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Development and validation of a patient-based disease activity score in rheumatoid arthritis that can be used in clinical trials and routine practice.

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Arthritis & Rheumatism (Arthritis Care & Research)
Vol. 59, No. 2, February 15, 2008, pp 192–199
DOI 10.1002/art.23342
© 2008, American College of Rheumatology
Development and Validation of a Patient-Based
Disease Activity Score in Rheumatoid Arthritis
That Can Be Used in Clinical Trials and
Routine Practice
Objective. Assessor-based disease activity measures such as the Disease Activity Score in 28 joints (DAS28), although
widely used in rheumatoid arthritis (RA), have high interobserver variability. We developed and validated a patientbased disease activity score (PDAS) as an alternative assessment.
Methods. Patients’ assessments of swollen or tender joints, visual analog scales for pain and general health, the Health
Assessment Questionnaire, and erythrocyte sedimentation rate (ESR) were used to develop the PDAS. In a developmental
cohort (204 patients), regression analyses determined the best fit with the DAS28. A validation cohort (322 patients)
subsequently evaluated criterion and construct validity against a range of outcome measures, including the Nottingham
Health Profile (NHP) and Short Form 36 (SF-36). Sensitivity to change was assessed in 56 patients after 6 months of
treatment with disease-modifying antirheumatic drugs or biologics.
Results. In the developmental cohort, the PDAS with ESR (PDAS1) and without ESR (PDAS2) achieved excellent fit with
the DAS28 (r ⴝ 0.88 and 0.74, respectively). In the validation cohort, the PDAS showed high criterion validity by
correlation with the DAS28 (PDAS1: r ⴝ 0.89, PDAS2: r ⴝ 0.76). Construct validity was demonstrated by high correlations
with a range of disease activity measures (r > 0.45), whereas low correlations (r < 0.45) with mental and social
components of the SF-36 and NHP indicated divergent validity. The PDAS and DAS28 had similar sensitivity to change,
determined using effect sizes (DAS28 ⴝ 1.03, PDAS1 ⴝ 1.02, PDAS2 ⴝ 0.77) or standardized response means (DAS28 ⴝ
0.79, PDAS1 ⴝ 0.77, PDAS2 ⴝ 0.73).
Conclusion. The PDAS1 and PDAS2 are valid and sensitive tools to assess disease activity in RA. They appear suitable
for clinical decision making, epidemiologic research, and clinical trials.
Joint counts undertaken by physicians, nurses, or therapists are one cornerstone in the conventional assessment
of disease activity in rheumatoid arthritis (RA). Substan-
Supported by the Arthritis Research Campaign (UK) and
the Joint Research Committee of King’s College Healthcare
National Health Service Trust.
Ernest H. Choy, MD, FRCP, Bernadette Khoshaba, PhD,
David L. Scott, MD, FRCP: King’s College London, London,
UK; 2Derek Cooper, PhD: King’s College Hospital, London,
UK; 3Alex MacGregor, MD, FRCP: University of East Anglia,
Norfolk, and Norwich Hospital, Norwich, UK.
Address correspondence to Ernest H. Choy, MD, FRCP, Sir
Alfred Baring Garrod Clinical Trials Unit, Academic Department of Rheumatology, King’s College London, Weston
Education Centre, Cutcombe Road, London SE5 9RJ, UK.
Submitted for publication March 8, 2007; accepted in
revised form August 17, 2007.
tial interobserver variation presents one substantial practical disadvantage when using such joint counts and this
variability persists despite training (1–5). One limitation of
this variability is the need to ensure the same assessor
carries out disease activity assessments in each patient.
This is generally stipulated in the protocols of clinical
trials and attempted as far as possible in routine clinical
practice. However, prolonged followup in trials and everyday care makes it impractical for long-term observations to
be made by a single individual clinician. A second limitation is that joint counts by clinicians appear to be relatively insensitive for separating the effects of active therapy from placebo treatment compared with subjective
patient-based measures such as the Health Assessment
Questionnaire (HAQ) (6,7). The probable explanation for
this latter finding is that clinicians overestimate placebo
effects compared with patients.
