Short and Precise Patient Self-Assessment of Heart Failure Symptoms
Using a Computerized Adaptive Test (HF-CAT)
Rose et al: Heart Failure CAT
Matthias Rose MD PhD 1,2,3, Milena Anatchkova PhD 1, Jason Fletcher PhD 4,
Arthur E. Blank PhD 4, Jakob Bjørner MD PhD 5, Bernd Löwe MD PhD 3,
Thomas S. Rector PhD 6, John E. Ware PhD 1,7
Downloaded from http://circheartfailure.ahajournals.org/ by guest on June 17, 2017
1
Department of Quantitative Health Sciences, University of Massachusetts, Worcester, MA,
USA
2
Department of Psychosomatic Medicine, Charité – University Medicine Berlin
Berlin,
in
n, Germany
3
University Medical Center Hamburg-Eppendorf and Schön Klinik
nik Hamburg-Eilbek,
Ham
H
ambu
am
burg
bu
rg-E
rg
-Eil
-E
ill
Germany
4
Department of Family and Social Medicine, Albert Einstein College
llegee of M
Med
Medicine,
edic
ed
iciin
ic
in Bronx, NY,
USA
5
3i QualityMetric, Lincoln,
L
RI, USA
6
VA Medical Centerr and Department of Medicine, University of Minnesota, Minneapolis,
Min
n
MN,
USA
7
John Ware Research
h Group, Incorporated, Worcester, MA, USA
Correspondence to
Matthias Rose
Department of Psychosomatic Medicine,
Charité – University Medicine Berlin, Germany
Charitéplatz 1
10117 Berlin, Germany
office +49 30 450 553002
fax +49 30 450 553989
[email protected]
Journal Subject Codes: 110
DOI: 10.1161/CIRCHEARTFAILURE.111.964916
Abstract
Background—Assessment of dyspnea, fatigue and physical disability is fundamental to the
monitoring of patients with heart failure (HF). A plethora of patient-reported measures exist,
but most are too burdensome or imprecise to be useful in clinical practice. New techniques
used for computer adaptive tests (CAT) may be able to address these problems. The purpose
of this study was to build a CAT for patients with HF.
Methods and Results—Item banks of 74 queries (‘items’) were developed to assess selfDownloaded from http://circheartfailure.ahajournals.org/ by guest on June 17, 2017
reported physical disability, fatigue and dyspnea. All queries were administered to 658 adults
with HF to build three item banks. The resulting HF-CAT was administered to 100 ancillary
n, the physical
physica
caal function and
HF-patients (NYHA I 11%, II 53%, III&IV 36%). In addition,
rtnes
esses
s-of
sof-b
of
-bre
-b
reat
re
athh
at
vitality domains of the SF-36 questionnaire, an established shortness-of-breath-scale
(SOB),
Failur Questionnaire (MLHFQ) were app
and the Minnesota Living with Heart Failure
applied. The HFo
ook
3:09r1:52 minutes to complete and score. All HF
F
CAT assessment took
HF-CAT
scales
demonstrated good construct validity through high correlations with the corresp
corresponding SF-36
r
r=-.87),
vitality (r=-.85) scales, and the SOB scale (r=.84
4 Simulation
physical function (r=-.87),
(r=.84).
HF CAT scales over a larg
studies showed a more precise measurement of all HF-CAT
larger range than
comparable static tools. HF-CAT scales identified significant differences between patients
classified by the NYHA symptom criteria, similar to the MLHFQ.
Conclusions—A new CAT for HF patients was built using modern psychometric methods.
Initial results demonstrate its potential to increase the feasibility, and precision of patient selfassessments of symptoms of HF with minimized respondent burden.
Clinical Trial Registration—URL: http://www.projectreporter.nih.gov. Unique identifier:
1R43HL083622-01.
Key Words: heart failure, patient-reported outcomes, computer adaptive tests
The cardinal manifestations of heart failure (HF) are dyspnea and fatigue, limited tolerance of
physical activity, fluid retention, pulmonary congestion and peripheral edema. Therefore, HF
is a clinical diagnosis that is largely based on physical examination and a careful history about
typical subjective symptoms in the presence of cardiac dysfunction (1). A patient-centered
measurement approach is particularly important in HF, to provide clinicians with tools to help
them to monitor the syndrome, to compare improvements under different forms of therapy,
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and to identify risk of deterioration. The NYHA classification has been used for this purpose,
rare
ra
rreely
y uused
seed ou
ooutside
u
but is being criticized for its questionable reliability (2,3) andd rarely
clinical
studies or specialized units.
e
elf-assessments
have been shown to be the more reliable aassessments of
Generally, patient self-assessments
m which is one reason for a growing interest in subjectivee health status
ms,
subjective symptoms,
measures from thee scientific community, clinical practitioners, as well as from the
industry (4,5). Self-assessed symptoms are used to predict declines in health status of patients
with HF (6), total expenses for HF care (7), hospitalization or even mortality (8,9). Their
widespread use has been recommended to increase quality of care (10), and 30% of all new
drug developments use Patient-Reported Outcomes (PROs) as their primary or co-primary
endpoint (11).
