Leonard Rosenthall, Julian Falutz and Giovanni Guaraldi
Background: The total bone mineral density T-score cutoff for low bone mass underestimates the frequency shown by femoral neck and lumbar T-score cutoffs.
Objective: To determine whether a total body DXA T-score cutoff can be found that will produce results similar those obtained by local measurements of the femoral neck and lumbar spine.
Methodology: Participants were all HIV-infected; 1730 males and 840 females. T-score correlations of the three sites were obtained. ROC analyses were performed to obtain the T-score cutoffs for the total body that would produce results that best matched those of the femoral neck and lumbar spine. Low bone mass was defined as a T-score <-1, which includes both osteopenia and osteoporosis categories as defined by the World Health Organization (WHO). The efficacy of the derived T-score cutoffs were determined by cross tabulation of the modified total body classifications against the femoral neck and lumbar spine classification, and rated by the kappa coefficient of agreement and percent of agreement (concordance).
Results: Spearman rank correlations varied from 0.570 to 0.752 between total body, lumbar spine and femoral neck T-scores. Area under the ROC curve varied from 0.777 to 0.874 for the different paired sites. The T-score cutoffs for the total body were selected from the ROC curves at a point where the sum of the sensitivity and specificity is a maximum. Cross tabulation of the binary categories. i.e., normal or abnormal, of the total body using the derived T-score cutoffs against those of the femoral neck and lumbar spine registered a reduction of false negatives, but it was associated with a consistent increase in the number of false positives. The resultant kappa coefficients of agreement varied from 0.429 to 0.564; a moderate rating when perfect agreement is 1.0.
Conclusion: The modification of the total body T-score cutoffs for the disclosure of low bone mass at the femoral neck and lumbar spine is not sufficiently accurate for clinical application, in particular fracture risk prediction.
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