NON-COMPLIANCE WITH DRUG DOSING REGIMENS: AN UNDERESTIMATED BIAS IN DRUG …atar-vih.wdfiles.com/local--files/biblio/Non-compliance... · 2012-09-17 · NON-COMPLIANCE WITH DRUG DOSING

The experts on patient adherence

NON-COMPLIANCE WITH DRUG DOSING REGIMENS: AN UNDERESTIMATED BIAS IN DRUG TRIALS AND CLINICAL PRACTICE

Bernard Vrijens PhD, Chief Scientist Pharmionic Systems, Visé & University of Liège, Belgium

15 MARCH 2007 – BeAPP Brussels 2

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Need for a new discipline… Pharmionics

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Sources of Variances in Drug Response

Harter & Peck, Ann NY Acad Sci, 618: 563-571, 1991

manufacturing & distribution

prescribing

dispensing

Pharmionics

PK

PD drug response


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Definitions

Vrijens & Urquhart, 2005 Journal of Antimicrobial Chemotherapy

Acc

epta

nce

EXECUTIONof the drug regimen

PERSISTENCE (days)

ADHERENCE/COMPLIANCE

Drugprescription

Endprescription

Dis

cont

inua

tionQUALITY OF

Need a simple but robust taxonomy


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Reliable Assessment of Drug Exposure

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Vrijens & Urquhart, 2005 Journal of Antimicrobial Chemotherapy


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Literature on Pill Counts

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Introducing Advanced Technology on Monitoring Adherence

Medication Event Monitoring System ®from AARDEX (Advanced Analytical Research on Drug Exposure)


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A

Dosing Date (in Relative Dates)

Dos

ing

Tim

e (in

24

Hou

rs)

0 20 40 60 80

00

06

12

18

24

6 0123456 0123456 0123456 0123456 0123456 0123456 0123456 0123456 0123456 0

123456 0123456 012

Chronology plot

A “perfect” patient

0=Sunday;1=Monday; 2=Tuesday, …, 6=Saturday


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B


Dos

ing

Tim

e (in

24

Hou

rs)

0 20 40 60 80

00

06

12

18

24

123456 01

23456 0

12345

6 012345

6 0

123456

0

123456 0

12345

6 0

1234566 0

123456

0

123456 0

123456 0

12345

6

Chronology plot

A patient with delayed intake during the weekends



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C


Dos

ing

Tim

e (in

24

Hou

rs)

0 20 40 60 80

00

06

12

18

24

0

45 1

2

3

4

5

6 0

3

4

5

3

64

0

2

5 4

6

1

1

4

4

5

0

1 3

45

0

2 1

Chronology plot

A « random » patient


Drug holiday


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Chronology plot


Dos

ing

Tim

e (in

24

Hou

rs)

0 20 40 60 80

00

06

12

18

24

601234

56012345601234560

123

4

56

0

1

23

4

5

6

012

345

601

23


A non persistent patient


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Days

Per

cent

0 100 200 300

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Graphical display of the definitions

Persistence

Non acceptance

Non e

xecu

tion Adherence/compliance

Perfect adherence

Treatmentdiscontinuation

*# ��0�1�2.3...�� Source: AARDEX (Pharmionic database)


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Time to dropout (Days)

Per

cent

age

of p

atie

nts

0 100 200 300

0.0

0.2

0.4

0.6

0.8

1.0

CENTRAL-NERVOUS SYSTEMDIABETESGASTRO-INTESTINAL DISEASEHIVHYPERTENSIONOSTEOPOROSIS

Data on Persistence

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Vrijens et al., Clinical Pharmacology and Therapeutics 2005

Overall, 40% of patients will have discontinued the prescribed drug after 12 months


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Type of error

Time (days)

Frac

tion

of p

atie

nts

0 100 200 300

0.0

0.2

0.4

0.6

0.8

1.0

Details about quality of execution

Single day error in dosing regimen

�3 consecutive days with errors in dosing regimen

2 consecutive days with errors in dosing regimen

Non execution by type of dosing errors

Source: AARDEX (Pharmionic database)


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Cumulative proportion of pts affected by poor execution

Time (days)

