The activity of a Spanish bone densitometry unit revisited under the point of view of FRAX

RESUMEN

Objetivo. Analizar el riesgo de fractura calculado por FRAX y sus determinantes en los pacientes remitidos a una unidad de densitometría ósea para evaluación de la densidad mineral ósea (DMO). Métodos. Los pacientes remitidos desde Atención Primaria a la Unidad de Densitometría para evaluación de la DMO rellenaron un cuestionario autoadministrado acerca de los factores de riesgo clínicos incluidos en el FRAX; se les realizó una densitometría ósea. Con los datos del cuestionario, se analizó el riesgo absoluto de presentar una fractura mayor (MFR) y de cadera (HFR). Ambos riesgos se calcularon con o sin la inclusión de la DMO en el algoritmo MFR+, MFR-, HFR+ y HFR-. Resultados. Se analizaron los datos de 853 mujeres con una edad media de 61,9 (8,9) años y un índice de masa corporal medio de 27,0 (4,2) kg/m2. El 20% de las pacientes tenía una DMO normal, el 55% tenía osteopenia y el 25%, osteoporosis. Excluyendo la edad y el índice de masa corporal, el número de factores de riesgo de fractura fue bajo. El MFR+ medio fue de 5,4 (4,8)%; el MFR- de 6,3 (5,5)%; el HFR+, de 1,5 (2,9)%; y el HFR- de 2,1 (3,3)%. Cuando se incluyeron los valores densitométricos en el algoritmo de cálculo del riesgo de fractura, éste fue significativamente menor (p < 0,001), especialmente en pacientes con mejor DMO. Conclusiones. En nuestro medio, el riesgo de fractura calculado por FRAX en las pacientes remitidas a la unidad de densitometría para evaluación de la DMO es bajo. El riesgo de fractura es inferior cuando se introduce la DMO en el algoritmo de cálculo (AU)

ABSTRACT

In March 2008, FRAX, developed by Kanis and collaborators in the University of Sheffield and supported by the World Health Organization, became available online to calculate absolute risk of osteoporotic fracture in the next 10 years. Objective. To analyze the risk of fracture calculated by FRAX and its determinants in the patients sent to a densitometry unit for bone mineral density (BMD) testing. Methods. All the patients submitted by Primary Care to the Densitometry Unit for BMD testing underwent a self administered questionnaire to assess the clinical risk factors included in FRAX and a bone densitometry of lumbar spine and proximal femur with a DXA densitometer Hologic QDR 4500. They were classified as having a normal BMD, osteopenia or osteoporosis along with the recommendations of the International Society for Clinical Densitometry. As the reference population to calculate the T and Z scores, we used the one from the NHANES III study for femoral neck and total hip and the one from the Study of the Spanish Population for total spine. With the data of the questionnaire, we calculated, by FRAX, the absolute risk in the next ten years of having a major fracture (MFR) or a hip fracture (HFR). Both risks were calculated with or without the inclusion in the algorithm of BMD MFR+, MFR−, HFR+ and HFR−. The results were recorded in an Access 2003 database and analyzed with the statistical package SPSS 15.0 for Windows. Results. We analyzed the data from 853 women with a mean age of 61.9 (8.9) years and a mean body mass index of 27.0 (4.2)kg/m2. Mean BMD at lumbar spine was 0.873 (0.127)g/cm2; at femoral neck, 0.704 (0.105)g/cm2; and at total hip, 0.817 (0.107)g/cm2. Twenty percent of the patients had a normal BMD, 55% had osteopenia and 25%, osteoporosis. Yet excluding age and body mass index, the number of fracture risk factors seems low 31% of the patients had no risk of fracture; 40%, had one; 22%, two; 6%, three; 1%, four; and one patient had five. Mean MFR+ was 5.4 (4.8)%; mean MFR−, 6.3 (5.5)%; mean HFR+, 1.5 (2.9)%; and HFR−, 2.1 (3.3)%. When BMD was included in the algorithm for the calculation of the risk of fracture, the risk was statistically lower (p<0.001), especially in patients with better BMD. Conclusions. The risk of fracture calculated by FRAX in the patients sent to a densitometry unit for bone BMD testing seems low and, probably, a better selection of the patients would detect a higher risk of fracture population. When the fracture risk is calculated with the introduction of BMD in the algorithm, it is lower than without including BMD (AU)