Underlying systemic mastocytosis in patients with unexplained osteoporosis: score proposal
Ilaria Tanasi1, Lara Crosera1, Francesco Taus2
1Unità Operativa di Ematologia, Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy; Gruppo Interdisciplinare per lo Studio della Mastocitosi (GISM), Azienda Ospedaliera Universitaria Integrata di Verona, Verona, Italy.
Background:
A score to predict the association between unexplained osteoporosis and an underlying systemic Mastocytosis (SM) is lacking.
Objective:
This study aimed at identifying criteria able to predict the diagnosis of SM without skin involvement and provide an indication for bone marrow (BM) assessment.
Methods:
We included 139 adult patients with unexplained osteoporosis and suspected SM. After BM evaluation, 63 patients (45.3 %) were diagnosed with SM, while the remaining 76 patients (54.7 %) negative for clonal mast cell (MC) disorders, constituted our control group. Univariate and multivariate analysis identified three independent predictive factors: age (<54 years: +1 point, >64 years: -1 point), serum basal tryptase (sBT) levels >19 ng/mL (+2 points) and vertebral fractures (+2 points).
Results:
These variables were used to build the OSTEO-score, able to predict the diagnosis of SM before BM assessment with a sensitivity of 73.5 % and a specificity of 67.1 %. Patients with a score < 3 had a lower probability of having SM compared to patients with a score ≥ 3 (28.5 % and 71.4 %, respectively, p < 0.0001). When sBT levels were corrected for the presence of hereditary alpha-tryptasemia (HαT) using the BST calculater (https://bst-calculater.niaid.nih.gov/) recently published [1,2], the sensitivity of ΗαT-adjusted OSTEO-score increased to 87.8 %, and the specificity reached 76.1 %. Also, the positive predictive value of a score ≥ 3 increased to 85.2 %.
Conclusions:
Further studies are needed to validate these results and characterize the role of tryptase genotyping in patients with unexplained osteoporosis in reducing the risk of misdiagnosing patients with SM. Our proposed scoring model allows the identification of patients with the highest probability of having SM, avoiding unnecessary BM studies.
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