However, with traditional methods, a comprehensive and reliable ‘static’ measure is likely to
be long and time-consuming to administer and score. If questionnaire data need to be
analyzed manually assessments become cost-prohibitive for use in routine clinical practice,
and individual patient reports cannot be provided timely. Short-forms limit the respondent
burden, but often show more ceiling- or floor effects and lack the precision required at the
individual patient level (12,13). Measurement precision to guide individual decision-making
must be substantially higher than for group comparisons, because true change must be
separated from measurement error for every single assessment (13). For example, if a
confidence interval of 95% is required, a traditional tool with good psychometric properties
for group comparisons (e.g. with Cronbach Į=.80) would only allow for interpretation of
score differences of almost one standard deviation when used for an individual (14).
Moreover, classic psychometric methods cannot be used to determine the measurement
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precision for an individual measurement. As a result, none of the existing tools has become a
cc
standard measure in clinical practice (15,16). Enhancing the precision, ac
accessibility
and
oul
uldd ma
make
k heart failure
ke
interpretability of patient reported outcome (PRO) measuress co
could
e
and effective in meeting patient care needs.
management more efficient
With the presentedd study we apply computerized adaptive testing (CAT
(CAT) methods, a
o
ology
(17) which is used widely in educational testing (18).. We aimed to
measurement technology
build a system which will allow routine, comprehensive assessment of pathognomonic
symptoms. The use of CAT techniques also promise to provide more precise measures, with
fewer items, and an effective resolution to the classic conflict between practicality and
precision faced by traditional measurement methodology (12). CATs tailor each assessment to
the individual’s status on what is being measured, applying only items which are most
appropriate for her/his current health status. Responses to each CAT-item direct the choice of
the following CAT-item towards the most informative for this particular assessment. A
patient indicating higher levels of disability within the first questions would only be asked
about this level of ability. Omitting the use of uninformative items not relevant for a given
functional limitation focuses the assessment, decreases the respondent burden, and increases
the measurement precision achievable with a given number of items.
CATs select the items out of a larger item bank representing the entire range of the construct
being measured. Most of the item banks are built upon the principles of the Item-Response
Theory (IRT). The National Institutes of Health (NIH) are intensively promoting use of these
methods to develop a comprehensive Patient-Reported Outcomes Measurement Information
System (PROMIS) as part of their roadmap initiatives (http://nihroadmap.nih.gov/). Authors
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of this paper are part of the PROMIS initiative, which aims to provide a standard assessment
for generic health status measures in the near future (19).
dy was to develop CATs for dyspnea, fatigue and physical function
fu
The goal of this study
for the
n with HF, and to evaluate their acceptability, precision andd validity.
nts
assessment of patients
Methods
Development of the items
After review of the relevant literature we developed a set of 74 patient questions (items)
covering the three primary physical impairments commonly reported by patients with HF:
physical function/disability (24 items), dyspnea (30 items) and vitality/fatigue (20 items). The
queries were designed to be short enough to fit on a portable phone screen for home
assessments (Figure 1). Items were selected to represent the entire continuum of each aspect
of HF from no to severe impairment. All three item banks have been scored in the direction
that higher scores indicate more impairment (i.e. physical disability, fatigue, and dyspnea).
The item bank development was performed separately for each of the three domains of
physical function, dyspnea, and fatigue following the same procedures as described in
previous studies (20,21). After the item banks had been developed we used them as a basis for
a CAT. A new software solution was developed to work on a Personal Digital Assistant. The
CAT logic can be set to stop after the measurement reaches a particular precision or after a
maximum of items had been administered. For this study phase the CAT was set to assess
each of the three different domains with a standard error of SE < 3.3 (corresponding to a
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reliability of Cronbach Į > .90 for samples with a standard deviation of 10) or a maximum
number of 7 items per scale.
Participants
T item bank development (IB sample) were collected via thee Internet from
The data for the CAT
d
dults
Y
English speaking adults
with HF. All respondents were recruited by YouGov. YouGov
uses a
l matching
t hi for
f the
th selection
l ti off study
t d samples
l ffrom pools of opt-in
methodology called sample
respondents (22). Sample matching starts with an enumeration of the target population. For
patient recruitments, the target population is all adults with similar sociodemographic
characteristics like patients with a particular condition, as enumerated in consumer databases
(e.g. maintained by Acxiom, Experian, and InfoUSA). Then a random sample is drawn from
the target population. Finally for each member of the target sample, a matching member of the
internet pool of opt-in respondents is selected, resulting in a “matched sample”. Matching was
based on age, gender and race. The resulting matched sample has similar characteristics to the
target population and, will have similar properties to a true random sample. For this study
14,028 adults have been approached until the target number of patients with heart failure had
been enrolled. All newly developed items were administered randomly.
