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“Known knowns, known unknowns, unknown unknowns…..

Ronald Dumsfeld

Senior Lecturer in Metabolic Bone Diseases

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Aims

• Known knowns?– We can do better

• But what is the incremental benefit?

• And at what cost?

• Known unknowns?– The risk is reversible

• But doesn’t everyone need a BMD?

• Unknown unknowns and unknown knowns?– Suggestions on the back of a postcard to Liz by next

Thursday morning!

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Risk Factors Previous fragility fracture

Investigations

Measure BMD

Normal Osteopenia Osteoporosis

ReassureLifestyle advice

Lifestyle adviceTreat if previous

fracture

Lifestyle adviceTreatment

IOF Case-finding Strategy

RCP Guidelines 1999

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Combining risk factors for prediction of fracture

0

1

2

3

4

5

6

7

8

9

10

-4 -3 -2 -1 0 1 2 3 4

Risk ratio versus the

general population

GR=2.6

GR=1.6

Z-score

% 50.0100.0 97.799.9 84.1 15.9 2.3 0.1 0.0

Study Aim: To compare a case-finding strategy that combined the information from validated risk factors (WHO

Strategy) with the current IOF strategy.

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Methods

• Baseline and follow-up data from nine prospective population-based cohorts– Rotterdam, EVOS/EPOS, CaMos, Rochester, Sheffield, Dubbo

(DOES), Gothenburg, Japan (Adult Health Study )

• All cohorts had BMD estimates undertaken at baseline and a risk factor assessment.

• Both IOF and WHO strategies used same risk factors comprising– a prior history of a fragility fracture– BMI <19kg/m2

– parental history of hip fracture– long-term use of oral glucocorticoids– rheumatoid arthritis– current smoking– alcohol ≥3 units daily.

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Methods of Comparison

• IOF - selects candidates for BMD tests on the basis of prevalent clinical risk factors. We estimated:– The number of BMD tests that would be required in cohorts of

women aged 50 years or more.– The hip fracture risk in women so identified.– Treatment offered when T-score ≤ -2.5.

• WHO – Used the WHO algorithm (combined BMD and risk factors):– Maintained approximately the same number of BMD tests at

each age as the IOF strategy.– Treatment offered when the computed 10-year probability of

hip fracture exceeded an intervention threshold derived for the UK.

• Simulation samples of 1000 women using the different identification strategies

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Probability of Fracture

10-year Probability of Fracture- Targeting of BMD -

0%

Frequency

0%

100%

35%

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Strategy Costs

Risk Assessment£5.76

BMD Scan£35

No BMD Scan

TreatmentDiscussion

£19.20

No TreatmentDiscussion

£5.76

TreatmentDiscussion

£19.20

Primary outcome – Costs of the IOF and WHO identification strategies based on a UK setting.

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Risk factors in the cohorts

0

10

20

30

40

50

60

Low B

MI

Ora

l GC u

se RA

Alcoho

l

Paren

tal h

ip fr

actu

re

Smoki

ngPrio

r fra

cture

Any >1

% Prevalence of risk factors (%)

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Hip fractures identified and costs of identification

Age DXA tests /1000

High risk women

identified /1000

Hip fractures expected*

Cost/identified hip fracture (£)

IOF WHO IOF WHO IOF WHO

50 450 26 41 <1 1 60,823 21,940

60 450 52 65 2 4 12,930 7,566

70 500 120 355 16 30 1,731 1,069

80 550 235 606 51 91 608 424

*in women at high risk

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Conclusions

• Compared to the IOF strategy, the WHO approach– identifies more patients at high risk of hip fracture– makes more effective use of BMD tests.

• At each age, the cost of the identification strategy per detected hip fracture is lower with the WHO approach.

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a. Risk amenable to intervention

Low BMD

Previous fracture

Use of glucocorticoids

b. Presence of risk factor does not adversely affect therapeutic

response

Age

Body mass index

Family history of fracture

Smoking and alcohol

Markers of bone turnover

c. Uncertain effects

Neuromuscular incompetence

Liability to falling

Identification of reversible risk

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Targeting and Treatment EfficacyIs the treatment appropriate for the risk regardless of the underlying risk factors?

