Nguyen et al developed several models for prediction of osteoporotic fracture risk, then used these to develop nomograms. These can individualize a patient's risk of fracture, which can help to determine the best way to reduce the risk. The authors are from the Garvan Institute of Medical Research at st. Vincent's Hospital and the University of New South Wales in Sydney, Australia.
Model 1 uses bone mineral density (BMD) at baseline.
Patient selection: adult male
Parameters:
(1) age in years, from 55 to 100 years
(2) bone mineral density T-score from +4 to -6
(3) number of prior fractures after age 50 years (>50)
(4) number of falls in past 12 months
points for age =
= (2.222 * (age)) - 122.22
points for T-score =
= (-7.5 * (T-score)) + 30
points for fractures =
= MIN(64,(number of fractures)*21.333)
points for falls =
= MIN(7,(number of falls)*2.333)
total score =
= SUM(points for all 4 parameters)
Interpretation:
• minimum score: 0
• maximum score: 193
• The higher the score the greater the risk of osteoporotic fracture.
|
Total Score
|
5-Year Risk
|
|
< 30
|
<1%
|
|
30 to 71
|
(0.09756 * (points)) - 1.9268
|
|
71 to 108.4
|
(0.004074 * ((points)^2)) - (0.3245 * (points)) + 7.125
|
|
108.4 to 166
|
(0.004804 * ((points)^2)) - (0.0576 * (points)) - 31.25
|
|
> 166
|
> 90%
|
|
Total Score
|
10-Year Risk
|
|
< 14
|
< 1%
|
|
14 to 55
|
(0.0701754 * (points)) + 0.0175
|
|
55 to 104
|
(0.004969 * ((points)^2)) - (0.2746 * (points)) + 4.074
|
|
104 to 134.5
|
(1.319 * (points)) - 108.2
|
|
134.5 to 168.3
|
(-0.0219 * ((points)^2)) + (7.499 * (points)) - 542.6
|
|
> 168.3
|
> 99%
|