Per

cent

age

of p

atie

nts

0 100 200 300

020

4060

8010

0

1234

…

12

One day without dosing



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Cumulative proportion of pts affected by poor execution

Time (days)

Per

cent

age

of p

atie

nts

0 100 200 300

010

2030

4050

60

1

2

3

4

…

12

Drug holiday (3 consecutive days without dosing)



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Weekend Effect

61

70.5

87.1

97.195.695.9

60.6

37.2

54.4

67.166.671.9

78.9

20

40

60

80

100

Probability of drug intake in the morning Probability of drug intake in the evening

Pro

b abi

lity

Baseline


Mon Tue Wed Thu Fri Sat Sun


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Patient id = 716 Treatment 400/300

Dosing Date

Dos

ing

Tim

e

0 100 200 300

2

2

3

3

4

4

56

012

2

3

3

4

4

5

5

6

0

0

1

1

2

2

3

3

4

4

5

5

6

6

0

0

1

1

2

2

3

3

4

4

5

5

6

6

0

0

1

1

2

2

3

3

4

4

56

0

0

1

1

2

2

3

3

4

4

56

6

0

0

1

1

2

2

3

3

4

4

5

5

6

0

1

1

2

2

34

4

5

5

6 0

0

1

2

2

3

3

4

4

5

5

6

6

0

0

1

1

2

2

3

3

4

4

51

1

2

2

3

3

4

4

5

5

6

6

0

12

2

3

3

4

4

5

6

6

0

1

1

2

2

3

3

4

4

5

5

6

6

0

0

1

2

2

3

3

4

4

5

6

01

23

3

4

4

5

5

6

0

0

1

2

2

3

4

4

5

5

6

6

0

0

1

1

2

2

3

3

4

4

56

6

0

0

12

2

34

4

5

56

1

2

2

46

6

0

0

1

1

234

4

5

5

6

0

1

1

23

3

456 0

0

1

1

2

2

3

3

456

6

0

1

1

23

3

456

1

23

3

4

4

562

2

4

4

5

5

6

6 0

1

1

2

2

34

4

56

6

0

0

1

1

2

4

5

0

6

2

2

3

3

456

0

23

3

45613

3

4

4

5

0

1

1

23

3

4

4

56

2

34

0

112

2

4 5

5

6

01

1

23

4

562

2

34

4

5

3

3

4

4

6

0

3456245

0

34563452456

23

3

6 0

1

2341456123

3

45 3

3

4

51

1

2

2

3

4

4

61

1

234561

VV VVV VVVV VVVVVVVVVVVVV VVVVVVVVVVVV VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

03:00

06:00

09:00

12:00

15:00

18:00

21:00

24:00

03:00

1 PK4 12 24 48

START END

03:00

06:00

09:00

12:00

15:00

18:00

21:00

24:00

03:00

2

3

4

5

Log1

0 V

iral L

oad

Deteriorating adherence – lose viral control

0=Sunday;1=Monday; 2=Tuesday, …, 6=SaturdayHIV


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The better the execution, the longer the persistence

Time (days)

Pro

porti

on o

f pat

ient

s

0 100 200 300

0.0

0.2

0.4

0.6

0.8

1.0

>90%

]80-90]%

]60-70]%

]50-60]%

<=50%

]70-80]%

PKC in HIV, N=1311pts


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Multi-steps Successful Intervention

Acc

epta

nce

EXECUTIONof the drug regimen

Dis

cont

inua

tion

MOTIVATIONS- physician/nurse- pharmacist- disease understanding- targeted intervention- word of mouth- (DTC promotion)

BARRIERS- negative TV/print- word of mouth- disinterest

Patient

History

-Other therapies- Family history- Way of life- Relation with health care system

EFFECT

Vrijens, et al., Pharmacoepidemiology and Drug Safety, 2006


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What can be measured, can be managed

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Measurement Guided Medication Management (MGMM)

REALISTIC & ACCURATE measurement of day by day execution

of the dosing regimen

Deming WE


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Individual example of successful MGMM

Identification of adherence problems and result of intervention

3456012456012345

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601234560123456012345

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25

VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV

Days on treatment

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Dos

ing

Tim

e

0 100 200 300

00:00

06:00

12:00

18:00

24:00

23

60

4

5 56

2 1 1

1

10

1234

4 45

60

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VVVVVVVVVVVVVVV VV V V

2 3 4 5 6 7


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Individual example of successful MGMM