The same data collection method and vendor has been used for many similar projects,
including a NIH roadmap initiative for the development of generic PRO tools
(www.nihpromis.org). To ensure a sufficient distribution of responses for the item parameter
estimation, we used a quota of 1/3 of patients with minor, medium, and severe impairment
based on one screening question describing the level of impairment analogous to the NYHAclassification (I/II/•III).
To help ensure the quality of the data we applied the following exclusion criteria: (a) average
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answering time per item was less than 5 seconds, (b) subjects who did not indicate they had
ndi
dica
ccaatee tthat
haat th
thee HF diagnosis
HF and one underlying cause for HF, (c) subjects who did not indicate
y
((d)) last visit to a pphysician
y
re th
han 6 m
onth ago, or (e)
on
was given by a physician,
was more
than
months
d not indicate at least one drug used for the treatment of HF (diuretics,
current medication did
o
ockers,
digoxin).
ACEI or ARB, ȕ-blockers,
racteristics of the HF-CAT
HF CAT different simulation studies were conducted as
To examine the characteristics
described earlier (20,23). These analyses are based on the real data provided for all items in
the bank by the patients in the online survey. Only small subsets of those item responses are
used to estimate the patient score for the CAT simulation (in IRT terms called ‘theta score’).
The quality of the items in the bank defines the precision of the score at different ranges. The
‘test information curve’ identifies floor- and ceiling effects and if the measurement range of
the tool fits to the symptoms of the sample. To illustrate this for the HF-CAT, the precision of
the score estimate was plotted as a function of the patient scores (20).
To evaluate the construct validity of the HF-CAT, items from the following established tools
were also included in the data collection: the SF-36® Health Survey scales for Physical
Functioning (PF) and Vitality (VT) (24), four items from the Medical Health Outcomes
Survey (HOS) to assess Shortness of Breath (SOB) (25) and the Minnesota Living with Heart
Failure Questionnaire (26) (MLHFQ, 21 items) as a legacy tool for measuring HF as indicated
by patients’ perceptions of its overall effects on their lives.
A separate sample of 100 consecutive participants was recruited for the validity test
conducted at the heart failure clinic of the Montefiore Medical Center, Bronx, NY (MMC
sample). The clinic was selected as it usually does not use PRO assessments, and
predominantly serves a low income, diverse population. We considered this environment as
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particularly challenging to test a new technology, assuming relatively low health literacy
ic pr
prop
op
per
erti
t es w
ti
wo
o
levels. In addition, we felt that an evaluation of psychometric
properties
would
be more
p as the validityy of the IRT assumptions
umpt
ppttio
ions
ns have
hav
avee bbeen evaluated
relevant in a less educated sample,
o
opment
d (Tab
b 1). Patients
already in the development
sample, which was affluent and well-educated
(Table
g
gnosed
y Consenting
with previously diagnosed
heart failure were invited to participate in the study
study.
s
sked
u (Personal
participants were asked
to complete the actual HF-CAT on a hand-held compu
computer
Digital Assistant, PDA) and a series of paper- and pencil-assessments including sociodemographic questions, the MLHFQ, and a survey evaluation the experience with the HFCAT. All participants completed both instruments. Participants were randomly assigned to
one of two groups within a cross-over design where the order of presentation of the HF-CAT
assessment and the MLHFQ was counterbalanced. Patients were placed in the waiting area
and asked to follow the standard instructions provided for each measure.
Medical information, including the NYHA class was extracted from the medical files. The
NYHA class is determined routinely for all patients at every visit at the MMC Heart Failure
Clinic based on the clinical assessment of the treating physician. The NYHA class was
determined without knowledge of the results of patient self-assessments. Patients gave written
informed consent and received a $25 incentive for their participation in the study.
Results
Samples
After applying the inclusion and exclusion criteria, the final item development sample (IB
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sample) consisted of 658 participants, 60r13 years old (49% female) who had experienced
g conditions bbeside
e
es
HF for 8.8r7.9 years (Table 1). Patients reported the following
their HF:
ardiiom
omyo
yopa
yo
path
pa
thyy, 14% valvular
th
43% coronary heart disease, 42% previous heart attacks, 18% cardiomyopathy,
% rheumatic fever, 60% hypertension, 31% arrhythmias, 440% diabetes.
heart disease, 5.2%
r
by 5.9%.
