The Osteoporosis Self-assessment Tool (OST) is a clinical instrument designed to select patients at risk of osteoporosis, who would benefit from a bone mineral density measurement. The OST only takes into account the age and weight of the subject. It was developed for Asian women and later validated for European and North American white women. The performance of the OST in a sample of 4343 women from Greater Mendoza, a large metropolitan area of Argentina, was assessed. Dual X-ray absorptiometry (DXA) scans of lumbar spine and hip were obtained. Patients were classified as either osteoporotic ( ) or nonosteoporotic ( ) according to their lowest T-score at any site. Osteoporotic patients had lower OST scores ( ). A receiver operating characteristic (ROC) curve showed an area under the curve of 71% ( ), with a sensitivity of 83.7% and a specificity of 44% for a cut-off value of 2. Positive predictive value was 52% and negative predictive value was 79%. The odds ratio for the diagnosis of osteoporosis was 4.06 (CI95 3.51 to 4.71; ). It is concluded that the OST is useful for selecting postmenopausal women for DXA testing in the studied population. 1. Introduction Osteoporosis is a systemic skeletal disorder characterized by low bone strength (arising from both low bone mass and microarchitectural deterioration), which increases the risk of fractures. Osteoporosis is a major public health problem and an important contributor to the global burden of noncommunicable disease [1]. Currently the recommended method for the diagnosis of osteoporosis is bone mineral density (BMD) measurement by dual-energy X-ray absorptiometry (DXA) [2]. According to the World Health Organization criteria, osteoporosis is operationally defined “as a BMD that lies 2.5 standard deviations or more below the average value for young healthy women.” [2]. Since, due to cost and availability, DXA scans are not recommended for screening purposes, several tools based on known clinical risk factors have been developed to identify those patients with high risk of osteoporosis, in whom actual BMD testing would be most useful in terms of diagnosis, treatment, and followup [3, 4]. Some of these clinical tools, or aids in decision making, include many factors, making calculation of risk cumbersome [5, 6]. Arguably the simplest decision rule is the Osteoporosis Self-assessment Tool (OST) which only takes into account body weight and age, which in adult populations are, respectively, related inversely and directly to the risk of osteoporosis [7]. The OST was developed for predicting risk of femoral
References
[1]
WHO Scientific Group on the Assessment of Osteoporosis at Primary Care Level, Summary Meeting Report: Brussels, Belgium, 5–7 May 2004, World Health Organization, Geneva, Switzerland, 2007.
[2]
O. Johnell and J. A. Kanis, “An estimate of the worldwide prevalence and disability associated with osteoporotic fractures,” Osteoporosis International, vol. 17, no. 12, pp. 1726–1733, 2006.
[3]
L. E. Wehren and E. S. Siris, “Beyond bone mineral density: can existing clinical risk assessment instruments identify women at increased risk of osteoporosis?” Journal of Internal Medicine, vol. 256, no. 5, pp. 375–380, 2004.
[4]
L. S. Lim, L. J. Hoeksema, and K. Sherin, “Screening for osteoporosis in the adult U.S. population. ACPM position statement on preventive practice,” American Journal of Preventive Medicine, vol. 36, no. 4, pp. 366–375, 2009.
[5]
S. M. Cadarette, S. B. Jaglal, N. Kreiger, W. J. McIsaac, G. A. Darlington, and J. V. Tu, “Development and validation of the osteoporosis risk assessment instrument to facilitate selection of women for bone densitometry,” CMAJ, vol. 162, no. 9, pp. 1289–1294, 2000.
[6]
A. Masoni, M. Morosano, S. M. Pezzotto et al., “Construction of two instruments for the presumptive detection of post-menopausal women with low spinal bone mass by means of clinical risk factors,” Maturitas, vol. 51, no. 3, pp. 314–324, 2005.
[7]
P. Dargent-Molina, S. Piault, and G. Bréart, “Identification of women at increased risk of osteoporosis: no need to use different screening tools at different ages,” Maturitas, vol. 54, no. 1, pp. 55–64, 2006.
[8]
L. K. H. Koh, W. Ben Sedrine, T. P. Torralba et al., “A simple tool to identify Asian women at increased risk of osteoporosis,” Osteoporosis International, vol. 12, no. 8, pp. 699–705, 2001.
[9]
B. Rud, J. Hilden, L. Hyldstrup, and A. Hróbjartsson, “Performance of the Osteoporosis Self-Assessment Tool in ruling out low bone mineral density in postmenopausal women: a systematic review,” Osteoporosis International, vol. 18, no. 9, pp. 1177–1187, 2007.
[10]
S. M. Cadarette, W. J. McIsaac, G. A. Hawker et al., “The validity of decision rules for selecting women with primary osteoporosis for bone mineral density testing,” Osteoporosis International, vol. 15, no. 5, pp. 361–366, 2004.
[11]
F. Richy, M. Gourlay, P. D. Ross et al., “Validation and comparative evaluation of the osteoporosis self-assessment tool (OST) in a Caucasian population from Belgium,” Quarterly Journal of Medicine, vol. 97, no. 1, pp. 39–46, 2004.
[12]
B. Rud, J. Hilden, L. Hyldstrup, and A. Hróbjartsson, “The osteoporosis self-assessment tool versus alternative tests for selecting postmenopausal women for bone mineral density assessment: a comparative systematic review of accuracy,” Osteoporosis International, vol. 20, no. 4, pp. 599–607, 2009.
