Published online Jul 16, 2023. doi: 10.12998/wjcc.v11.i20.4824
Peer-review started: June 6, 2023
First decision: June 15, 2023
Revised: June 16, 2023
Accepted: June 19, 2023
Article in press: June 19, 2023
Published online: July 16, 2023
Spinal osteoporosis is a prevalent condition that increases the risk of fractures, particularly among older populations. Traditional predictive models for fracture risk rely on a limited set of factors and lack precision. This study aimed to develop and validate a more comprehensive prediction model that includes a wider range of factors, such as biochemical indicators and bone mineral density, based on the medical records of 80 patients with spinal osteoporosis. Factors significantly associated with fracture risk included age, sex, body mass index, smoking history, bone mineral density, vertebral trabecular alterations, and prior vertebral fractures. The final risk-prediction model demonstrated strong discriminatory capabilities and has potential for identifying high-risk individuals for early intervention and guiding appropriate preventive actions.
The high incidence of spinal osteoporosis among older populations and the associated risk of fractures highlights the need for accurate and effective predictive models for fracture risk. However, traditional models have limitations in terms of precision and inclusiveness of potential risk factors. This study was motivated by the need to develop a more comprehensive and statistically robust prediction model that can better identify high-risk individuals for early intervention. By incorporating a wide range of factors, including biochemical indicators and bone mineral density, the developed model offers a precise tool for evaluating fracture risk in patients with spinal osteoporosis. The model has the potential to significantly improve patient outcomes by enabling early intervention and guiding appropriate preventive actions to reduce the impact of osteoporosis-related fractures. Ultimately, this research is driven by the goal of improving the quality of life for patients with spinal osteoporosis and reducing the burden of osteoporosis-related fractures on individuals and healthcare systems.
The main objective of this research was to develop and validate a prediction model for spinal fracture risk in patients with osteoporosis. The model aimed to offer improved accuracy and inclusiveness of potential risk factors compared to traditional models, which often lack precision and fail to consider all relevant variables. To achieve this objective, the study employed a retrospective analysis of medical records from 80 patients with spinal osteoporosis. Demographic, clinical, biochemical, and radiological data were collected and compared between patients who had experienced fractures and those who had not. Using logistic regression analysis, the study identified factors significantly associated with fracture risk, including age, sex, body mass index, smoking history, bone mineral density, vertebral trabecular alterations, and prior vertebral fractures. Based on these findings, the researchers developed a final prediction model that incorporates multiple factors and demonstrated strong discriminatory capabilities in evaluating fracture risk. Overall, the study's objectives were to improve prediction accuracy and ultimately aid in early identification and intervention of individuals at high risk of osteoporosis-related fractures.
This study employed a retrospective analysis of medical records to develop and validate a prediction model for spinal fracture risk in patients with osteoporosis. The study included 80 patients with spinal osteoporosis who were diagnosed and treated between 2019 and 2022. Using strict criteria, the patients were categorized into two groups: Those with fractures (n = 40) and those without fractures (n = 40). Demographic, clinical, biochemical, and radiological data were collected from the medical records and compared between the two groups. A logistic regression analysis was employed to identify factors significantly associated with fracture risk. Based on these findings, a final prediction model was developed that incorporated multiple factors, including age, sex, body mass index, smoking history, bone mineral density, vertebral trabecular alterations, and prior vertebral fractures. The model's performance was evaluated using the area under the receiver operating characteristic curve, which measures the model's discriminatory capabilities. The statistical analyses were conducted using appropriate software and tools. Overall, the study's methods involved a rigorous and systematic approach to developing an accurate and effective prediction model for spinal fracture risk in patients with osteoporosis.
Spinal osteoporosis is a common health condition that increases fracture risk. This study aimed to develop and validate a comprehensive model for predicting fracture risk in patients with spinal osteoporosis. The medical records of 80 patients were retrospectively analyzed, and factors associated with fracture risk, including age, sex, body mass index, smoking history, bone mineral density, vertebral trabecular alterations, and prior vertebral fractures, were identified. Using logistic regression analysis, a risk-prediction model was developed and validated, showing strong discriminatory capabilities with an area under the receiver operating characteristic curve of 0.93. This model can potentially assist in identifying high-risk individuals for early intervention and guiding appropriate preventive actions to reduce the impact of osteoporosis-related fractures.
This study emphasizes the significance of a comprehensive approach to predicting fracture risk in patients with spinal osteoporosis. The developed model utilizing factors such as age, sex, body mass index, smoking history, bone mineral density, vertebral trabecular alterations, and prior vertebral fractures offers a precise tool for evaluating fracture risk in these patients. The model's strong discriminatory capabilities make it potentially valuable in identifying high-risk individuals for early intervention and guiding appropriate preventive measures to reduce the impact of osteoporosis-related fractures. By considering a wide range of accessible clinical, biochemical, and radiological information, clinicians can accurately assess fracture risk and improve patient outcomes.
This study's findings open up new research perspectives in the field of spinal osteoporosis. Further studies can focus on validating and refining the developed fracture risk-prediction model by including more comprehensive and diverse data sources, such as genetic factors and lifestyle behaviors. Additionally, larger-scale studies incorporating various ethnic groups and age ranges can provide insights into whether the developed model is applicable to wider populations. Furthermore, future research can evaluate the effectiveness of using the prediction model in clinical practice and determine its impact on reducing fracture-related morbidity and mortality rates. Lastly, considering the potential of artificial intelligence and machine learning algorithms in predicting fracture risk, there is scope for developing more sophisticated models that can identify high-risk individuals with higher accuracy and efficiency. In summary, this study provides a foundation for further research aimed at improving fracture risk prediction and prevention in patients with spinal osteoporosis.