereometric analysis.

INTRODUCTION: The inelasticity of ceramic bearings might affect primary stability and migration of implants in press-fit total hip arthroplasty (THA). This randomised controlled trial compares migration patterns of the uncemented Delta-TT cup and H-MAX S stem between ceramic and polyethylene liners, up to 5 years follow-up. METHODS: Patients receiving primary press-fit THA were randomly allocated to a ceramic (n = 28) or polyethylene (n = 25) liner. Migration was measured using model-based radiostereometric analysis (RSA) at baseline and 1.5, 3, 6, 12, 24 and 60 months postoperatively and compared between groups using mixed models statistical analysis. The focus of this study is on the 2- to 5-year migration of the Delta-TT cup and migration during complete follow-up of the H-MAX S stem up to 5-years. RESULTS: At 5-year follow-up, mean (95% CI) proximal cup translation was 0.56 (0.37-0.74) mm in ceramic and 0.58 (0.25-0.90) mm in polyethylene (p = 0.729). The mean change in adduction was 1.05° (0.27-1.82°) in ceramic and 0.78° (-0.16-1.71°) in polyethylene. Mixed modelling showed that all between-group effects were ⩽0.20 mm for translation and ⩽0.22° for rotation at 5 years postoperatively (p ⩾ 0.23). Mean cup migration between 2 and 5 years was limited (all parameters <0.17 mm and <0.30°). At 5-year follow-up, mean stem subsidence was 2.09 mm (0.89-3.29 mm) in ceramic and 2.55 (0.97-4.12) mm in polyethylene. The mean change in internal rotation was 3.69° (1.98-5.40°) in ceramic and 4.01° (2.20-5.81°) in polyethylene. Most stem migration occurred up to 1.5 months, stabilising afterwards. All between-group effects were ⩽0.75 mm for translations and ⩽1.41° for rotations (p ⩾ 0.26). CONCLUSIONS: 5-year migration patterns of press-fit cups and stems were similar between ceramic and polyethylene liners. The Delta-TT cup and H-MAX S stem showed secondary stabilisation and remained stable up to 5 years in both groups, which is promising for long-term survival with both liner types. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov (NCT03093038).

Posterolateral or Direct Lateral Surgical Approach for Hemiarthroplasty After a Hip Fracture: A Randomized Clinical Trial Alongside a Natural Experiment.

Importance: Hip fractures in older adults are serious injuries that result in disability, higher rates of illness and death, and a substantial strain on health care resources. High-quality evidence to improve hip fracture care regarding the surgical approach of hemiarthroplasty is lacking. Objective: To compare 6-month outcomes of the posterolateral approach (PLA) and direct lateral approach (DLA) for hemiarthroplasty in patients with acute femoral neck fracture. Design, Setting, and Participants: This multicenter, randomized clinical trial (RCT) comparing DLA and PLA was performed alongside a natural experiment (NE) at 14 centers in the Netherlands. Patients aged 18 years or older with an acute femoral neck fracture were included, with or without dementia. Secondary surgery of the hip, pathological fractures, or patients with multitrauma were excluded. Recruitment took place between February 2018 and January 2022. Treatment allocation was random or pseudorandom based on geographical location and surgeon preference. Statistical analysis was performed from July 2022 to September 2022. Exposure: Hemiarthroplasty using PLA or DLA. Main Outcome and Measures: The primary outcome was health-related quality of life 6 months after surgery, quantified with the EuroQol Group 5-Dimension questionnaire (EQ-5D-5L). Secondary outcomes included dislocations, fear of falling and falls, activities of daily living, pain, and reoperations. To improve generalizability, a novel technique was used for data fusion of the RCT and NE. Results: A total of 843 patients (542 [64.3%] female; mean [SD] age, 82.2 [7.5] years) participated, with 555 patients in the RCT (283 patients in the DLA group; 272 patients in the PLA group) and 288 patients in the NE (172 patients in the DLA group; 116 patients in the PLA group). In the RCT, mean EQ-5D-5L utility scores at 6 months were 0.50 (95% CI, 0.45-0.55) after DLA and 0.49 (95% CI, 0.44-0.54) after PLA, with 77% completeness. The between-group difference (-0.04 [95% CI, -0.11 to 0.04]) was not statistically significant nor clinically meaningful. Most secondary outcomes were comparable between groups, but PLA was associated with more dislocations than DLA (RCT: 15 of 272 patients [5.5%] in PLA vs 1 of 283 patients [0.4%] in DLA; NE: 6 of 113 patients [5.3%]) in PLA vs 2 of 175 patients [1.1%] in DLA). Data fusion resulted in an effect size of 0.00 (95% CI, -0.04 to 0.05) for the EQ-5D-5L and an odds ratio of 12.31 (95% CI, 2.77 to 54.70) for experiencing a dislocation after PLA. Conclusions and Relevance: This combined RCT and NE found that among patients treated with a cemented hemiarthroplasty after an acute femoral neck fracture, PLA was not associated with a better quality of life than DLA. Rates of dislocation and reoperation were higher after PLA. Randomized and pseudorandomized data yielded similar outcomes, which suggests a strengthening of these findings. Trial Registration: ClinicalTrials.gov Identifier: NCT04438226.

Systematic review of machine-learning models in orthopaedic trauma.

Aims: Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods: A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results: A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures. Conclusion: The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.

Impact of Active Disinvestment on Decision-Making for Surgery in Patients With Subacromial Pain Syndrome: A Qualitative Semi-structured Interview Study Among Hospital Sales Managers and Orthopedic Surgeons.

BACKGROUND: Withdrawal of reimbursement for low-value care through a policy change, ie, active disinvestment, is considered a potentially effective de-implementation strategy. However, previous studies have shown conflicting results and the mechanism through which active disinvestment may be effective is unclear. This study explored how the active disinvestment initiative regarding subacromial decompression (SAD) surgery for subacromial pain syndrome (SAPS) in the Netherlands influenced clinical decision-making around surgery, including the perspectives of orthopedic surgeons and hospital sales managers. METHODS: We performed 20 semi-structured interviews from November 2020 to October 2021 with ten hospital sales managers and ten orthopedic surgeons from twelve hospitals across the Netherlands as relevant stakeholders in the active disinvestment process. The interviews were video-recorded and transcribed verbatim. Inductive thematic analysis was used to analyse interview transcripts independently by two authors and discrepancies were resolved through discussion. RESULTS: Two overarching themes were identified that negatively influenced the effect of the active disinvestment initiative for SAPS. The first theme was that the active disinvestment represented a "Too small piece of the pie" indicating little financial consequences for the hospital as it was merely used in negotiations with healthcare insurers to reduce costs, required a disproportionate amount of effort from hospital staff given the small saving-potential, and was not clearly defined nor enforced in the overall healthcare insurer agreements. The second theme was "They [healthcare insurer] got it wrong," as the evidence and guidelines had been incorrectly interpreted, the active disinvestment was at odds with clinician experiences and beliefs and was perceived as a reduction in their professional autonomy. CONCLUSION: The two overarching themes and their underlying factors highlight the complexity for active disinvestment initiatives to be effective. Future de-implementation initiatives including active disinvestment should engage relevant stakeholders at an early stage to incorporate their different perspectives, gain support and increase the probability of success.