Minimally invasive surgical technique for unstable supracondylar humerus fractures in children (Gartland type III or IV).

Background: Achieving and maintaining anatomical reduction during the treatment of pediatric humerus fractures, classified as Gartland type III or IV, presents a clinical challenge. Herein, we present a minimally invasive surgical approach using a novel and simple K-wire push technique that aids in achieving and maintaining anatomical reduction. Methods: We reviewed data of children receiving treatment for supracondylar fractures of the humerus at our hospital between January 2016 and December 2020. Patients were divided into two groups based on the method of treatment: Group 1 was treated with the K-wire push technique, and Group 2 was treated with the standard technique as described by Rockwood and Wilkins. The medical records and radiographic images were reviewed. In total, 91 patients with Gartland types III and IV fractures were included, with 37 and 54 patients in Groups 1 and 2, respectively. Results: The postoperative reduction radiographic parameters and Flynn scores at final follow-up were not significantly different between the two groups. Conclusion: The minimally invasive K-wire push technique for unstable supracondylar fractures in children is a safe and effective alternative for improving reduction. Using this technique, complications can be minimized, and the requirement for open reduction can be reduced.

Outcome Prediction Model Following Proximal Femoral Osteotomy in Legg-Calvé-Perthes Disease Using Machine Learning Algorithms.

BACKGROUND: The purpose of the current study was (1) to analyze various factors that may be associated with the outcomes of Legg-Calvé-Perthes disease (LCPD), and (2) to develop and internally validate machine learning algorithms capable of providing patient-specific predictions of which patients with LCPD will achieve relevant improvement in radiologic outcomes after proximal femoral varus osteotomy (PFVO). We examined several variables, previously identified as factors, that may influence the outcome of LCPD and developed a machine learning algorithm based on them. METHODS: In this retrospective study, we analyzed patients aged older than  6 years at the time of LCPD diagnosis who underwent PFVO at our institution between 1979 and 2015. Univariate and multivariate logistic regression analyses were used to examine the effects of variables on the sphericity of the femoral head at skeletal maturity, including age at onset, sex, stage at operation, extent of epiphyseal involvement and collapse, presence of specific epiphyseal, metaphyseal, and acetabular changes, and postoperative neck shaft angle (NSA). Recursive feature selection was used to identify the combination of variables from an initial pool of 13 features that optimized the model performance. Five machine learning algorithms [extreme gradient boosting (XGBoost), multilayer perception, support vector machine, elastic-net penalized logistic regression, and random forest) were trained using 5-fold cross-validation 3 times and applied to an independent testing set of patients. RESULTS: Ninety patients with LCPD who underwent PFVO were included in this study. The mean age at diagnosis was 7.93 (range, 6.0 to 12.33) years. The average follow-up period was 10.11 (range, 5.25 to 22.92) years. A combination of 8 variables, optimized algorithm performance, and specific cutoffs were found to decrease the likelihood of achieving the 1 or 2 Stulberg classification: age at onset ≤ 8.06, lateral classification ≤ B, 12.40 < preoperative migration percentage (MP) ≤ 22.85, Catterall classification ≤ 2, 117.4 < postoperative NSA ≤ 122.90, -10.8 < postoperative MP ≤ 6.5, 139.65 < preoperative NSA ≤ 144.67, and operation at stage 1. The XGBoost model demonstrated the best performance (F1 score: 0.78; area under the curve: 0.84). CONCLUSIONS: The XGBoost machine learning algorithm achieved the best performance in predicting the postoperative radiologic outcomes in patients with LCPD who underwent PFVO. In our population, age at onset ≤ 8.06, lateral classification ≤ B, 12.40 < preoperative MP ≤ 22.85, Catterall classification ≤ 2, 117.4 < postoperative NSA ≤ 122.90, -10.8 < postoperative MP ≤ 6.5, 139.65 < preoperative NSA ≤ 144.67, and operation at an early stage had the likelihood of achieving the spherical femoral head for the patients with LCPD that underwent PFVO. After external validation, the online application of this model may enhance shared decision-making. LEVEL OF EVIDENCE: Level III-retrospective cohort study.

Relationship between Slip Severity and BMI in Patients with Slipped Capital Femoral Epiphysis Treated with In Situ Screw Fixation.

Background: Slipped capital femoral epiphysis (SCFE) is a hip disorder that occurs in adolescence before epiphyseal plate closure, causing anatomical changes in the femoral head. Obesity is known to be the single most important risk factor for idiopathic slipped capital femoral epiphysis (SCFE), which is highly related to mechanical factors. Meanwhile, as increased slip angle increases major complications in patients with SCFE, slip severity is an important factor to evaluate prognosis. In obese patients with SCFE, higher shear stress is loaded on the joint, which increases the likelihood of slip. The study aim was to assess the patients with SCFE treated with in situ screw fixation according to the degree of the obesity and to find any factors affecting the severity of slip. Methods: Overall, 68 patients (74 hips) with SCFE who were treated with in situ fixation screw fixation were included (mean age 11.38, range: 6-16) years. There were 53 males (77.9%) and 15 females (22.1%). Patients were categorized underweight, normal weight, overweight, and obese depending on BMI percentile for age. We determined slip severity of patients using the Southwick angle. The slip severity was defined as mild if the angle difference was less than 30 degrees, moderate if the angle difference was between 30 and 50 degrees, and severe if the angle difference was greater than 50 degrees. To examine the effects of several variables on slip severity, we used a univariable and multivariate regression analysis. The following data were analyzed: age at surgery, sex, BMI, symptom duration before diagnosis (acute, chronic, and acute on chronic), stability, and ability to ambulate at the time of the hospital visit. Results: The mean BMI was 25.18 (range: 14.7-33.4) kg/m2. There were more patients with overweight and obese than those with normal weight in SCFE (81.1% vs. 18.9%). We did not find significant differences between overall slip severity and degree of obesity or in any subgroup analysis. Conclusions: We did not find a relationship between slip severity and degree of obesity. A prospective study related to the mechanical factors affecting the slip severity according to the degree of obesity is needed.