Recovery of pre-injury functional state following operative treatment of displaced femoral neck fractures; a prospective cohort study

A Systematic Review and Meta-Analysis of Supercapsular Percutaneously Assisted Total Hip Arthroplasty Versus Standard Posterior Approach for Femoral Neck Fracture in Elderly Patients

INTRODUCTION

This meta-analysis aimed to evaluate the efficacy and safety of the supercapsular percutaneously assisted total hip arthroplasty (SuperPATH) and the standard posterior approach in hip arthroplasty in treating femoral neck fractures in elderly patients.

METHOD

A systematic search was conducted for studies from 2012 to December 2022. Meta-analysis was conducted using Review Manager 5.3 on surgical time, intraoperative blood loss, Harris hip scores, and visual analog scale scores.

RESULT

A total of 26 studies involving 2,236 patients with femoral neck fractures were included. The SuperPATH group performed better than traditional posterior approach group in reducing intraoperative blood loss (in ml), shortening incision length (in cm), length of hospitalization period (in days) and improving Harris Hip score (HHS). The operation time took longer than the traditional posterior approach, with statistically significant differences. The VAS scores at 1 week and 3 months after surgery in the SuperPATH group were lower than those of the traditional posterior approach, with statistically significant differences. There was no statistical significance between the two groups in VAS scores 2 weeks and 1 month after surgery.

CONCLUSION

The SuperPATH group resulted in better effects in reducing intraoperative blood loss (in ml), shortening incision length (in cm), length of hospitalization period (in days), and improving Harris hip score (HHS), which is conducive to the rapid postoperative recovery of patients.

Self-reported involvement in road traffic crashes in Kenya: A cross-sectional survey of a nationally representative sample

Background

Road traffic crashes (RTCs) are a global public health burden whose resulting morbidity and mortality disproportionately impact low- and middle-income countries with stressed health systems. There is a paucity of published studies that evaluate the sociodemographic distribution of RTCs using nationally representative samples from the African region.

Aim

To examine population-wide associations between sociodemographic factors and involvement in RTCs in Kenya.

Methods

Data were obtained from the 2014 Kenyan Demographic Health Survey, representing all 47 counties in Kenya, from May to October of 2014. We estimated the prevalence of RTCs and utilized logistic regression for bivariate and multivariable analyses to determine the sociodemographic factors associated with RTCs. Study variables included age, place of residence, household wealth index, educational attainment, and history of alcohol consumption. We computed odds ratios (ORs) and their corresponding 95% confidence intervals (CIs).

Results

A higher prevalence was reported among men (8.76%) versus women (3.22%). The risk factors among men included being 20-34 years of age, living in a rural area (OR 1.38, 95% CI 1.09, 1.74), drinking alcohol (OR 1.33, 95% CI 1.11, 1.59), and having not higher than a primary (OR 1.90, 95% CI 1.19, 3.03) or secondary (OR 1.68, 95% CI 1.04, 2.71) education. The strongest risk factors for women included the being aged 45-49 (OR 2.30, 95% CI 1.44, 3.67) and 20-24 years (OR 1.81, 95% 1.17, 2.79) as well as being in the fourth wealth quintile (OR 1.83, 95% CI 1.15, 2.91).

Conclusion

Men and the most economically productive age groups were more likely to report being involved in RTCs. Strategies to reduce the occurrences of RTCs should prioritize the most vulnerable sociodemographic groups.

Prediction Models for Prognosis of Femoral Neck–Fracture Patients 6 Months after Total Hip Arthroplasty

Purpose

To establish prediction models for 6-month prognosis in femoral neck–fracture patients receiving total hip arthroplasty (THA).

Patients and Methods

In total, 182 computed tomography image pairs from 85 patients were collected and divided into a training set (n=127) and testing set (n=55). Least absolute shrinkage–selection operator regression was used for selecting optimal predictors. A random-forest algorithm was used to establish the prediction models, which were evaluated for accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC).

Results

The best model in this study was constructed based on demographic data, preoperative laboratory indicators, and three preoperative radiomic features. In the random-forest model, activated partial thromboplastin time, a preoperative radiomic feature (maximum diameter), and fibrinogen were important variables correlating with patient outcomes. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the training set were 0.986 (95% CI 0.971–1), 0.925 (95% CI 0.862–0.988), 0.983 (95% CI 0.951–1.016), 0.984 (95% CI 0.953–1.014), 0.922 (95% CI 0.856–0.988), and 0.953 (95% CI 0.916–0.990), respectively. The AUC, sensitivity, specificity, PPV, NPV, and accuracy in the testing set were 0.949 (95% CI 0.885–1), 0.767 (95% CI 0.615–0.918), 1 (95% CI 1–1), 1 (95% CI 1–1), 0.781 (95% CI 0.638–0.924), and 0.873 (95% CI 0.785–0.961), respectively.

Conclusion

The model based on demographic, preoperative clinical, and preoperative radiomic data showed the best predictive ability for 6-month prognosis in the femoral neck–fracture patients receiving THA.