Ten existing osteoporosis prediction tools for the successful application of National Health Insurance-reimbursed anti-osteoporosis medications in long-term care residents in Taiwan

Osteoporotic fracture introduce enormous societal and economic burden, especially for long-term care residents (LTCRs). Although osteoporosis prevention for LTCRs is urgently needed, obstacles such as frail status and inconvenient hospital visits hurdled them from necessary examinations and diagnoses. We aimed to test 10 existing osteoporosis screening tools (OSTs), which can be easily used in institutions and serve as a prediction, for accurately determining the outcome of a Taiwan's National Health Insurance (NHI)-reimbursed anti-osteoporosis medications (AOMs) application for LTCRs.This prospective analysis recruited 444 patients from LTC institutions between October 2018 and November 2019. Predictions of whether the NHI-reimbursed AOMs criteria was met were tested for 10 OSTs. The results of OSTs categorized into self-reported or validated based on previous fracture history were self-reported by LTCRs or validated by imaging data and medical records, respectively. The receiver operating characteristic curve and the optimal cut-off points for LTCRs based on Youden's index were explored.Overall, the validated OSTs had a higher positive predictive value (PPV) and negative predictive value (NPV) summation than the corresponding reported OSTs. The validated FRAX-Major was the best OST (PPV = 63.6%, NPV = 82.4% for the male group and, PPV = 78.8%, NPV = 90.0% for the female group). After applying the optimum cut-off derived from Youden's index, the validated FRAX-Major (PPV = 75.4%, NPV = 92.0%)) remained performed best for men. In female population, validated FRAX-Major (PPV = 87.2%, NPV = 84.1%) and validated osteoporosis prescreening risk assessment (OPERA; PPV = 96.1%, NPV = 79.7%)) both provided good prediction results.FRAX-Major and OPERA have better prediction ability for LTCRs to acquire NHI-reimbursed AOMs. The validated fracture history and adjusted cut-off points could prominently increase the PPV during prediction.