DATA DRIVEN FRACTURE RISK PREDICTION: A SIMPLE YET EFFECTIVE TOOL

초록

Most existing fracture risk prediction models have focused on estimating long-term risk, typically over 10 years. While such models are valuable, they may not be sufficient for identifying individuals at immediate or short-term risk of fracture, which is a crucial window for intervention. To address this gap, we aimed to develop a simplified and clinically practical risk tool for predicting short-term fracture risk using nationwide health data. We utilized a large-scale national cohort of older adults who underwent standardized health examinations in Korea. Routinely available clinical variables—including anthropometric measures, bone health indicators, and recent fracture history—were integrated into a simplified scoring system. Statistical modeling was applied to derive risk estimates for major osteoporotic fractures and hip fractures within 1–3 years. The focus was on creating a tool that can be applied in routine clinical settings without requiring complex calculations or specialized software. The simplified scoring system demonstrated clear risk stratification across categories. Individuals with higher scores showed progressively greater short-term fracture incidence. The model offers practical utility by converting complex regression outputs into a user-friendly risk grouping, supporting both clinicians and public health practitioners in early identification of vulnerable populations. This nationwide analysis highlights the feasibility and value of constructing a simple, data-driven risk prediction tool to estimate imminent fracture risk. By leveraging routinely collected health information, such tools can enhance clinical decision-making, facilitate timely preventive strategies, and ultimately reduce the burden of osteoporotic fractures at both individual and population levels.