Dosing Date

Dos

ing

Tim

e

0 100 200 300 400

VVVVVVVVVVVVVVVVVVVVVVVVV VVVVVVVVVVVVVVVV V VVV VVVVVVV VVVVVVVVVVVV VVV VVVV VVVV

03:00

06:00

09:00

12:00

15:00

18:00

21:00

24:00

03:00

ENDSTART

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Identification of adherence problems and result of intervention


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RE+

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Regions selected based on good collaboration between pharmacists and GP

MGMM tested in Lipid Lowering

Vrijens et al, Pharmacoepidemiology and Drug Safety, 2006


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~86%

~71%


P<0.01

Daily Percentage of adherers

Days since inclusion

%

0 50 100 150 200 250 300

6070

8090

100

InterventionNo Intervention

Differenceof 15%


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Time to dropout (Days)

Per

cent

age

of p

atie

nts

0 50 100 150 200 250 300

0.6

0.7

0.8

0.9

1.0

No InterventionIntervention

Persistence at300 days

87%

74%

P=0.002 (M-H)

Frac

tion

ofpa

tient

s

Non acceptance: 10 patients

Persistence=time to treatment discontinuation


InterventionNo Intervention



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Dosing Day (Relative to Visit 1)

Per

cent

of P

atie

nt W

ith In

take

-30 -20 -10 0 10 20 30

8085

9095

100

ReinforcedNo InterventionIntervention

No Intervention

30 DAYS BEFORE INTERVENTION 30 DAYS AFTER INTERVENTION


Execution = daily % of patients with drug intake among those who persist

P < 0.001


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Conclusion from MGMM case studies

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No 2 drugs will require the same solution!

Importance of using the right interventional tools


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Dosing Date

Dos

ing

Tim

e

0 20 40 60 80 100 120

03:00

06:00

12:00

18:00

24:00

03:00

Start V1 V2 V3 End fdur

Comp

V

Pers.

Dosing Date

Dos

ing

Tim

e

0 20 40 60 80 100 120

03:00

06:00

12:00

18:00

24:00

03:00


Comp

V V V V

Pers.

ALL of those 4 patients are 79% « compliant »!

Dosing Date

Dos

ing

Tim

e

0 20 40 60 80 100 120

03:00

06:00

12:00

18:00

24:00

03:00


Comp

V VVVVVVVVV

Pers.

Dosing Date

Dos

ing

Tim

e

0 20 40 60 80 100 120

03:00

06:00

12:00

18:00

24:00

03:00


Comp

V VVVVVVVVVVVVVVVVVVVVVVVV

Non Pers. Color code

2 doses1 doses0 doses

>2 doses


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Patient management model


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Breaking The Downward Spiral


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kr kd(t)Nt

kd (t) is the time-dependent discontinuation ratekr is the recruitment rate of naive patientsk’r is the re-recruitment rate of non-naive patientsk’m (t) is the time-dependent mortality of formerly treated patientskm (t) is the time-dependent mortality of treated patients

At Rt

k’r

k’m(t)

km (t)

Demokinetic model

Naive Treated Pts who stopped

Rate of drug consumption at time t is : At x Regimen x Compliance


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Time in Months

Num

ber o

f pat

ient

s ( u

nit =

100

000)

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15 years follow-up with 10,000 patients recruited/mo.

Perfect persistence

Observedpersistence

Time in Months

Num

ber o

f pat

ient

s ( u

nit =

100

000)

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

Time in Months

Num

ber o

f pat

ient

s ( u

nit =

100

000)

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

It is the single largest untapped value opportunity in the industry!

Recouping The Hidden Blockbuster

10K/mo

20K/moRecruit more

subjects

t1/2= 1yr

t1/2=10yr

Increasepersistence

40% increasein total exposure


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Thank you for your attention

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Documents

NON-COMPLIANCE WITH DRUG DOSING REGIMENS: AN UNDERESTIMATED BIAS IN DRUG …atar-vih.wdfiles.com/local--files/biblio/Non-compliance... · 2012-09-17 · NON-COMPLIANCE WITH DRUG DOSING