0

1

2

3

4

5

6

Inci

den

ce (

%)

<80y with OP >79 with at least 1 riskfactor

Placebo Risedronate

RR 0.6 (0.4-0.9)

RR 0.8

2.5mg, 5mg daily for 3 years McClung et al NEJM 2001

N=5445 N=3886

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Fracture Prediction by Non-BMD Risk Factors

Fracture Probability (% in 10y)

Mean Hip BMD

T-score Mean age

0-5 5-10

10-15 0.93 -0.13 77.8 15-20 0.85 -0.75 78.5 20-25 0.76 -1.50 80.3 25-30 0.78 -1.33 79.5 30-35 0.74 -1.67 79.6

35-40 0.66 -2.33 82.1 40-45 0.69 -2.08 80.6 45-50 0.67 -2.25 80.6 50-55 0.57 -3.08 82.1

Johansson et al JBMR 2004

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Background

• Treatments for osteoporosis are commonly targeted to patients with low bone mineral density– Low BMD is associated with increased fracture risk– An entry criterion for phase III studies in

development of most osteoporosis therapies– DXA measured low BMD a pre-requisite to ensure

the efficacy of osteoporosis therapies?

• The WHO tool for fracture prediction estimates an individual’s probability of fracture in the next 10 years from clinical risk factors, with or without BMD measurement

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Methods (1)Aims and Study Population

• Aim– To determine if patients identified at high risk by the

algorithm are responsive to anti-resorptive treatment

* Excludes fractures of the hands, feet, ankle and skull

3210

12

10

8

6

4

2

0

Time on study (years)

Cu

mu

lati

ve I

nci

de

nce

of

Fra

ctu

res*

(%

)

HR 0.77, 95%CI 0.64-0.93

Placebo

Clodronate

• Randomized, double-blind, placebo controlled trial over 3 years

• Women aged at least 75 years unselected for osteoporosis or low BMD

• Clodronate (Bonefos®) 800mg/day orPlacebo

• Fractures ascertained at 6-monthly visits and confirmed against source documents or radiographs

McCloskey et al, JBMR 2007

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Methods (2)WHO Fracture Probability Model

• Age

• Sex

• Femoral neck BMD

• Previous fragility fracture after age 50

• Body mass index

• Ever use of glucocorticoids

• Secondary osteoporosis (e.g., rheumatoid arthritis)

• Parental history of hip fracture (Paternal)

• Current cigarette smoking

• Alcohol intake 3 or more units/day

Data not captured at entry

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Methods (3)Treatment Interaction

• Complete baseline data available in 3974 women (76.2% of cohort)– Efficacy of clodronate HR 0.76 (0.63-0.93)

• 10-year probability of osteoporotic fracture computed by WHO model– Without femoral neck BMD (expressed as the T-score)– With femoral neck BMD

• Fracture rates examined by quintile of fracture probability

• Interaction between treatment efficacy and fracture probability explored by Poisson regression

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Baseline Characteristics Clodronate

(N=2016) Placebo

(N=1958)

Age (years) 79.83.7 79.73.7

BMI (kg/m2) 26.84.4 27.04.7

Femoral Neck BMD (g/cm2) 0.650.12 0.650.12

Femoral neck BMD T-score -1.740.98 -1.720.99

Previous fracture (%) 22 24

Family history (%) 5 6

Current smoking (%) 6 6

Corticosteroids (%) 9 10

RA (%) 2 2

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Incidence of osteoporotic fracture and estimated 10-year probability

Without BMD

0

2

4

6

8

I II III IV V

Placebo

Clodronate

Fractures /100 patient-years

Quintiles of probability

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Incidence of osteoporotic fracture and estimated 10-year probability

0

2

4

6

8

I II III IV V

With BMDPlacebo

Clodronate

Fractures /100 patient-years

Quintiles of probability

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Interaction between treatment and fracture probability (without BMD)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 10 20 30 40 50 60 70 80

10 year probability

HR

tre

atm

en

t v

ers

us

pla

ce

bo

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Interaction between treatment and fracture probability

Prcenti

0

0.5

1.0

1.5

13 15 18 24 30

Without BMD

Probability (%)

Percentile 10 25 50 75 90

HR

P = 0.043

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Interaction between treatment and fracture probability

10 12 16 22 30

0

0.5

1.0

1.5

HR

Probability (%)

With BMD

Percentile 10 25 50 75 90

P = 0.10

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Study Limitations

• Not all of WHO risk variables collected at baseline

• Community-dwelling women in the UK unselected for osteoporosis– Applicable to other populations?

• Clodronate not licensed for treatment of osteoporosis– Applicable to other agents?

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Conclusions

• Women identified to be at high risk of future fracture, as predicted by the WHO fracture risk algorithm:

• Are responsive to treatment with clodronate

• This response occurs in the presence or absence of BMD assessment in the risk model