Clinical performance of an updated trabecular bone score (TBS) algorithm in men and women: the Manitoba BMD cohort.

This is the first study to directly compare the original and recently updated versions of the trabecular bone score (TBS) algorithm. We confirmed improved performance of the new algorithm, especially among men.

INTRODUCTION: Lumbar spine trabecular bone score (TBS) predicts major osteoporotic fractures (MOFs) and hip fractures (HFs) independent of bone density. The original TBS algorithm (version 1; [TBS-v1]) was optimized for women of average body size. Limitations were identified when used in men or extremes of body mass index (BMI). The current study evaluates an updated TBS algorithm (version 2; [TBS-v2]) modified to address these issues.

METHODS: From a registry with all DXA results for Manitoba, Canada, we identified 47,736 women and 4348 men age ≥ 40 with baseline spine DXA (GE Prodigy, 1999-2011). Spine TBS was measured using both TBS-v1 and TBS-v2 algorithms. Risk stratification for incident fractures identified from population-based data was assessed from area under the receiver operating characteristic curve (AUROC).

RESULTS: With the TBS-v1 algorithm, average TBS for men was significantly lower than for women (p < 0.001) and showed significant inverse correlations with BMI (Pearson r-0.40 in men, -0.18 in women [both p < 0.001]). With the TBS-v2 algorithm, average values for men were slightly greater than for women (p < 0.001) and there were no significant correlations with BMI (Pearson r 0.01 in men, -0.01 in women [both p > 0.1]). During mean follow-up of 5 years in men, there were 214 incident MOFs and 47 HFs; during 6 years mean follow-up in women, there were 2895 incident MOFs and 694 HFs. Improvements in fracture prediction were seen with TBS-v2 in both men (change in AUROC for MOFs +0.021 [p = 0.17], HFs +0.046 [p = 0.04]) and women (change in AUROC for MOFs +0.012 [p < 0.001], HFs +0.020 [p < 0.001]).

CONCLUSION: The updated TBS algorithm is less affected by BMI, gives higher mean results for men than women consistent with their lower fracture risk, and improves fracture prediction in both men and women.