[13]
S. S. Sen, V. P. Rives, O. D. Messina et al., “A risk assessment tool (OsteoRisk) for identifying Latin American women with osteoporosis,” Journal of General Internal Medicine, vol. 20, no. 3, pp. 245–250, 2005.
[14]
J. Morales-Torres and S. Gutiérrez-Ure？a, “Osteoporosis Committee of Pan-American League of Associations for Rheumatology. The burden of osteoporosis in Latin America,” Osteoporosis International, vol. 15, no. 8, pp. 625–632, 2004.
[15]
G. Riera-Espinoza, “Epidemiology of osteoporosis in Latin America 2008,” Salud Pública de México, vol. 51, supplement 1, pp. S52–S55, 2009.
[16]
P. Clark, F. Cons-Molina, M. Deleze et al., “The prevalence of radiographic vertebral fractures in Latin American countries: the Latin American Vertebral Osteoporosis Study (LAVOS),” Osteoporosis International, vol. 20, no. 2, pp. 275–282, 2009.
[17]
Instituto Nacional de Estadísticas y Censos, Censo Nacional de Población, Hogares y Viviendas 2010: Censo del Bicentenario, Resultados Definitivos, Serie B, no. 2, Instituto Nacional de Estadística y Censos, INDEC, Buenos Aires, Argentina, 1st edition, 2012.
[18]
F. Magkos, Y. Manios, E. Babaroutsi, and L. S. Sidossis, “Development and validation of a food frequency questionnaire for assessing dietary calcium intake in the general population,” Osteoporosis International, vol. 17, no. 2, pp. 304–312, 2006.
[19]
Argenfood, “Tablas de composición de alimentos,” Universidad Nacional de Luján, Luján, Argentina, 2010, http://www.unlu.edu.ar/~argenfood/Tablas/Tabla.htm.
[20]
International Society for Clinical Densitometry, “2007 Official Positions of the International Society for Clinical Densitometry,” http://www.iscd.org/wp-content/themes/iscd/pdfs/official-positions/ISCD2007OfficialPositions-Adult.pdf.
[21]
A. Sánchez and D. Salica, “Diagnóstico de osteoporosis basado en DXA: ？Qué datos normativos deberían usarse?” Revista Argentina de Osteología, vol. 6, no. 2, pp. 14–18, 2007.
[22]
N. Zavaroni, C. Rodríguez, and F. D. Saraví, “Densidad mineral ósea e ingesta actual de calcio en pacientes postmenopáusicas de Mendoza,” La Prensa Médica Argentina, vol. 90, no. 3, pp. 254–258, 2003.
[23]
S. H. Park, J. M. Goo, and C. H. Jo, “Receiver operating characteristic (ROC) curve: practical review for radiologists,” Korean Journal of Radiology, vol. 5, no. 1, pp. 11–18, 2004.
[24]
S. A. Avena, A. S. Goicochea, J. Rey, J. M. Dugoujon, C. B. Dejean, and F. R. Carnese, “Mezcla génica en una muestra poblacional de la ciudad de Buenos Aires,” Medicina, vol. 66, no. 2, pp. 113–118, 2006.
[25]
Programa para los asentamientos humanos, Estado de Las ciudades de América Latina y el Caribe 2012: Rumbo a una Nueva Transición Urbana, ONU Habitat, Nairobi, Kenya, 2012.
[26]
“Indicadores básicos—Argentina 2012,” Ministerio de Salud de la Presidencia de la Nación and Organización Panamericana de la Salud, Buenos Aires, Argentina, 2012, http://www.deis.gov.ar/publicaciones/archivos/indicadores_2012.pdf.
[27]
L. Weinstein and B. Ullery, “Identification of at-risk women for osteoporosis screening,” American Journal of Obstetrics and Gynecology, vol. 183, no. 3, pp. 547–549, 2000.
[28]
W. Ben sedrine, J. P. Devogelaer, J. M. Kaufman et al., “Evaluation of the simple calculated osteoporosis risk estimation (SCORE) in a sample of white women from Belgium,” Bone, vol. 29, no. 4, pp. 374–380, 2001.
[29]
W. B. Sedrine, T. Chevallier, B. Zegels et al., “Development and assessment of the osteoporosis index of risk (OSIRIS) to facilitate selection of women for bone densitometry,” Gynecological Endocrinology, vol. 16, no. 3, pp. 245–250, 2002.
[30]
P. Geusens, M. C. Hochberg, D. J. M. Van der Voort et al., “Performance of risk indices for identifying low bone density in postmenopausal women,” Mayo Clinic Proceedings, vol. 77, no. 7, pp. 629–637, 2002.
[31]
A. El Maghraoui, A. Habbassi, M. Ghazi et al., “Validation and comparative evaluation of four osteoporosis risk indexes in Moroccan menopausal women,” Archives of Osteoporosis, vol. 1, no. 1-2, pp. 1–6, 2006.
[32]
M. E. Anders, L. Turner, and J. Freeman, “Evaluation of clinical decision rules for bone mineral testing among white women,” Journal of Osteoporosis, vol. 2013, Article ID 792831, 6 pages, 2013.
