Validation of the fracture risk evaluation model (FREM) in predicting major osteoporotic fractures and hip fractures using administrative health data.

Abstract

BACKGROUND\nPrevention of osteoporotic fractures remains largely insufficient, and effective means to identify patients at high, short-term fracture risk are needed. The FREM tool is available for automated case finding of men and women aged 45\u202fyears or older at high imminent (1-year) risk of osteoporotic fractures, based on administrative health data with a 15-year look-back. The aim of this study was to validate the performance of FREM, and the effect of applying a shorter look-back period. We also evaluated FREM for 5-year fracture risk prediction.\n\n\nMETHODS\nUsing Danish national health registers we generated consecutive general population cohorts for the years 2014 through 2018. Within each year and across the full time period we estimated the individual fracture risk scores and determined the actual occurrence of major osteoporotic fractures (MOF) and hip fractures. Risk scores were calculated with 15- and 5-year look-back periods. The discriminative ability was evaluated by area under the receiver operating curve (AUC), and negative predictive value (NPV) and positive predictive value (PPV) were estimated applying a calculated risk cut-off of 2% for MOF and 0.3% for hip fractures.\n\n\nRESULTS\nApplying a 15-year look-back, AUC was around 0.75-0.76 for MOF and 0.84-0.87 for hip fractures in 2014, with minor decreases in the subsequent fracture cohorts (2015 to 2018). Applying a 5-year look-back generated similar results, with only marginally lower AUC. In the 5-year risk prediction setting, AUC-values were 0.70-0.72 for MOF and 0.81-0.84 for hip fractures. Generally, PPVs were low, while NPVs were very high.\n\n\nCONCLUSION\nFREM predicts the 1- and 5-year risk of MOF and hip fractures with acceptable vs excellent discriminative power, respectively, when applying both a 15- and a 5-year look-back. Hence, the FREM tool may be applied to improve identification of individuals at high imminent risk of fractures using administrative health data.