Alcohol abuse was reported
e
edical
Center clinical sample (MMC sample, n=100) was ppredominantly
The Montefiore Medical
male (62%), with a mean age of 58 years. The sample was diverse including a majority of
African-American patients and a large proportion of Hispanics. One third of the population
had a comparatively low household income. The severity of their heart failure symptoms
assessed by the New York Heart Association (NYHA) classification was 11% in class I, 53%
in class II, 36% in class III or IV.
HF-CAT Development
Item Banks Development: In the final calibrated item banks there were 21 items assessing
Physical Disability, 20 items assessing Fatigue and 29 items in the Dyspnea bank with
satisfactory item fit (Table 2). Most informative (i.e. with a high discrimination parameter:
‘slope’) was the item asking about the ability to run errands, an item referring to a feeling of
being “worn out”, and the item asking if the patient will be short of breath walking from one
room to another.
Simulation Studies: The precision of every score estimate can be displayed as a function of
the level of function, or the severity of the symptoms. The results of the simulation studies
showed that a highly precise score (comparable to an internal consistency of Į>.90) can be
estimated with 5 items for each domain over a range of nearly three SDs. (Figure 2, left side).
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The concordance between the results of the CATs and the entire item bank was very good for
atio
at
ions
io
ns (r=0.95-0.97),
(r=
r=0.
0 95
0.
95-0
-0
0
all of the constructs as illustrated by the extremely high correlations
showing
T can essentially capture the information provided by the en
that the 5 item CAT
entire bank. As
r high correlations between the simulated CAT scale sc
re
c
expected there were
scores
and the
6 Health Survey’s Physical Function (r=-.87), and Vitality sscales (r=-.84),
corresponding SF-36
(r .83). Compared to all legacy tools,
as well as the static Shortness of Breath measurement (r=.83).
the HF-CAT provides a more precise measurement over a larger measurement range (Figure
2, right side). For Physical Disability a similar measurement precision like with SF-36
Physical Function scale can be achieved with ½ the number of items (Figure 2, upper left
corner).
HF-CAT Evaluation
Respondent burden: On average 4-5 items were administered for the assessment of physical
disability, fatigue and dyspnea to achieve the predefined level of precision (Table 3). The
average time for administration of the entire HF-CAT with all three domains was 3 minutes
(3r2 min).
Validity: We used the MLHFQ to help evaluate the constructs of the HF-CAT and the NYHA
class to evaluate its discriminative validity (Table 3). The mean MLHFQ score of the sample
was 38 ± 25, the mean score of the HF-CAT were 59.6 ± 8.4 for Physical Disability,
52.6 ± 8.5 for Fatigue, and 54.8 ± 13.3 for Dyspnea. There were no order effects for any
measure. The HF-CAT scales for physical disability, fatigue, dyspnea correlated significantly
with the MLHFQ total score (r = 0.71, r = 0.63, r = 0.68 respectively).
A general linear model was used to evaluate the ability of the HF-CAT scales to statistically
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differentiate patients with different levels of symptom severity as measured by the clinician’s
eaasurres were
werre significant,
sigg
si
NYHA classification (Table 3). The main effects for all the measures
with
y ((Eta², F-values)) for the HF-CAT
CAT
T Ph
Phys
yys
ysic
ical
ic
a D
al
very similar discriminative ability
Physical
Disability and
d the MLHFQ scale.
Dyspnea scales, and
study took place
ace in a low income, less educated, minor
minority population
User Experience: Ass this stu
we had been particularly
cularly interested in the subjective user experience with a computer
assessment. 98% of the patients found the HF-CAT assessment overall very easy or easy,
100% thought it was very easy or easy to follow the instructions, and 95% said it was very
easy or easy to read the questions on the screen. 98% judged the time for the assessment as
‘just right’, and 90% considered the questions as relevant. 98% had been willing to use the
device again on the next visit.
Discussion
For the first time we applied computerized adaptive testing methods to develop and evaluate
an ultra-short assessment system for patients with HF (HF-CAT) in clinical practice. The tool
allows routine, comprehensive assessment of three primary problems that are commonly
experienced by patients with heart failure. If the emotional or social impact of the disease is of
additional interest, further tools, e.g. from the PROMIS, need to be added for a
comprehensive coverage of the health-related quality of life construct.
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Feasibility
uate
teed in a low
low income, low
The feasibility of the HF-CAT in its PDA version was evaluated
p
in the Bronx, NY. It was demonstrated that the HF-CAT is a
educated minority population
o
practical tool well accepted. Nevertheless, it was tested under study co
conditions,
and
h
been biased receiving an incentive for their particip
p
participants might have
participation.
To our
b t th
t
ithi clinical
li i l practice
t settings is
knowledge, only one reportt about
the acceptance
off CAT
CATs within
available. A similar CAT, also being displayed on a PDA, is in routine clinical use since
2004. Patients answering this CAT also report a high acceptability. All most all of the 423
consecutive patients considered the handling as easy and felt that the use of the PDA made
sense (27).
