Adjusting Fracture Probability by Trabecular Bone Score

Abstract

The aim of the present study was to determine the impact of trabecular bone score on the probability of fracture above that provided by the clinical risk factors utilized in FRAX. We performed a retrospective cohort study of 33,352 women aged 40–99 years from the province of Manitoba, Canada, with baseline measurements of lumbar spine trabecular bone score (TBS) and FRAX risk variables. The analysis was cohort-specific rather than based on the Canadian version of FRAX. The associations between trabecular bone score, the FRAX risk factors and the risk of fracture or death were examined using an extension of the Poisson regression model and used to calculate 10-year probabilities of fracture with and without TBS and to derive an algorithm to adjust fracture probability to take account of the independent contribution of TBS to fracture and mortality risk. During a mean follow-up of 4.7 years, 1754 women died and 1639 sustained one or more major osteoporotic fractures excluding hip fracture and 306 women sustained one or more hip fracture. When fully adjusted for FRAX risk variables, TBS remained a statistically significant predictor of major osteoporotic fractures excluding hip fracture (HR/SD 1.18, 95 % CI 1.12–1.24), death (HR/SD 1.20, 95 % CI 1.14–1.26) and hip fracture (HR/SD 1.23, 95 % CI 1.09–1.38). Models adjusting major osteoporotic fracture and hip fracture probability were derived, accounting for age and trabecular bone score with death considered as a competing event. Lumbar spine texture analysis using TBS is a risk factor for osteoporotic fracture and a risk factor for death. The predictive ability of TBS is independent of FRAX clinical risk factors and femoral neck BMD. Adjustment of fracture probability to take account of the independent contribution of TBS to fracture and mortality risk requires validation in independent cohorts.

Appendices

Appendix 1

The following method estimating the difference between probability of fracture with and without TBS avoids probability values outside the range 0–100. The inverse of the logistic function was used for each probability, i.e., −log(100/p − 1), where p is the 10-year probability in  %. Let us call this quantity for W₁ when calculated for the 10-year probability including TBS and W₀ for the probability without TBS. Now W₁ will be the dependent variable of a multivariable regression analysis. Independent variables are age, TBS, age·TBS, and W₀. We will find an estimated mean of W₁ as a function of TBS, age, and W₀. Let us call this estimated mean Z(W1, TBS, age) or shortly Z. Now we calculate 100/(1 + exp(−Z)), which is always a number in the interval 0–100. The number is our estimate of the 10-year probability including TBS. Again we can replace W₀ for the corresponding number calculated (by the inverse of the logistic function) from the FRAX probability.

Appendix 2

The adjustment of fracture probabilities according to TBS is given for the 10-year probabilities of hip fracture and major osteoporotic fracture are given below

Outcome: Hip fracture

The 10-year probability calculated with TBS is $\frac{100}{{1 + e^{ - w} }},$ where W = 15.420 − 12.627 × TBS − 0.194 × age + 0.157 × TBS × age + 0.920 × L, L = −ln(100/p − 1), p is the 10-year probability calculated without TBS

Outcome: Major Osteoporotic Fracture

The 10-year probability calculated with TBS is $\frac{100}{{1 + e^{ - w} }},$ where W = 5.340 − 4.213 × TBS − 0.0521 × age + 0.0393 × TBS × age + 0.897 × L, L = −ln(100/p − 1), p is the 10-year probability calculated without TBS