Factors influencing the achievement of early surgery in proximal femoral fractures under a Japanese incentive policy.

BACKGROUND: Proximal femoral fractures in geriatric patients are a major challenge in orthopedics, often leading to major functional impairment. Early surgical intervention is crucial for improving patient recovery and overall health outcomes. Thus, Japan's Ministry of Health, Labour and Welfare initiated a reimbursement policy in April 2022 to encourage early surgery for these fractures in patients aged ≥75 years. This study investigated the impact of this policy on early surgery rates in Japan and identified factors influencing the timing of surgical interventions. METHODS: We retrospectively analyzed the data of patients who underwent surgery for proximal femoral fractures at our institution between April 2022 and March 2023. Patients were categorized into two groups based on the timing of surgery relative to the injury: ≤48 h and >48 h. Demographic and clinical data, including age, sex, fracture type, and various health- and admission-related factors, were assessed. RESULTS: Of the 192 patients, 152 were included in the study. Among them, 38% underwent early surgery (≤48 h), and 15% of the patients arrived more than 48 h post-injury. Significant differences were found in admission routes and residence types between the groups. The ≤48 h group had shorter intervals from injury to admission and surgery than the >48 h group. Factors such as the admission process, day of the week, and C-reactive protein levels significantly influenced the timing of surgery. CONCLUSIONS: After introducing incentives for early surgery in Japan, 38% of patients with proximal femoral fractures underwent surgery within 48 h of injury. Factors contributing to patients not receiving early surgery included transport from another hospital, weekend hospitalization, and elevated CRP levels. These findings suggest that achieving surgery within 48 h of injury is challenging through hospital efforts alone, and the time criteria might be more appropriate if changed to "admission to surgery."

Automated classification of hip fractures using deep convolutional neural networks with orthopedic surgeon-level accuracy: ensemble decision-making with antero-posterior and lateral radiographs.

Background and purpose - Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using antero-posterior (AP) and lateral hip radiographs. Patients and methods - 1,703 plain hip AP radiographs and 1,220 plain hip lateral radiographs were included in the total dataset. 150 images each of the AP and lateral views were separated out and the remainder of the dataset was used for training. The CNN made the diagnosis based on: (1) AP radiographs alone, (2) lateral radiographs alone, or (3) both AP and lateral radiographs combined. The diagnostic performance of the CNN was measured by the accuracy, recall, precision, and F1 score. We further compared the CNN's performance with that of orthopedic surgeons. Results - The average accuracy, recall, precision, and F1 score of the CNN based on both anteroposterior and lateral radiographs were 0.98, 0.98, 0.98, and 0.98, respectively. The accuracy of the CNN was comparable to, or statistically significantly better than, that of the orthopedic surgeons regardless of radiographic view used. In the CNN model, the accuracy of the diagnosis based on both views was significantly better than the lateral view alone and tended to be better than the AP view alone. Interpretation - The CNN exhibited comparable or superior performance to that of orthopedic surgeons to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using both AP and lateral hip radiographs.