Several other studies report about the reception of CATs under study conditions. The majority
of patients in a feasibility test of a pain CAT found the CAT application to be useful, relevant,
of appropriate length, and easy to complete (28). Similarly the majority of respondents in a
feasibility study of an asthma impact CAT found it easy to complete and of appropriate length
(29). The results of a feasibility test of a diabetes CAT gave somewhat mixed results. While
both English-speaking and Spanish-speaking participants agreed that a paper-and-pencil
assessment was more burdensome than a CAT, the Spanish-speaking participants preferred
the paper tool and were more willing to complete a paper tool in the future (30).
Respondent Burden
One important contribution of the Computer Adaptive Test technology will be to reduce the
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respondent burden without compromising the precision and validity of the assessment, by
g was demonstrated earlier,
tailoring each assessment to the patient’s condition. This advantage
iving
ngg C
AT,, wh
AT
whi
i found that
for example, in a simulation study of the Activities of Daily Living
CAT,
which
m
the CAT provided similar results to a static version while reducing the num
number
of items
% (31). Results from other studies
t
sim
m
administered by 50%
indicate that scores similar
to those
e
ength
a be achieved
obtained with full-length
item banks (ranging in length from 18 to 585 items) ca
can
through much shorter CATs when measuring functional status (32-34), mental health status
(21,27,35,36) or the impact of conditions like headache (23,37), diabetes (30), chronic
pain (28), and asthma (29). Most actual CAT applications used between 5-7 items to measure
one construct. The present HF-CAT applied between 4-5 items per scale and the average total
time for the entire assessment and scoring was 3 min, i.e. 1 min per scale (which could be
applied individually). The assessment time of the MLHFQ electronically measured in a
previous study was 4r2 min (38), and time administer the Kansas City Cardiomyopathy
Questionnaire (KCCQ), another common tool for the assessment of HF patients, is reported to
be 4-6 minutes without scoring (39).
In summary, the HF-CAT provides a precise measure over a large measurement range with
minimal respondent burden. As far it is known today, it seems that CATs offer an effective
resolution to the classic conflict between practicality and precision faced by traditional
measurement technology (12).
Validity
Studies of CAT applications in diseases, like depression (27,35), or headache (40), have
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shown that their measurement advantages can transfer to increased validity in identifying
differences between groups known to differ in clinical characteristics, compared to static
etw
twee
eenn gr
ee
grou
oups
ou
ps oof patients of
tools. The three scales of the HF-CAT also discriminated between
groups
assification equally as well as a legacy tool measuring the im
different NYHA classification
impact of heart
t
HF-failure, using four times
more items. These initial results show thatt the HF-CAT
has the
l
potential to provide a valid, highly relevant assessment of patients with heart fail
failure.
t
Serial Measurements
For the assessment of HF patients, we believe it is important to assess the health status of the
patient at the point of care as well as at the patient’s home. As many elderly patients do not
have access to the internet or are not familiar with its use, one way to do so is the use of a
smart phone and or interactive voice recognition. Most established tools include items which
are not suitable to be used over the phone. IRT methods allow using much simpler items over
the phone and more comprehensive items at the doctor’s office, and scoring both assessments
on the same measurement metric . This allows having a smart phone administer the HF-CAT
at the patient’s home, and have the same patient answering the more comprehensive
PROMIS-CAT on a tablet PC at the doctor’s office. IRT-based measurements of health
outcomes are independent of the particular items being administered and from the test
administrator. The same value for the same domain yields the same interpretation, whereas
results from different traditional tools cannot be compared directly making serial health status
monitoring less practicable.
Limitations
Despite many encouraging findings with recent CAT developments, a number of issues still
need to be addressed. Within this study we have only used outpatients to evaluate the HFDownloaded from http://circheartfailure.ahajournals.org/ by guest on June 17, 2017
CAT, which limits the generalizability to less severely disabled patients. However, one of the
most relevant advantages of CATs is that they can essentially eliminate floor and ceiling
on st
stud
udie
ud
iess ha
ie
have
ve sshown that the
effects by applying items tailored to the test-taker. Our simulation
studies
current item bank covers more than three standard deviations aabove the pop
population mean,
s
spitalized
which is where a hospitalized
population of HF patients usually scores.
v not used the
We did not evaluate the test-retest reliability for the HF-CAT. Similarly, we hav
have
ti study
t d tto ttestt it
i
tto ttreatments.
t
t H
HF-CAT in an intervention
its responsiveness
However, several
studies have reported on the ability of other CATs to detect change. For example, in a
telephone study of 540 headache patients, a CAT for headache impact was demonstrated to be
more responsive to self-evaluated changes of headache impact than a corresponding 54-item
bank (23). In a longitudinal, prospective cohort study of 94 patients discharged from inpatient
rehabilitation, the CAT version of the Activity Measure for Post-Acute Care was found to be
comparable in responsiveness to the 66-item static version (41). Similarly, in a series of
articles, Hart et al. report on the results of validation studies of condition-specific CATs,
using large data sets from patients receiving rehabilitation services across multiple U.S.
clinics (33,34).
