Does Shared Treatment Decision-Making
Improve Asthma Adherence and Outcomes?
Only ~50% of patients take asthma
medications at effective doses
Documented problems:
„ Under-use of controller medications
„ Over-use of relievers & OTC medications
„ Poor inhaled medication technique
„ Failure to fill/refill prescriptions
„ Failure to keep medications available
when and where they are needed
Supported by grants from the National Heart, Lung and Blood Institute
1R01 HL69358 (PI: SWilson) and 1R18 HL67092 (PI: ASBuist)
Known contributors to non-adherence
„
„
Patient
¾ Younger age
¾ Low socioeconomic status
¾ Lack of education
¾ Memory problems
¾ Lack of understanding of the disease
¾
¾
¾
¾
¾
¾
„
„
„
„
„
Interaction is directive;
Clinician makes the treatment decision
Evidence-based management usually follows a
traditional model
Higher cost
Complexity, more frequent dosing
Properties (bad taste, more side effects, etc.)
Failure to explain side effects
Failure to analyze patient’s medication-taking
behaviors
Failure to address the patient’s individual situation and
preferences
Mutual exchange of information and treatment
preferences between clinician & patient
Both participate in treatment decisions
Each brings unique knowledge to the interaction
Informed decision-making model:
„
„
Clinician provides information to the patient
Patient makes the decision
MD
Pt
Design of the BOAT trial
„
Three-arm, randomized controlled trial
„
„
„
MD
Pt
Hypothesis:
Involving patients in treatment decisions should result in:
¾ Better adherence to treatment
¾ Better asthma control
¾ Greater patient satisfaction
Pt
MD
„
Inadequate monitoring
Shared decision-making model:
„
Traditional model:
Longer duration of treatment
Physician-patient relationship
¾
„
„
Regimen
¾
„
Models of Clinician-Patient Interaction
„
SDM = shared decision making care management
MBG = guidelines-based traditional care management
UC = usual medical care
Data collection
„
Baseline and 12-mos. post-randomization
¾ Questionnaire
„
12-mos. pre and 24 mos. post-randomization (36 mo.)
¾ PFT
¾ Asthma medications dispensed
¾ All health care utilization
1
BOAT study hypotheses regarding
adherence and disease outcomes
SDM > MBG
SDM > UC
Study Outcomes
„ Primary
„
¾ Adherence to asthma
medications
¾ Asthma-related quality
of life
¾ Asthma-related health
care utilization
Secondary
¾
¾
¾
¾
¾
¾
¾
¾
Both the SDM & MBG Interventions:
„
¾
Target patients with poorly controlled,
moderate-severe asthma
Involve 2 in-person sessions, approximately 1
mo. apart, plus 3 follow-up calls at 3 mo.
intervals
¾
Conducted by asthma care managers:
ƒ
ƒ
ƒ
ƒ
„
Clinical pharmacists
Nurse practitioners and registered nurses
Physician assistants
Respiratory therapists
Parallel written protocols (scripts) guide both
SDM and MBG clinician-patient interactions
¾
¾
Structured to enable tailoring to the individual patient
Instructional aides and worksheets are included in the
interventionist manual
Asthma control
Use of reliever medications
Symptom-free days;
Lung function
Satisfaction with asthma care
Preferences, values, &
attitudes towards adherence
Total asthma health care
utilization
Asthma-related health care
costs
SDM and MBG Interventions*
Set the Stage
• Establish rapport
• Describe session schedule
• Describe shared decision making approach
Gather patient information
• Asthma symptoms
• Perceptions of control
• Medication use
• Use of alternative therapies
• Environmental triggers
• Patient goals & preferences
Provide information
• Assess understanding of asthma
• Review asthma and how it is treated
• Confirm comprehension
Negotiate (SDM)/Prescribe (MBG)
• Summarize patient goals and priorities
• Review PFTs with patient
• Assess symptom control using objective criteria
• Determine asthma severity per GINA guidelines
• Define medication preferences
• Discuss +/- of each treatment option per patient
goals and preferences
• Negotiate a treatment decision
Wrap Up
• Write Rx
• Give Asthma Action & Management Plan
• Teach proper inhaler use
• Give asthma diary
• Schedule follow-up appointment
* White = MBG and SDM
Gold = SDM only
2
Inclusion Criteria
Exclusion Criteria
„
Recent ED/hospital visit for asthma and/or
evidence of over-use of rescue medication
„
Mild intermittent/seasonal asthma
„
18-70 years of age
„
Regular use of oral corticosteroids
„
KFHP member ≥ 1 year
„
Currently receiving asthma care-management
„
Self-reported, doctor-diagnosed asthma
„
„
Currently Rxed asthma medications
Not able to speak, read, and understand
English
„
Meets obstruction reversibility criterion
„
Planning to move out of area within two years
„
One or more asthma control problems
(ATAQ score ≥1)
Randomization*
SDM
Eligible
Patients
MBG
Demographic characteristics*
N=613
(N= 204)
Age
(N= 205)
(N=613)
Gender
Ethnicity
20
42
51-70 yrs.
