Polish archives of internal medicine · 2023
POL-RISK: an algorithm for 10-year fracture risk prediction in postmenopausal women from the RAC-OST-POL study.
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Pluskiewicz Wojciech, Adamczyk Piotr, Werner Aleksandra, Bach Małgorzata, Drozdzowska Bogna
Аннотация
INTRODUCTION: Fragility fractures are the major consequence of osteoporosis. Thus, fracture risk assessment is an essential part of the diagnostic process in osteoporosis.
OBJECTIVES: The aim of the study was to develop an algorithm for fracture risk prediction.
PATIENTS AND METHODS: Bone status was evaluated in a population‑basedcohort of postmenopausal women at a mean (SD) age of 66.4 (7.8) years. Subsequently, all participants were contacted by phone once a year (for 10 consecutive years) to update their history of fractures. At the end of the 10‑year follow‑up, the number of the study participants was 640, of whom 129 had a history of 190 osteoporotic fractures recorded during the study period. Statistical analysis included multistep data preprocessing, feature selection, identification of fracture risk factors, and design of the final model. Logistic regression models were fitted and used for the evaluation of variables from determined feature sets, including global fit measures, as well as individual parameters, such as the Wald statistic and P value, odds ratio, and 95% CI.
RESULTS: The 10‑year risk for any fracture depended on the age of the patient, the number of recorded fractures after the age of 40 years, femoral neck bone mass, and the occurrence of falls during the previous year. The achieved equation was incorporated into an algorithm, available at the www.fracture‑risk.pl webpage.
CONCLUSION: A fracture prediction algorithm was developed in a longitudinal study to enable calculation of the 10‑year fracture risk. Identification of patients at a high risk of fracture should be followed by implementation of appropriate treatment strategy to reduce the number of future fractures.
OBJECTIVES: The aim of the study was to develop an algorithm for fracture risk prediction.
PATIENTS AND METHODS: Bone status was evaluated in a population‑basedcohort of postmenopausal women at a mean (SD) age of 66.4 (7.8) years. Subsequently, all participants were contacted by phone once a year (for 10 consecutive years) to update their history of fractures. At the end of the 10‑year follow‑up, the number of the study participants was 640, of whom 129 had a history of 190 osteoporotic fractures recorded during the study period. Statistical analysis included multistep data preprocessing, feature selection, identification of fracture risk factors, and design of the final model. Logistic regression models were fitted and used for the evaluation of variables from determined feature sets, including global fit measures, as well as individual parameters, such as the Wald statistic and P value, odds ratio, and 95% CI.
RESULTS: The 10‑year risk for any fracture depended on the age of the patient, the number of recorded fractures after the age of 40 years, femoral neck bone mass, and the occurrence of falls during the previous year. The achieved equation was incorporated into an algorithm, available at the www.fracture‑risk.pl webpage.
CONCLUSION: A fracture prediction algorithm was developed in a longitudinal study to enable calculation of the 10‑year fracture risk. Identification of patients at a high risk of fracture should be followed by implementation of appropriate treatment strategy to reduce the number of future fractures.