Summary
In summary, we have developed a promising method to measure patient-reported dyspnea,
fatigue and physical function for use in the care of patients with heart failure. This new
measure is part of a rapidly growing number of new assessment tools utilizing the advantages
of item response theory and computerized adaptive test techniques (16,19,42), with some of
them being used in clinical practice already (27,43). However, whether these encouraging
improvements in measurement will transfer to improved care and ultimately health of heart
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failure patients warrants further studies.
Sources of Funding
s
in part by an NIH/NLHBI grant (1 R43 HL083622
2
The work has been supported
HL083622-01,
PI Rose)
Disclosures
None.
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Table 1. Characteristics of the Samples
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HF-CAT
Development
(IB sample)
HF-CAT
Evaluation
(MMC sample)
Total Sample
n = 658
N = 100
Age
Years with HF
Family Status
Living in partnership
Living alone
60 (13)
58 (12)
8.8 (7.9)
4.6 (4.5)
78%
21%
54%
33%
Gender
Female
49%
38%
Ethnicity
y
Hispanic or Latino
4%
35%
Race
White
African American
Other
93%
3%
4%
19%
46%
35%
Education
8th Grade or Less
Some High
g School
High
g School Graduate
Some College
g
College
g Graduate
0.1%
3%
15%
39%
22%
13%
21%
25%
24%
11%
Postgraduate
20%
5%
Household income
Less than $5,000
1%
11%
$5,001 to $20,000
18%
22%
$20,001 to $45,000
32%
15%
$45,001 to $75,000
23%
10%
More than $75,000
17%
5%
Prefer not to answer
9%
37%
Employment
p y
status
Student
.3%
4%
Working at a paying job
22%
23%
Retired
56%
47%
Laid off or unemployed
3%
2%
A full-time homemaker
7%
9%
Other
11%
11%
Table 2. IRT Item Parameters HF-CAT Item Banks
Physical Disability
slope
mean
1
2
Exercising hard for half an hour 1
2.549
0.556
-0.123
1.236
Doing an hour of physical labor 1
2.810
0.625
0.072
1.177
1.650
Walking up a steep hill
1
thresholds
3.558
0.748
-0.154
Rearranging furniture at home 1
3.952
1.136
0.610
1.663
Doing chores 1
4.252
1.391
0.765
2.017
3.432
1.492
0.537
1.191
Doing daily physical activities 2
1
Climbing up a flight of stairs
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3.728
1.535
0.824
2.246
Doing daily physical activities 1
4.092
1.583
0.881
2.285
Carrying two bags of groceries 1
3.800
1.595
1.132
2.058
*
Walking on flat ground
1
5.247
1.621
1.621
Preparing a meal 1
5.554
1.643
1.643
*
Walking one hundred yards 1
3.977
1.648
1 158
1.158
2.137
2.1
.1137
3
Standing up from a chair 1
3.837
1.814
1.814
1.8
14
*
Running errands and shopping 1
5.713
1.826
1.236
1.2
236
2.4
2.417
417
Dressing myself 1
4.825
1 832
1.832
1 832
1.832
*
Taking a tub bath
1
2.683
1.869
1.601
2.137
7
Getting from one room to another 1
5.494
1.907
1.907
*
Standing up from a bed 1
3.922
1.910
1.910
*
Getting on and off the toilet
1
3.890
1.953
1.953
*
Making the bed 1
4.330
1.955
1.444
2.465
5
Putting a trash bag outside 1
4.768
1.995
1.536
2.453
3
2.419
-0.475
-1.407
0.456
Fatigue
Full of energy 3
3
2.243
-0.421
-1.271
0.429
Fresh and rested 3
1.979
-0.175
-1.195
0.845
Lively 3
1.925
-0.131
-1.100
0.839
Strong and vital
Active
3
1.856
-0.123
-1.203
0.957
Full of life 3
1.600
0.063
-0.756
0.881
Tired 3
2.591
0.406
-0.638
1.450
3
3.617
0.546
-0.345
1.436
Sluggish 3
2.899
0.578
-0.383
1.539
4.090
0.647
-0.214
1.508
1.551
Fatigued
Worn out 3
Run down
3
3.445
0.679
-0.192
Wide awake 3
1.217
0.741
-0.407
1.889
As if I have no energy left 3
3.189
0.767
-0.072
1.606
Spent 3
3.325
0.807
-0.104
1.719
Exhausted
3
Weary 3
Weak 3
Save my energy
3
3.392
0.811
-0.016
1.637
2.614
0.852
-0.042
1.747
2.421
0.866
-0.094
1.825
