Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a National cohort study of 52,707 cases.

BACKGROUND: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. METHODS: A total of 52,707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve Bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. FINDINGS: The eXGBM model exhibited the best prediction performance, with an AUC of 0.908 (95% CI: 0.881-0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/ , based on the eXGBM model. The comparative testing revealed that the AI application's predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P <0.001). CONCLUSIONS: The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

CHRONIC CRITICAL ILLNESS-INDUCED MUSCLE ATROPHY: INSIGHTS FROM A TRAUMA MOUSE MODEL AND POTENTIAL MECHANISM MEDIATED VIA SERUM AMYLOID A.

ABSTRACT: Background: Chronic critical illness (CCI), which was characterized by persistent inflammation, immunosuppression, and catabolism syndrome (PICS), often leads to muscle atrophy. Serum amyloid A (SAA), a protein upregulated in critical illness myopathy, may play a crucial role in these processes. However, the effects of SAA on muscle atrophy in PICS require further investigation. This study aims to develop a mouse model of PICS combined with bone trauma to investigate the mechanisms underlying muscle weakness, with a focus on SAA. Methods: Mice were used to examine the effects of PICS after bone trauma on immune response, muscle atrophy, and bone healing. The mice were divided into two groups: a bone trauma group and a bone trauma with cecal ligation and puncture group. Tibia fracture surgery was performed on all mice, and PICS was induced through cecal ligation and puncture surgery in the PICS group. Various assessments were conducted, including weight change analysis, cytokine analysis, hematological analysis, grip strength analysis, histochemical staining, and immunofluorescence staining for SAA. In vitro experiments using C2C12 cells (myoblasts) were also conducted to investigate the role of SAA in muscle atrophy. The effects of inhibiting receptor for advanced glycation endproducts (RAGE) or JAK2 on SAA-induced muscle atrophy were examined. Bioinformatic analysis was conducted using a dataset from the GEO database to identify differentially expressed genes and construct a coexpression network. Results: Bioinformatic analysis confirmed that SAA was significantly upregulated in muscle tissue of patients with intensive care unit-induced muscle atrophy. The PICS animal models exhibited significant weight loss, spleen enlargement, elevated levels of proinflammatory cytokines, and altered hematological profiles. Evaluation of muscle atrophy in the animal models demonstrated decreased muscle mass, grip strength loss, decreased diameter of muscle fibers, and significantly increased expression of SAA. In vitro experiment demonstrated that SAA decreased myotube formation, reduced myotube diameter, and increased the expression of muscle atrophy-related genes. Furthermore, SAA expression was associated with activation of the FOXO signaling pathway, and inhibition of RAGE or JAK2/STAT3-FOXO signaling partially reversed SAA-induced muscle atrophy. Conclusions: This study successfully develops a mouse model that mimics PICS in CCI patients with bone trauma. Serum amyloid A plays a crucial role in muscle atrophy through the JAK2/STAT3-FOXO signaling pathway, and targeting RAGE or JAK2 may hold therapeutic potential in mitigating SAA-induced muscle atrophy.

Bone Engineering Scaffolds With Exosomes: A Promising Strategy for Bone Defects Repair.

The treatment of bone defects is still an intractable clinical problem, despite the fact that numerous treatments are currently available. In recent decades, bone engineering scaffolds have become a promising tool to fill in the defect sites and remedy the deficiencies of bone grafts. By virtue of bone formation, vascular growth, and inflammation modulation, the combination of bone engineering scaffolds with cell-based and cell-free therapy is widely used in bone defect repair. As a key element of cell-free therapy, exosomes with bioactive molecules overcome the deficiencies of cell-based therapy and promote bone tissue regeneration via the potential of osteogenesis, angiogenesis, and inflammation modulation. Hence, this review aimed at overviewing the bone defect microenvironment and healing mechanism, summarizing current advances in bone engineering scaffolds and exosomes in bone defects to probe for future applications.