Differential Risk of Incident Fractures Depending on Intensity and Frequency of Physical Activity According to Cognitive Status: A Nationwide Longitudinal Study

A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

Background

Post-operative mortality risk assessment for geriatric patients with hip fractures (HF) is a challenge for clinicians. Early identification of geriatric HF patients with a high risk of post-operative death is helpful for early intervention and improving clinical prognosis. However, a single significant risk factor of post-operative death cannot accurately predict the prognosis of geriatric HF patients. Therefore, our study aims to utilize a machine learning approach, random forest algorithm, to fabricate a prediction model for post-operative death of geriatric HF patients.

Methods

This retrospective study enrolled consecutive geriatric HF patients who underwent treatment for surgery. The study cohort was divided into training and testing datasets at a 70:30 ratio. The random forest algorithm selected or excluded variables according to the feature importance. Least absolute shrinkage and selection operator (Lasso) was utilized to compare feature selection results of random forest. The confirmed variables were used to create a simplified model instead of a full model with all variables. The prediction model was then verified in the training dataset and testing dataset. Additionally, a prediction model constructed by logistic regression was used as a control to evaluate the efficiency of the new prediction model.

Results

Feature selection by random forest algorithm and Lasso regression demonstrated that seven variables, including age, time from injury to surgery, chronic obstructive pulmonary disease (COPD), albumin, hemoglobin, history of malignancy, and perioperative blood transfusion, could be used to predict the 1-year post-operative mortality. The area under the curve (AUC) of the random forest algorithm-based prediction model in training and testing datasets were 1.000, and 0.813, respectively. While the prediction tool constructed by logistic regression in training and testing datasets were 0.895, and 0.797, respectively.

Conclusions

Compared with logistic regression, the random forest algorithm-based prediction model exhibits better predictive ability for geriatric HF patients with a high risk of death within post-operative 1 year.

Platelet-Rich Plasma for Bone Fracture Treatment: A Systematic Review of Current Evidence in Preclinical and Clinical Studies

Background: Recently, there is an increasing interest in the therapeutic potential of platelet-rich plasma (PRP) for bone fracture treatment. Nevertheless, the effect of PRP for bone fracture treatment remains controversial and is still a matter of discussion. Therefore, we performed a systematic review to evaluate the efficacy and safety of PRP injection for treatment of bone fracture. Methods: The main bibliographic databases, including Medline, PubMed, Embase, Web of Science, and the Cochrane library, were comprehensively searched for studies focusing on the application of platelet-rich plasma (PRP) on bone fracture treatment. All relevant articles were screened for eligibility and subdivided into the preclinical and clinical studies. Data were extracted and presented systematically. Results: Finally, twenty-six in vitro preclinical studies (basic studies), nine in vivo preclinical studies (animal studies), and nine clinical studies, met the selection criteria, and were included in the present systematic review. Preclinical studies showed an overall positive effect of PRP on osteoblast-like cells in vitro and bone healing in animal models. The most used treatment for bone fracture in animal and clinical studies is fixation surgery combined with PRP injection. The clinical studies reported PRP shortened bony healing duration, and had no positive effect on improving the healing rate of closed fractures. However, the results of functional outcomes are controversial. Additionally, compared with control group, PRP would not increase the rate of postoperative wound infection. Conclusion: The present systematic review confirmed the continuing interests of PRP as an additional treatment for bone fracture. Preclinical studies highlighted the potential value of PRP as promising therapy for bone fracture. However, the preclinical evidence did not translate into a similar result in the clinical studies. In addition, types of fractures and procedures of PRP preparation are heterogeneous in enrolled studies, which might result in controversial results. Meanwhile, characteristics of PRP, such as platelet concentration, the numbers of leukocytes, still need to be determined and further research is required.