Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients.

BACKGROUND: Geriatric hip fractures are one of the most common fractures in elderly individuals, and prolonged hospital stays increase the risk of death and complications. Machine learning (ML) has become prevalent in clinical data processing and predictive models. This study aims to develop ML models for predicting extended length of stay (eLOS) among geriatric patients with hip fractures and to identify the associated risk factors. AIM: To develop ML models for predicting the eLOS among geriatric patients with hip fractures, identify associated risk factors, and compare the performance of each model. METHODS: A retrospective study was conducted at a single orthopaedic trauma centre, enrolling all patients who underwent hip fracture surgery between January 2018 and December 2022. The study collected various patient characteristics, encompassing demographic data, general health status, injury-related data, laboratory examinations, surgery-related data, and length of stay. Features that exhibited significant differences in univariate analysis were integrated into the ML model establishment and subsequently cross-verified. The study compared the performance of the ML models and determined the risk factors for eLOS. RESULTS: The study included 763 patients, with 380 experiencing eLOS. Among the models, the decision tree, random forest, and extreme Gradient Boosting models demonstrated the most robust performance. Notably, the artificial neural network model also exhibited impressive results. After cross-validation, the support vector machine and logistic regression models demonstrated superior performance. Predictors for eLOS included delayed surgery, D-dimer level, American Society of Anaesthesiologists (ASA) classification, type of surgery, and sex. CONCLUSION: ML proved to be highly accurate in predicting the eLOS for geriatric patients with hip fractures. The identified key risk factors were delayed surgery, D-dimer level, ASA classification, type of surgery, and sex. This valuable information can aid clinicians in allocating resources more efficiently to meet patient demand effectively.

Relationships among body weight, lipids and bone mass in elderly individuals with fractures: A case-control study.

BACKGROUND: The prevalence of osteoporosis and low bone mass is steadily rising each year. Low body weight is commonly linked to diminished bone mass and serves as a robust predictor of osteoporosis. Nonetheless, the connection between body mass index (BMI), bone mineral density, and lipid profiles among the elderly remains elusive. AIM: To examine the association between BMI and bone mass, explore the correlation between lipid profiles and bone mass, and delve into the interplay between lipid metabolism and bone health. METHODS: The study included 520 patients aged ≥ 65 years (178 men and 342 women). Age, sex, weight, and height were recorded. Femoral neck bone mineral density and T scores were determined using a dual-energy X-ray absorptiometry scanner. Blood calcium (Ca), phosphorus (P), albumin (ALB), alkaline phosphatase (ALP), aspartate aminotransferase, alanine aminotransferase, triglyceride (TG), total cholesterol (TC), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) levels were measured. Patients were classified by sex (male and female), age (65-79 years and ≥ 80 years), and T score (normal bone mineral density, osteopenia and osteoporosis). RESULTS: Age, sex, BMI, and ALP and TG levels were independent risk factors for osteoporosis. For the 65-79- and ≥ 80-year-old groups, females presented lower T scores than males. Ca, P, ALB, ALP, TC, HDL and LDL levels were significantly different between men and women in the 65-79-year-old group. In addition, BMI and TG levels were significantly decreased in osteoporotic patients compared with patients with normal bone mass. TC levels declined in 65- to 79-year-old male and female osteoporosis patients. In the group of women aged ≥ 80 years, osteoporotic patients showed significantly increased ALP levels. Furthermore, we found positive correlations between BMI and TG levels in the male and female patient groups. However, we found no significant differences in ALB, Ca, P, HDL and LDL levels in osteoporotic patients compared to patients with normal bone mass. CONCLUSION: Osteoporotic patients showed significantly decreased BMI and TG levels compared with those with normal bone mass. BMI showed positive correlations with TG levels in male and female patients. These results indicate correlations between BMI and bone mass and between lipid profiles and bone mass.

The regulative effect and repercussion of probiotics and prebiotics on osteoporosis: involvement of brain-gut-bone axis.

Osteoporosis (OP) is a systemic disease characterized by decreased bone mass and degeneration of bone microstructure. In recent years, more and more researches have focused on the close relationship between gut microbiota (GM) and the occurrence and progression of OP, and the regulation of probiotics and prebiotics on bone metabolism has gradually become a research hotspot. Based on the influence of brain-gut-bone axis on bone metabolism, this review expounds the potential mechanisms of probiotics and prebiotics on OP from next perspectives: regulation of intestinal metabolites, regulation of intestinal epithelial barrier function, involvement of neuromodulation, involvement of immune regulation and involvement of endocrine regulation, so as to provide a novel and promising idea for the prevention and treatment of OP in the future.

A narrative review of the moderating effects and repercussion of exercise intervention on osteoporosis: ingenious involvement of gut microbiota and its metabolites.

Osteoporosis (OP) is a systemic bone disease characterized by the decreased bone mass and destruction of bone microstructure, which tends to result in the enhanced bone fragility and related fractures, as well as high disability rate and mortality. Exercise is one of the most common, reliable and cost-effective interventions for the prevention and treatment of OP currently, and numerous studies have revealed the close association between gut microbiota (GM) and bone metabolism recently. Moreover, exercise can alter the structure, composition and abundance of GM, and further influence the body health via GM and its metabolites, and the changes of GM also depend on the choice of exercise modes. Herein, combined with relevant studies and based on the inseparable relationship between exercise intervention-GM-OP, this review is aimed to discuss the moderating effects and potential mechanisms of exercise intervention on GM and bone metabolism, as well as the interaction between them.

The modulatory effect and implication of gut microbiota on osteoporosis: from the perspective of "brain-gut-bone" axis.

Osteoporosis (OP) is a kind of systemic metabolic disease characterized by decreased bone mass and destruction of the bone microstructure. In recent years, it has become an expected research trend to explore the cross-linking relationship in the pathogenesis process of OP so as to develop reasonable and effective intervention strategies. With the further development of intestinal microbiology and the profound exploration of the gut microbiota (GM), it has been further revealed that the "brain-gut" axis may be a potential target for the bone, thereby affecting the occurrence and progression of OP. Hence, based on the concept of "brain-gut-bone" axis, we look forward to deeply discussing and summarizing the cross-linking relationship of OP in the next three parts, including the "brain-bone" connection, "gut-bone" connection, and "brain-gut" connection, so as to provide an emerging thought for the prevention strategies and mechanism researches of OP.