Association of Sex With Risk of 2-Year Revision Among Patients Undergoing Total Hip Arthroplasty

Primary Total Hip Arthroplasty in Juvenile Idiopathic Arthritis: Survivorship After a Median Follow-Up of 12 Years

BACKGROUND

Juvenile idiopathic arthritis (JIA) is a chronic inflammatory condition of childhood that frequently affects the hip. Total hip arthroplasty (THA) in JIA can be challenging due to the patient's young age, small proportion, complex anatomy, and bone loss. Outcome data are limited.

METHODS

We reviewed prospectively collected data in 57 JIA patients (83 hips) who underwent THA between 1986 and 2020 by a single surgeon. The median patient age at surgery was 26 years (range, 14 to 62). Reoperation-free survival was assessed via the cumulative incidence function, accounting for the competing risk of death. Relationships between patient and implant factors and survivorship were evaluated by stratification of the cumulative incidence function and Gray's tests. Wilcoxon signed rank tests were used to assess the preoperative to latest postoperative change in patient-reported outcome measures.

RESULTS

At a median (interquartile range) of 12 (4, 20) years of follow-up, 13 (16%) patients underwent reoperation, most commonly for polyethylene wear and osteolysis (7 hips). The estimated incidence of 10-year, 20-year, and 30-year revision (95% confidence interval) were 11.3% (4.5, 21.6%), 18.5% (8.9, 30.9%), and 40.6% (19.4, 60.9%), respectively. There were no differences in survival based on patient age, sex, implant fixation method, polyethylene type, or thickness. All patient-reported outcome measures improved from preoperative to latest follow-up.

CONCLUSIONS

Primary THA is a durable and effective treatment for JIA patients with severe hip involvement and results in major improvements in pain and function. We did not identify any factors predictive of failure.

Sex-Specific Implant Fixation Can Reduce Revision Rates in Total Hip Arthroplasty: Evidence From the Swiss National Joint Registry

BACKGROUND

Patient's sex is considered a risk factor for revision following primary total hip arthroplasty (THA), but sex-specific treatment guidelines are lacking. The purpose was to assess sex-specificity of risk factors for periprosthetic femoral fractures (PFFs) and aseptic stem loosening (ASL) in a nationwide register study.

METHODS

All uncemented and hybrid THAs for hip osteoarthritis registered in the Swiss National Joint Registry were considered. 86,423 THAs were analyzed. Comparable THA subsets for both sexes were obtained through propensity score matching (1:1). A sex-specific analysis of risk factors for early PFF or ASL was performed using recursive partitioning analyses.

RESULTS

In women, PFFs were most significantly associated with uncemented THA fixation (P < .0001) and age (P < .01, threshold: 70.5 years). The ASLs were solely associated with patient age of <65 years (P = .023). In men, PFFs were associated exclusively with an American Society of Anesthesiologists (ASA) score >2 (P = .026). The ASLs were not correlated to any of the potential risk factors analyzed. A mathematical simulation indicated that avoiding uncemented THA fixation in women ≥70.5 years of age decreased the number of revisions within the observational period by 21% in this subset and by 4.9% in the entire patient population.

CONCLUSION

Uncemented THA should be avoided in women >70.5 years due to the increased risk of early PFF, while the mode of stem fixation did not influence revision risk in men. A sex-specific regimen for THA fixation has the potential to markedly reduce early THA revision rates.

An Interpretable Machine Learning Model for Predicting 10-Year Total Hip Arthroplasty Risk

BACKGROUND

As the demand for total hip arthroplasty (THA) rises, a predictive model for THA risk may aid patients and clinicians in augmenting shared decision-making. We aimed to develop and validate a model predicting THA within 10 years in patients using demographic, clinical, and deep learning (DL)-automated radiographic measurements.

METHODS

Patients enrolled in the Osteoarthritis Initiative were included. DL algorithms measuring osteoarthritis- and dysplasia-relevant parameters on baseline pelvis radiographs were developed. Demographic, clinical, and radiographic measurement variables were then used to train generalized additive models to predict THA within 10 years from baseline. A total of 4,796 patients were included (9,592 hips; 58% female; 230 THAs (2.4%)). Model performance using 1) baseline demographic and clinical variables 2) radiographic variables, and 3) all variables were compared.

RESULTS

Using 110 demographic and clinical variables, the model had a baseline area under the receiver operating curve (AUROC) of 0.68 and area under the precision recall curve (AUPRC) of 0.08. Using 26 DL-automated hip measurements, the AUROC was 0.77 and AUPRC was 0.22. Combining all variables, the model improved to an AUROC of 0.81 and AUPRC of 0.28. Three of the top five predictive features in the combined model were radiographic variables including minimum joint space along with hip pain and analgesic use. Partial dependency plots revealed predictive discontinuities for radiographic measurements consistent with literature thresholds of osteoarthritis progression and hip dysplasia.

CONCLUSION

A machine learning model predicting 10-year THA performed more accurately with DL radiographic measurements. The model weighted predictive variables in concordance with clinical THA-pathology assessments.