One approach to limiting the impact of these problems is
to replace joint counts by clinicians with patient self-
Patient Disease Activity Score in RA
Table 1. Details of patients in the different assessment groups*
Age, mean (range) years
Disease duration, mean (range)
Rheumatoid factor positive
African Caribbean
DMARD therapy
Oral steroids
IM depomedrone
Study 1:
model data
(n ⴝ 204)
Study 2:
(n ⴝ 322)
Study 3:
changes with
(n ⴝ 56)
160 (78)
44 (22)
60.5 (21–90)
9.9 (0–54)
169 (83)
246 (76)
76 (24)
60.29 (23–87)
9.13 (0–48)
261 (81)
49 (88)
7 (12)
55.4 (20–79)
8.31 (0–40)
35 (63)
167 (82)
26 (13)
12 (5)
150 (74)
6 (3)
48 (24)
6 (3)
96 (47)
282 (88)
31 (10)
9 (3)
234 (73)
10 (3)
41 (13)
20 (6)
222 (69)
48 (86)
6 (11)
2 (3)
40 (71)
0 (0)
43 (77)
0 (0)
95 (80)
* Values are the number (percentage) unless otherwise indicated. DMARD ⫽ disease-modifying antirheumatic drug; IM ⫽ intramuscular; NSAIDs ⫽ nonsteroidal antiinflammatory drugs.
assessed joint counts. However, studies of self-assessed
joint counts show that despite providing useful information, these joint counts cannot directly substitute joint
counts by clinicians (8 –18). More information is gained by
combining self-assessed joint counts with health status
assessments in instruments such as the RA Disease Activity Index (RADAI) (19 –21). Although such measures provide much useful data, they are not directly comparable
with existing integrated assessments of disease activity
such as the Disease Activity Score in 28 joints (DAS28)
(22,23) and, as a consequence, their value is restricted in
situations in which a score comparable with the DAS28 is
needed. One example is identifying patients who may
benefit from therapy with biologic treatments, for which
some regulatory bodies require specific DAS28 scores.
Previous work from our unit has demonstrated that selfassessed joint counts can be used to generate patient-based
disease activity scores (24). However, this earlier approach
was simplistic and did not involve a formal evaluation of
the optimal combination of measures to reproduce the
DAS28. We have therefore extended this approach by developing and validating a patient-based disease activity
score (PDAS), which is comparable with the clinicianbased DAS28 using measures within the internationally
agreed core data set for RA. Our goal was to design a valid,
reliable, sensitive, and feasible alternative to conventional
assessment by clinicians for determining individual clinical disease activity and responses to therapy with antirheumatic drugs.
Patients. We studied current outpatients attending specialist rheumatology clinics in southeast London who met
the 1987 American College of Rheumatology (ACR; for-
merly the American Rheumatism Association) criteria for
RA (25). Three cohorts of patients were studied (Table 1).
Developmental cohort. The developmental cohort comprised 204 consecutive patients with RA who completed
the patient self-assessments. The initial 20 patients in this
cohort were also involved in testing face validity. These 20
patients found that rating tender joints and swollen joints
verbatim was confusing and preferred performing these
assessments using a mannequin without grading. This is a
key difference between the RADAI and PDAS. In addition,
the test–retest reliability of the questionnaire was evaluated in 46 of the 204 patients who were asked to complete
the questionnaire 24 hours after their initial assessment.
Validation cohort. A different group of 322 consecutive
patients with RA then completed the patient self-assessments and also had standard measures of disease assessed.
Responsiveness cohort. The responsiveness cohort
comprised 56 patients who had started disease-modifying
antirheumatic drugs (DMARDs) or biologic agents and
were seen 6 months apart to assess responsiveness to
change. Six patients were going to start biologic agents
(infliximab or etanercept), 33 were going to start methotrexate, and 17 were going to start other DMARDs including combination therapies.
Ethical review. The South Thames Multicentre Research Ethics Committee approved the study. All patients
who were enrolled gave written informed consent.
Patient self-assessments. An initial systematic review
of the literature identified the most relevant patient-based
disease activity assessments. These included pain score
(0 –100-mm visual analog scale [VAS]), patient global assessment of disease activity (PGA; 0 –100-mm VAS), fatigue score (0 –100-mm VAS), early morning stiffness score
(0 –5 scale), patient self-assessed tender joint counts and
swollen joint counts for up to 50 joints, HAQ, Short Form
36 (SF-36), Nottingham Health Profile (NHP), and EuroQol. Erythrocyte sedimentation rate (ESR) was measured
on the same day as these assessments. Patient self-assessed
joint counts were recorded on a self-administered questionnaire completed without specific verbal assistance; patients were asked to indicate all the joints that were painful at present using one mannequin that displayed
individual joints (for tender joints) and all the joints that
were swollen at present using a second mannequin of an
identical design (for swollen joints).
Observer-based assessments. Conventional disease outcome assessments were also performed, including tender
and swollen joint counts (for 28 joints), and were used to
calculate the conventional DAS28 (for 28 joints).
Statistical analysis. Dispersion and distribution of the
data in the self-assessment questionnaire were examined
and when necessary transformed into a Gaussian distribution. Many self-assessed outcome measures showed
skewed distributions; one notable exception was the HAQ,
which had a Gaussian distribution. These self-assessed
measures were logarithmically transformed prior to multiple regression analysis.