1.161
1.065
-0.064
2.195
Sleepy all day 3
1.765
1.125
0.139
2.111
Jaded 3
1.241
1.809
0.817
2.801
3
4
1.715
2.523
Dyspnea
slope
mean
1
2
3
Running a short distance makes me short of breath 3
1.190
-0.525
-2.072
-0.090
0.587
1.134
0.131
-0.206
0.468
Exercising hard for half an hour makes me short of breath
Talking while walking up a hill will make me short of breath
3
thresholds
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2.040
0.185
-1.455
0.385
An hour of physical labor makes me short of breath 4
1.418
0.394
0.033
0.755
My breathing problems limit my ability to exercise as much
as I would like 3
1.500
0.449
-0.529
0.685
1.193
Talking while walking up a flight of stairs makes
me short of breath 3
2.068
0.564
-0.919
0.807
1.803
During a typical day I feel short of breath 4
2.407
0.649
-0.220
1.518
Doing chores, like vacuuming or yard work, makes
me short of breath 3
2.033
0.693
-0.608
0.763
1.924
Climbing up one flight of stairs makes me short of breath 3
2.440
0.819
-0.646
0.983
2.120
Going outside for a walk makes me short of breath 3
2.646
1.037
-0.213
1.197
2.128
Walking one hundred yards makes me short of breath 3
2.351
1.052
-0.064
1.194
2.027
Walking up a hill makes me short of breath
4
1.625
2.122
1.085
0.381
1.789
Carrying groceries makes me short of breath 3
2.796
1.102
-0.106
-0.
10
106
1.222
1.2
22
2
Talking while walking makes me short of breath 3
2.422
1.241
-0.023
-0.
0 023
1.313
1 313
1.3
3
2.434
Running errands makes me short of breath
reath 3
2.677
1.351
0.191
1.415
2.448
Taking a bath makes me short of breath
a 4
ath
2.849
1.404
1.009
1.800
0
4
Dressing myself makes me short of breath
r
reath
3.118
1.431
0.887
1.9755
3.104
1.451
0.988
1.914
4
4
Preparing a meal makes me short of breath
b
Singing or humming makes me short of breath
4
Speaking in a group makes me short of
o breath 4
I feel short of breath when I sit and rest
st 4
Talking at noisy places makes me short of breath
4
2.086
1.456
0.943
1.970
0
1.900
1.481
0.994
1.969
9
2.775
1.543
1.543
*
2.187
1.606
1.170
2.043
Walking from one room to another makes me short of breath 4
3.909
1.647
1.154
2.139
Talking to someone makes me short of breath 4
2.875
1.779
1.247
2.311
Talking on the phone makes me short of breath
4
2.768
1.840
1.398
2.281
Getting off the bed makes me short of breath 4
2.958
1.849
1.305
2.393
Going to the toilet makes me short of breath 4
2.868
1.900
1.501
2.298
1.532
1.924
1.234
2.000
2.511
1.924
1.302
2.547
Lying down flat makes me short of breath
3
Standing up from a chair makes me short of breath 4
2.190
2.537
The table is ordered by the mean threshold value. Response options: 1: easy / hard /
impossible, 2: no difficulty / a little bit of difficulty / some difficulty / a lot of difficulty /
can’t do because of my health; 3: not at all / somewhat / very much, 4: not at all / a little bit
/ quite a lot / can’t do; 5: not at all / a little bit / quite a lot; * two highest response option
had been collapsed for the item parameter estimation the presentation of responses options
for the patient remains the same
IRT item bank parameters are developed as usual on a 0±1 metric, with 0 representing the
scaling sample mean with a standard deviation of 1.
For easier interpretability estimated patient scores are transformed linear to a 50+10 metric
later.
4
The slope parameter is also called discrimination parameter. Higher slope parameters
indicate a better discrimination, which makes the item more valuable, i.e. ‘informative’, for
the score estimation: the capability e.g. to ‘run errands’ is more informative to determine
the physical disability of a patient than e.g. her or his ability to ‘put the trash outside the
house’.
