Heterotopic hip ossification resection: monocentric experiences of associated factors, outcomes, and complications.

PURPOSE: The exponential increase in total hip arthroplasty (THA) has led to acute and chronic surgery-related complications. Common chronic and local complications are represented by hip ossification (HO). The aim of our study was to assess the clinical and radiological correlates of patients undergoing surgical removal of heterotopic ossifications after THA and the possible association between HO and prosthetic joint infection. METHODS: Data of 26 patients who underwent surgical removal of periprosthetic calcifications after THA from 2000 to 2022 were analyzed and compared with characteristics of 156 subjects without HO. RESULTS: The preoperative radiographs of patients showed a high-grade Brooker, 3 or 4, later reduced to 1 or 2 in the postoperative radiographs. Ten (38.5%) patients underwent radiotherapy prophylaxis, administered as a single dose 24 h before surgery. In 19 (73%) patients, pharmacological prophylaxis with indomethacin was added in the 30 postoperative days. Only one patient who underwent radiotherapy had a recurrence, while new ossifications were found in three patients without prophylaxis (11.5%). Intraoperative cultures were performed for suspected periprosthetic infection in 8 study group patients. In logistic regression, the presence of HO was significantly and inversely associated with the ASA score (OR = 0.27, 95% CI = 0.09-0.82; P = 0.021) after adjusting. CONCLUSION: Surgical HO removal in symptomatic patients with high-grade disease produces good clinical and radiographic results. Radiotherapy was a good perioperative and preventive strategy for recurrence, also associated with NSAIDs and COX-2 inhibitors.

Simultaneous vs staged bilateral hip arthroplasty: monocentric analysis of satisfaction rate and complications.

PURPOSE: Interest in bilateral total hip arthroplasty (THA) has been increasing over the past decade. This study aims to compare postoperative local and systemic complications, hospital readmissions, and satisfaction assessment in patients undergoing simultaneous versus staged bilateral THA. METHODS: A retrospective observational study was conducted among patients who underwent simultaneous or staged bilateral THA between 2017 and 2020. Data on perioperative parameters, local and systemic complications, and 30-day hospital readmissions were collected. Patient satisfaction was assessed using Forgotten Joint Score (FJS). The comparison of continuous variables with normal distribution of variance was performed by ANOVA; for variables with abnormal distribution, the nonparametric test Mann-Whitney U was adopted. The distribution of dichotomous variables was analyzed by chi-square test, and statistical significance was calculated by Fisher exact test. RESULTS: The study included 199 patients, of whom 156 underwent staged and 43 simultaneous bilateral THA. A total of 44 patients developed complications (21 systemics, 12 locals, and 20 postoperative anemia). There were no significant differences regarding local (simultaneous 5% vs. staged 6%; p = 0.999) or systemic complications (simultaneous 14% vs. staged 10%; p = 0.408). Only the incidence of postoperative anemia was significantly higher in simultaneous group compared with staged group (p = 0.003). There were no significant differences in FJS between the two groups (p = 0.258). CONCLUSION: No differences in local or systemic complications nor readmission rates were observed following simultaneous or staged bilateral THA. Simultaneous bilateral THA is non-inferior to staged implants in terms of safety and patient satisfaction.

Can machine learning predict the accuracy of preoperative planning for total hip arthroplasty, basing on patient-related factors? An explorative investigation on Supervised machine learning classification models.

Background: The success of Total Hip Arthroplasty (THA) is influenced by preoperative planning, with traditional 2D approaches displaying varied reliability as well. The present study investigates the use of Supervised Machine Learning (SML) models with patient-related features to improve accuracy. Methods: Preoperative and perioperative data, as well as planning and final implant information, were obtained from 800 consecutive cementless primary THA, which was performed uniformly by a specialized surgical team. Six Supervised Machine Learning models were trained and validated using patient characteristics and implant data: Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (CART), Gaussian Naive Bayes (GN), and Support Vector Classifier (SVC). The models' ability to predict planning reliability and leg length disparity was evaluated. Results: KNN performed better on the cup model (97.9 %), femur model (96.7 %), and femur size (99.2 %). SVM emerged as the model with the highest accuracy for cup size (60.4 %) and head size (62.1 %). CART had the best accuracy (99 %) when determining leg length discrepancy. Conclusion: The study demonstrates the utility of Supervised Machine Learning models, specifically KNN, in predicting the accuracy of preoperative planning in THA. The accuracy of these models, which are driven by patient-related characteristics, provides useful information for optimizing patients' selection and improving surgical outcome.