Description

Koh et al developed a simple index to identify an Asian woman who is at risk for osteoporosis. This can help identify a woman who may benefit from interventions to increase bone density. The authors are from the Osteoporosis Self-Assessment Tool for Asians (OSTA) Research Group.


 

NOTE: The OST index (based on weight and age alone as described on page 702) is discussed later in this chapter).

 

Inclusion: more than 6 months postmenopausal and non-Caucasian

 

Exclusions (page 700): cancer metastatic to bone, renal osteodystrophy, osteogenesis imperfecta, bisphosphonate therapy, fluoride therapy, calcitonin therapy, Paget's disease of bone, parathyroid disease, osteomalacia, bilateral hip replacement or disease, one or both ovaries removed.

 

Parameters:

(1) current estrogen therapy

(2) current thyroid medication

(3) any bone fracture after age 45

(4) vertebral fracture after age 45

(6) racial group

(7) location where examined

(8) age

(9) body weight in kilograms

Parameter

Finding

Points

current estrogen therapy

no

0

 

yes

1

current thyroid medication

no

0

 

yes

2

bone fracture after age 45 years

no

0

 

yes

-1

vertebral fracture after age 45 years

no

0

 

yes

-1

racial group

Chinese

-1

 

Thai

2

 

other

0

location where examined

Malaysia

2

 

Hong Kong

-1

 

Taiwan

-2

 

other

0

 

points for age =

= (-1) * INT((2 * (age in years) / 10)

 

points for body weight in kilograms =

= INT((2 * (weight) / 10)

 

where:

• Since a vertebral fracture is a bone fracture. there are 2 ways to score a single vertebral fracture. One way is to assign -2 points (-1 for bone fracture, -1 for spinal fracture). The second is to assign -1 for the spinal fracture and -1 only if there is another kind of bone fracture. The latter will be used in the implementation.

• The point assignment for age and weight is taken from the text on page 702.

• What constitutes thyroid medication was not stated.

• Patients were examined in 8 countries. This explains the "other" groups in the table.

 

total score =

= SUM(points for all 9 parameters)

 

Interpretation:

• An index <= -1 is associated with high risk.

• An index > -1 is associated with low risk.

 


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