Relationship between Low Muscle Strength, and Protein Intake: A Preliminary Study of Elderly Patients with Hip Fracture

Random forest predictive modeling of prolonged hospital length of stay in elderly hip fracture patients

Background

In elderly individuals suffering from hip fractures, a prolonged hospital length of stay (PLOS) not only heightens the probability of patient complications but also amplifies mortality risks. Yet, most elderly hip fracture patients present compromised baseline health conditions. Additionally, PLOS leads to increased expenses for patient treatment and care, while also diminishing hospital turnover rates. This, in turn, jeopardizes the prompt allocation of beds for urgent cases.

Methods

A retrospective study was carried out from October 2021 to November 2023 on 360 elderly hip fracture patients who underwent surgical treatment at West China Hospital. The 75th percentile of the total patient cohort's hospital stay duration, which was 12 days, was used to define prolonged hospital length of stay (PLOS). The cohort was divided into training and testing datasets with a 70:30 split. A predictive model was developed using the random forest algorithm, and its performance was validated and compared with the Lasso regression model.

Results

Out of 360 patients, 103 (28.61%) experienced PLOS. A Random Forest classification model was developed using the training dataset, identifying 10 essential variables. The Random Forest model achieved perfect performance in the training set, with an area under the curve (AUC), balanced accuracy, Kappa value, and F1 score of 1.000. In the testing set, the model's performance was assessed with an AUC of 0.846, balanced accuracy of 0.7294, Kappa value of 0.4325, and F1 score of 0.6061.

Conclusion

This study aims to develop a prognostic model for predicting delayed discharge in elderly patients with hip fractures, thereby improving the accuracy of predicting PLOS in this population. By utilizing machine learning models, clinicians can optimize the allocation of medical resources and devise effective rehabilitation strategies for geriatric hip fracture patients. Additionally, this method can potentially improve hospital bed turnover rates, providing latent benefits for the healthcare system.

Development and validation of risk prediction model for sarcopenia in patients with colorectal cancer

Objectives

Sarcopenia is associated with a poor prognosis in patients with colorectal cancer. However, the clinical factors that lead to colorectal cancer patients with sarcopenia are still unclear. The objective of this study is to develop and validate a nomogram for predicting the occurrence of sarcopenia and to provide healthcare professionals with a reliable tool for early identification of high-risk patients with colorectal cancer associated sarcopenia.

Methods

A total of 359 patients diagnosed with colorectal cancer from July 2021 to May 2022 were included. All patients were randomly divided into a training (n = 287) cohort and a validation cohort (n = 72) at the ratio of 80/20. Univariate and multivariate logistic analysis were performed to evaluate the factors associated with sarcopenia. The diagnostic nomogram of sarcopenia in patients with colorectal cancer was constructed in the training cohort and validated in the validation cohort. Various evaluation metrics were employed to assess the performance of the developed nomogram, including the ROC curve, calibration curve, and Hosmer-Lemeshow test.

Results

Smoking history, drinking history, diabetes, TNM stage, nutritional status, and physical activity were included in the nomogram for the prediction of sarcopenia. The diagnostic nomograms demonstrated excellent discrimination, with AUC values of 0.971 and 0.922 in the training and validation cohorts, respectively. Moreover, the calibration performance of the nomogram is also excellent, as evidenced by the Hosmer-Lemeshow test result of 0.886.

Conclusions

The nomogram consisting of preoperative factors was able to successfully predict the occurrence of sarcopenia in colorectal cancer patients, aiding in the early identification of high-risk patients and facilitating timely implementation of appropriate intervention measures.

Malnutrition in Older Adults—Effect on Falls and Fractures: A Narrative Review

Malnutrition in older adults impacts health status, increased mortality, and morbidity. Malnutrition may increase the development of geriatric syndromes and contribute to a higher prevalence of falls and osteoporotic fractures that lead to loss of independence and an increased rate of institutionalization. The role of malnutrition in the pathogenesis of other geriatric syndromes seems to be well established. However, the data concerning nutritional interventions are confounding. Moreover, long-term undernutrition seems to be one of the factors that strongly influences the efficacy of interventions. This review outlines the current literature on this topic, and aims to guide physicians to make proper decisions to prevent the vicious cycle of falls, fractures, and their negative outcomes in patients with malnutrition.

Effects of Muscles on Bone Metabolism—with a Focus on Myokines

Skeletal muscles and bones, the largest tissues in the body of a non-obese person, comprise the musculoskeletal system, which allows mobility and protects internal organs. Although muscles and bones are closely related throughout life, observations during development and aging and in human and animal disuse models have revealed the synchronization of tissue mass such that muscle phenotype changes precede alterations in bone mineral density and strength. This review discussed that mechanical forces, which have been the traditional research focus, are not the only mechanism by which muscle-derived signals may affect bone metabolism and emphasized the significance of skeletal muscles as an endocrine organ that secretes bone-regulatory factors. Consequently, both mechanical and biochemical aspects should be considered to fully understand muscle–bone crosstalk. This review also suggested that specific myokines could be ideal therapeutic targets for osteoporosis to both increase bone formation and reduce bone resorption; moreover, these myokines could also be potential circulating biomarkers to predict musculoskeletal health.