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The thresholds of an item show at which score level a particular response option is the
most likely to be endorsed. For the item ‘running errands’ the threshold 1.236 separates the
response ‘easy’ from ‘hard’, and the threshold 2.417 ‘hard’ from ‘impossible’. If a patient
scores 3 standard deviations above the population mean s/he is most likely to answer the
item ‘running errands is …’ with ‘impossible’, as her/his score is above the threshold of
2.417. If her/his level of disability is only 1.5 SD above the U.S. population mean s/he is
likely to endorse ‘hard’, as the score is between the thresholds 1.236 and 2.417. The mean
threshold illustrates the position of the item on the metric, which can be seen as ‘item
n threshold.
difficulty’ in traditional terms. The table is sorted by the mean
Table 3. Score differences between different NYHA classes
NYHA class
N°
I
II
III / IV
n=11
n=53
N=36
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Items
Mean
SD
Mean
SD
Mean
SD
Eta²
F
p
RV
(95%CI)
Physical
Disability
4.9±1.5
53.0
6.2
58.9
8.6
62.6
7.4
.12
6.2
.003
1.01
(.38-2.20)
Fatigue
3.7±0.7
46.8
6.9
52.0
7.6
55.4
9.4
.09
4.9
.009
.80
(.21-1.91
Dyspnea
4.6±1.5
43.9
14.4
53.8
12.7
59.8
11.7
.13
6.9
.002
1.13
(.34-2.67)
MLHFQ
21
15.5
14.8
38.3
25.3
44.9
22.9
.11
6.1
.003
1.00
Theta values of the CAT scales are scored on a T-distribution.
ion
o . Th
The
he ML
MLHFQ
LHF
H
scores are
summary scores ranging from 0-105. All analyses have been
en ccontrolled
ontr
on
trol
tr
olle
ol
ledd fo
le
for the order of
administration as a confounding variable.
alidity: HF-CAT scale F-values divided by the F-value for the MLHFQ
RV: Relative Validity:
tstrap analysis was used to determine the confidence interval
l
sum scale. A bootstrap
intervals
Figure Legends
Figure 1. HF CAT patient interface and examples for one item of each bank
Figure 2. Measurement precision in relation to measurement range
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The x-axis shows the patient score. In IRT terminology this score is referred to as the ‘theta
score’. To make the HF-CAT and the legacy tools comparable both instruments are scored on the
same metric as determined by the developed item banks.
The y-axis shows the 95% confidence interval of the patient score, the smaller the y-value the
higher the precision of the score. The dotted lines show confidence intervals which would be
comparable to an internal constancy of Cronbach Į 0.80, 0.90, and 0.95 for illustrative purposes.
With the following
questions we would
like to assess your
current health status
…
For me,
running errands
is …
easy
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hard
impossible
I feel short of
breath when I sit
and rest …
I feel tired …
not at all
not at all
somewhat
a little bit
very much
quite a lot
0,6
12
D=.80*
0,5
10
HF-CAT
5 items
0,4
8
95% CI
12
0,6
Physical
Disability
10
0,5
SF-36 PF
10 items
HF-CAT
10 items
0,48
SF-36 PF
10 items
D=.90*
0,36
6
0,3
Physical
Disability
D=.95*
Item Bank
20 items
2
0,1
0,12
-3
-2
30
-1
40
0,6
12
HF-CAT
5 items
0,5
10
95% CI
Item Bank
20 items
0
0
0
50
1
60
2
70
3
80
-1
40
HF-CAT
C
CAT
4 items
tem
ems
ems
0,5
10
0,4
8
0,3
6
0,3
6
4
0,2
Item Bank
29 items
2
0,1
-2
30
0
50
1
60
2
70
0,6
12
Dyspnea
HOS
4 items
-3
4
0,4
8
4
0,2
3
80
4
Dyspnea
H
HOS
4 item
iitems
temss
tem
Item Bank
29 items
2
0,1
0
0
-3
-2
30
-1
40
0
50
1
60
2
70
0,6
12
3
80
4
HF-CAT
5 items
-3
-2
30
-1
40
0
50
1
60
2
70
0,6
12
Fatigue
10
0,5
95% CI
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0,24
4
0,2
SF-36 VT
4 items
HF-CAT
4 items
0,5
10
SF-36 VT
4 items
3
80
4
Fatigue
0,4
8
8
0,4
6
0,3
6
0,3
4
0,2
0,2
4
Item Bank
20 items
2
0,1
Item Bank
20 items
0,1
2
0
0
-3
30
-2
40
-1
50
0
60
1
patient score
70
2
80
3
4
-3
-2
30
-1
40
0
50
1
60
patient score
2
70
3
80
4
Short and Precise Patient Self-Assessment of Heart Failure Symptoms Using a Computerized
Adaptive Test (HF-CAT)
Matthias Rose, Milena Anatchkova, Jason Fletcher, Arthur E. Blank, Jakob Bjørner, Bernd Löwe,
Thomas S. Rector and John E. Ware
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Circ Heart Fail. published online April 23, 2012;
Circulation: Heart Failure is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2012 American Heart Association, Inc. All rights reserved.
Print ISSN: 1941-3289. Online ISSN: 1941-3297
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://circheartfailure.ahajournals.org/content/early/2012/04/23/CIRCHEARTFAILURE.111.964916
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