Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Acta orthopaedica
Migration patterns of uncemented femoral stems in hip replacement: a systematic review and meta-analysis of clinical radiostereometric analysis cohort studies
BACKGROUND AND PURPOSE: We conducted a systematic review and meta-analysis of radiostereometric analysis studies of primary uncemented femoral stems to investigate their subsidence and retroversion patterns and the migration patterns according to implant, patient, and study characteristics. METHODS: A systematic search of PubMed, Web of Science, Cochrane, and Embase databases to identify all radiostereometric analysis studies on femoral stem migration following primary uncemented total hip replacement was performed. Clinical studies with 2 or more postoperative radiostereometric measurements within 2 years were included. Subsidence and retroversion at 6 weeks, 3 and 6 months, 1, 2, 5, and 10 years were included for analysis. Extracted implant characteristics included implant design, coating, and surgical approach. Data was analyzed using a random effects model. RESULTS: 73 studies on 120 cohorts and 2,970 uncemented stems were included. 119 cohorts reported on subsidence and 91 on retroversion. The pooled subsidence at 3 months was 0.29 mm (95% confidence interval [CI] 0.19-0.39) and 0.32 mm (CI 0.21-0.43) at 2 years. The pooled retroversion at 3 months was 0.51° (CI 0.33-0.70) and 0.70° (CI 0.48-0.93) at 2 years. Hydroxyapatite-coated stems showed the least migration (subsidence 0.26 mm; CI 0.13-0.40; retroversion 0.51°; CI 0.22-0.80) among different coating types. The anterior approach showed more migration (subsidence 1.04 mm, CI 0.53-1.55; retroversion 1.52°, CI 1.08-1.95) than other surgical approaches. CONCLUSION: Our study shows that most subsidence and retroversion of uncemented femoral stems occurs during the first 3 months. Stabilization of subsidence occurred after 3 months, and retroversion stabilized after 2 years. Migration patterns differ based on stem type, coating, surgical approach, the time period when inclusion started, and timing of baseline measurement.
18 Apr 2026
Read appraisal →European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
Predicting minimal clinically important difference after hip arthroscopy: logistic regression versus machine learning
PURPOSE: Hip arthroscopy outcomes for femoroacetabular impingement (FAI) remain heterogeneous, with 30-40% of patients failing to achieve meaningful improvement. We aimed to develop and validate a predictive model for achievement of minimal clinically important difference (MCID) using routine preoperative variables, and to compare logistic regression with machine learning algorithms. METHODS: We retrospectively analyzed 310 consecutive patients undergoing hip arthroscopy for FAI (2010-2024) at a single institution. Predictor variables included age, sex, body mass index (BMI), morphological etiology, preoperative pain (VAS), and Hip Outcome Score-Activities of Daily Living (HOS-ADL). The outcome was achievement of MCID (≥ 9-point improvement in HOS-ADL). We developed logistic regression, random forest, and gradient boosting models. Internal validation used 10-fold stratified cross-validation. Temporal external validation used 80% of patients for training and 20% for testing. RESULTS: Mean age was 39.7 ± 12.0 years; 59.7% were male. MCID was achieved by 213 patients (68.7%). Lower preoperative HOS-ADL (OR 0.911, 95% CI 0.883-0.940, p < 0.001) and lower BMI (OR 0.846, 95% CI 0.771-0.928, p < 0.001) independently predicted MCID achievement. Logistic regression achieved AUC 0.804 (95% CI 0.76-0.85) on internal validation and AUC 0.819 on temporal external validation (accuracy 81.4%). Logistic regression outperformed random forest (AUC 0.787) and gradient boosting (AUC 0.777). Calibration was excellent. CONCLUSION: A parsimonious logistic regression model using routine preoperative variables demonstrated excellent and externally validated discriminative ability for predicting MCID after hip arthroscopy. The model outperformed machine learning algorithms while maintaining superior interpretability. The developed nomogram provides a practical tool for individualized outcome prediction.
9 Apr 2026
Read appraisal →