38
Male
44
Female
56
Hispanic
Asian
UC
Native
Hawaiian/Pacific
Islander
(N= 204)
* Adaptive randomization algorithm (Pocock, 1983) - ensures better than chance balance
and increases likelihood of better than chance balance on correlated characteristics.
%
18-34 yrs.
35-50 yrs.
%
Level of
education
80%
Annual
family
income
4
10
8
Black/African
American
16
White/Caucasian
62
38%
2
HS Diploma/GED
16
Technical/Some College
43
4-Year Degree/BA/BS
22
Graduate Degree
17
≤ $20,000
8
$20,001 - $40,000
21
$40,001 - $60,000
25
$60,001 - $80,000
18
≥$80,001
24
DK/Refused To Answer
*
Baseline asthma status*
< High School Diploma
4
No significant group differences.
De facto medication regimen and
asthma control*
60
60
50
Percent
40
SDM
MBG
UC
30
20
10
1/
40
SDM
30
MBG
20
UC
10
0
>
Percent
50
ek ail y ail y
D
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ek
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e d ed
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/m < w r e
o
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£ but r m
o
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o
o
% % 0%
h
n t kly
6
80 80
<
> 60mo≤ ee
/
W
2x
Symptom Frequency
Nocturnal Symptoms
FEV1 % predicted
* No significant group differences in symptom frequency, nocturnal symptoms,
or FEV1 % predicted at baseline.
0
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nt
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Medication regimen
Asthma Control
* No significant group differences at baseline.
3
• Did the SDM patients’ medication choices differ
from the MBG care managers’ guidelines-based Rx?
Medication
SDM
N=191
MBG
N=186
Beclomethasone 80
90 (50%)
Fluticasone 220
78 (43%)
53 (30%)
Other ICS2
13
(7%)
17 (10%)
181 (95%)
178 (96%)
Any ICS
Leukotriene modifier
Any Controller3
pvalue1
CMA = Number of days’ supply of a medication
dispensed/365 days
0.03
¾ Proportion
of days on which medication was
available for use on Rxed regimen
0.67
14
(8%)
0.94
4 ( 2%)
1
(1%)
0.37
181 (97%)
1.00
186 (97%)
„
108 (61%)
14 ( 7%)
Theophylline
Adherence measure = Continuous Measure of
Medication Acquisition (CMA)
¾A commonly used indicator of adherence to
the intended daily regimen
„
Data from the HMO’s pharmacy database
1. Chi-square or Fishers exact test.
2. Includes Beclomethasone and Fluticasone at lower strengths, and Budesonide.
3. Includes ICSs, leukotriene modifiers, and theophylline; excludes LABAs and oral
prednisone.
¾ ~95% of patients obtain all their medications
from the HMO pharmacy
Cumulative medication acquisition (CMA) values
pre and post randomization, by experimental group
Conclusions:
For non-adherent patients with poorly
controlled asthma --
CMA index – Mean (SD)
Baseline Yr.
Any ICS
Any Controller
Follow-up Yr.
Any ICS
Any Controller
UC
MBG
SDM
N
N=203
N=203
N=204
N=610
0.32
(0.32)
0.32
(0.31)
0.33
(0.34)
N=204
N=205
N=204
0.41
(0.47)
0.38
(0.37)
0.40
(0.43)
N=203
N=202
N=204
0.39
(0.37)
0.54
(0.36)
0.62
(0.38)
N=204
N=205
N=204
0.49
(0.52)
0.59
(0.45)
0.69
(0.45)
p-value
„
0.8986
N=613
0.9490
N=609
SDM vs MBG p=0.0162
SDM vs UC p<0.0001
MBG vs UC p<0.0001
asthma
their current level of disease control
„ the medical rationale for asthma
treatment.
„
N=613
„
SDM vs MBG p=0.0095
SDM vs UC p<0.0001
MBG vs UC p=0.0014
Conclusions:
„
For non-adherent patients with poorly controlled
asthma, care management that utilizes a
shared clinician-patient approach to selection of
the treatment regimen significantly improves
adherence to asthma controllers over a one
year period when compared with both:
„
„
¾
usual medical care, and
traditional, prescriptive care management
Intervention effects did not differ as a function of
ethnic group (Caucasian, Asian and African
American)
Involving patients in a meaningful
way in treatment decisions does not
result treatment regimens that
conflict with standard guidelines,
assuming patients have a basic
understanding of:
Conclusions - continued
„
Clinical approaches of asthma care managers
can be shaped such that treatment decision
making is shared with the patient in a
meaningful way.
„
„
This required use of a detailed intervention protocol,
training, and ongoing feedback.
Patients evaluate their own vs. the clinician’s
influence on treatment decisions differently
when they experience a shared decision
making approach than when they experience
prescriptive care management
4
Process outcomes
„ Does shared decision-making lead to:
ƒ better asthma control?
