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Objective <p>Early screening and intervention for osteoporosis (OP), a common bone disease, are of great significance in reducing its incidence and improving the quality of life of patients. This study aimed to build a simple osteoporosis risk model that will provide a scientific basis for clinical decision-making.</p> Study Design <p>A cross-sectional clinical study.</p> Materials and Methods <p>We collected relevant data from the 2013–2014 and 2017–2020 cycles of the US National Health and Nutrition Examination Survey (NHANES) and extracted five predictor variables: gender, age, race, body mass index, and history of fractures. The data were processed, organized, and used to build prediction models. Discrimination, calibration, and clinical utility were used to evaluate the model performance.</p> Results <p>A total of 5732 participants (3520 in the development cohort and 2212 in the validation cohort) were included in the final analysis. A simplified osteoporosis risk model was established based on gender, age, race, body mass index, and history of fractures. The model achieved good discrimination, calibration, and clinical applicability in both the development and validation cohorts.</p> Conclusion <p>The osteoporosis risk model established in this study was simple, accurate, efficient, and had an important clinical application value for the early detection of osteoporosis. This allows for timely and effective intervention measures to be implemented, reduce the risk of fractures, and improve patients’ quality of life.</p>

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A Simple Osteoporosis Clinical Risk Model Based on the National Health and Nutrition Examination Survey

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • Qun Liu,
  • Yi Duan

摘要

Objective

Early screening and intervention for osteoporosis (OP), a common bone disease, are of great significance in reducing its incidence and improving the quality of life of patients. This study aimed to build a simple osteoporosis risk model that will provide a scientific basis for clinical decision-making.

Study Design

A cross-sectional clinical study.

Materials and Methods

We collected relevant data from the 2013–2014 and 2017–2020 cycles of the US National Health and Nutrition Examination Survey (NHANES) and extracted five predictor variables: gender, age, race, body mass index, and history of fractures. The data were processed, organized, and used to build prediction models. Discrimination, calibration, and clinical utility were used to evaluate the model performance.

Results

A total of 5732 participants (3520 in the development cohort and 2212 in the validation cohort) were included in the final analysis. A simplified osteoporosis risk model was established based on gender, age, race, body mass index, and history of fractures. The model achieved good discrimination, calibration, and clinical applicability in both the development and validation cohorts.

Conclusion

The osteoporosis risk model established in this study was simple, accurate, efficient, and had an important clinical application value for the early detection of osteoporosis. This allows for timely and effective intervention measures to be implemented, reduce the risk of fractures, and improve patients’ quality of life.