ƒ better asthma-related quality of life?
ƒ reduced asthma health care utilization?
ƒ increased patient satisfaction?
5
Protocol
Adherence
Protocol
Adherence
QC rater
4
Mean rating
Questions being investigated by
analyses in process
• How closely did interventionists follow the protocol
• Who made the treatment decisions?
Decision
Roles
Decision Roles
3
*
p=0.47
*
* p<0.001
1
„ Are adherence outcomes mediated by patient
perceptions of their influence on treatment
decisions?
„ Are disease outcomes mediated by medication
adherence?
QC rater
Care manager
2
*
Patients
0
SDM
MBG
Rating scales:
Protocol Adherence 1 = Relevant elements not covered
3 = All elements covered, but some briefly,
incompletely, or inadequately
5 = All topics covered completely, thoroughly,
and accurately
Decision Roles - Treatment decisions were made by:
1 = Care manager alone
2 = Care manager mostly
3 = Patient and care manager equally
4 = Patient mostly
5 = Patient alone
Investigators
Sandra Wilson, PhD, PI (PAMFRI, SUSM)
Sonia Buist, MD, PI (OHSU, CHR)
William Vollmer, PhD (CHR)
Tom Vogt, MD (CHR)
Nancy L. Brown, PhD (PAMFRI, SU)
Philip Lavori, PhD (SUSM)
Margaret Strub, MD (TPMG)
Stephen VanDenEeden, PhD (KRFI/DOR)
Clinical Site Co-investigators
Faith Bocobo, MD (TPMG)
Christine Fukui, MD (TPMG)
Donald German, MD (TPMG)
John Hoehne, MD (TPMG)
Matthew Lau, MD (TPMG)
Myngoc Nguyen, MD (TPMG)
Consultants
Amiram Gafni, PhD
Elizabeth Juniper, PhD
Cynthia Rand, PhD
Sean Sullivan, PhD
Kevin Weiss, MD
(SDM only)
5
Post-randomization CMA indices for
inhaled corticosteroids, by group1
Post-randomization CMA indices for all
asthma controllers combined, by group1
Overall p<0.00012,3
3
Overall p<0.00012,3
CMA FOR CONTROLLERS
1 .0
Mn = 0.62
N = 204
0 .5
Mn = 0.54
N = 202
Mn = 0.59
N = 205
Mn = 0.49
N = 204
0
MB G
SD M
GROUP
MB G
UC
Pre-randomization CMA for all controllers,
by ethnicity, within relevant sites
1.
2.
3.
UC
N = 504. Excludes 4 patients with mild persistent asthma, for whom no controller was prescribed.
Overall test of group differences, Wilcoxon/Kruskal Wallis test.
Multiple comparisons: SDM vs. MBG, p=0.02; SDM vs. UC, p<0.0001; MBG vs. UC, p=0.0023.
Post-randomization CMA for all controllers,
by group, separately for Whites and Asians.
3
Northern CA & Hawaii
Northern CA & Portland
SD M
GR OU P
N=504. Excludes 4 patients with mild persistent asthma for whom no ICS was prescribed.
Overall test of group differences, Wilcoxon/Kruskal Wallis test.
Multiple comparisons: SDM vs. MBG, p=0.02; SDM vs. UC, p<0.0001; MBG vs. UC, p<0.0001.
White
Asian
3
CMA 2 for Controllers
2
1
2
2
1
Mn = 0.36
N = 59
0
0
0
Mn=0.66 Mn=0.74 Mn=0.52
N = 68
N = 68
N = 69
MBG
African Am erican
Asian
W hite
2
1
Mn = 0.47
N = 205
1
Mn = 0.41
N = 344
Mn = 0.40
N = 94
CMA 2 for Controllers
3
3
CMA 2 for Controllers
Mn = 0.69
N = 204
1
Mn = 0.39
N = 203
0 .0
1.
2.
3.
2
CMA 2 for Controllers
CMA FOR ICS
1 .5
W hite
SDM
G roup
UC
0
Mn=0.78 Mn=0.87 Mn=0.52
N = 18
N = 19
N = 22
MBG
SDM
G roup
UC
Regression model
Group comparison: p-value <=0.0001. Group x Ethnicity interaction: p-value = 0.4478
ETHN IC ITY
Post-randomization CMA for all controllers, by group,
separately for Whites and African Americans
White
African American
3
CMA 2 for controllers
CMA 2 for Controllers
3
2
1
0
Mn = 0.63
N = 113
MBG
Mn = 0.74 Mn = 0.53
N = 115
N = 116
SDM
UC
2
1
0
Mn = 0.55
N = 33
MBG
Mn = 0.51 Mn = 0.34
N = 32
N = 29
SDM
UC
Group
Group
Regression model
Group comparison: p-value <=0.0001; Group X Ethnicity interaction: p-value = 0.6993.
6