Modeling of the PDAS was established by performing
forward stepwise regression analyses. Patient-derived
variables, coupled with HAQ scores and ESR results, were
entered into SPSS software, version 10 (SPSS, Chicago, IL)
to generate the best-fit models with the DAS28. Two models were developed: PDAS1, which included the ESR, and
PDAS2, which did not include the ESR. The internal consistency and test–retest reliability of the PDAS was tested
through Cronbach’s alpha and intraclass correlation coefficients.
Validation of the PDAS. Criterion validity for the
PDAS1 and PDAS2 developed in the first cohort of patients was confirmed by correlation with the DAS28 and
Clinical Disease Activity Index (CDAI) (26) in the second
validation cohort of patients. Construct validity was assessed by correlation with individual components of the
internationally agreed core data set for RA, SF-36, NHP,
and EuroQol-5D based on assumptions that patients with
active RA have more symptoms, more disability, and reduced physical function with relatively little direct impact
on mental health. In assessing construct validity, given
that the PDAS is a measure of disease activity, correlation
with other measures of disease activity should be higher
(convergent validity) than other measures such as quality
of life (divergent validity).
Responsiveness and sensitivity to change of the PDAS.
Patients who took part in the responsiveness/sensitivity to
change study were consecutive patients who were seen
twice and had started a DMARD or biologic agent. These
patients were asked to return to the clinic after a period of
6 months to complete the same set of questionnaires and
an assessment as detailed in study 2. The responsiveness/
sensitivity to change of the PDAS1 and PDAS2 were as-
Choy et al
sessed by calculating effect sizes and standardized response means. Effect size was measured by the difference
between the mean baseline scores and followup scores on
the measure, divided by the standard deviation of baseline
scores. Standardized response mean was calculated by
dividing the mean observed change by the standard deviation of the change.
Patients were also asked to assess their responses to
biologic agents and DMARDs at 6 months in terms of
whether or not there was a response. Changes in DAS28,
PDAS1, and PDAS2 were evaluated from this perspective.
Development of the PDAS. Face validity of the patient
self-assessments, incorporated into a questionnaire, was
evaluated by showing the questionnaire to 20 patients.
Patients preferred self-assessing joint tenderness and
swelling using a mannequin rather than using verbatim
assessment. Many patients found grading of joint tenderness and swelling to be too complicated and time consuming, and therefore this was omitted. The questionnaire was
consequently revised for use in the subsequent definitive
studies; this revised format was usually completed in 7
The test–retest reliability of each item in the finalized
questionnaire was assessed in 46 patients. This was graded
as excellent with intraclass correlation coefficients ranging
from 0.76 to 0.88.
The PDAS was then devised by stepwise multiple regression analysis in the full cohort of 204 patients after
appropriate transformations of the various clinical variables. This analysis showed that 4 measures in the patient
self-assessment questionnaire explained 79% of the variance in DAS28 scores (r ⫽ 0.89). PGA explained 44% of
the variance in DAS28, logarithmically transformed ESR
explained a further 28%, logarithmically transformed
numbers of patient-assessed tender joints (50 joints) explained a further 5%, and the HAQ explained a final 1%.
Because the HAQ added relatively little to the variation in
PDAS1, it could have been omitted, but a decision was
made to retain it to ensure maximal comparison with the
DAS28. The regression equation for the PDAS1 (including
ESR) was as follows:
PDAS1 ⫽ 0.019 ⫻ (PGA) ⫹ 0.842 ⫻ ln(ESR ⫹ 2)
⫹ 0.432 ⫻ ln(patient 50 TJC ⫹ 2) ⫹ 0.271 ⫻ (HAQ)
where 50 TJC ⫽ tender joint count of 50 joints. Because
ESR results may not be readily available in all clinical
situations, a second model, the PDAS2 (without ESR), was
also developed using a similar regression analysis. In this
model 4 measures explained 55% of the variation in
DAS28 (r ⫽ 0.74). PGA accounted for 44% of the variance
and addition of the HAQ, patient self-assessed swollen
joint count (for 28 joints), and early morning stiffness
(EMS) score added a further 5%, 4%, and 1%, respectively. Because EMS added little to the total variation in
PDAS2, it could have been omitted, but a decision was
made to retain it to ensure maximal comparison with the
Patient Disease Activity Score in RA
comparison, with the PDAS1 only 28 (9%) had scores ⬍3.1
and 9 (3%) had scores ⬍2.6, and with the PDAS2 there
were 25 (8%) and zero, respectively, with scores in these
lower ranges. The PDAS1 and PDAS2 both correlated
highly with the DAS28, with Spearman’s rank correlation
coefficients of 0.89 and 0.76, respectively. The correlations
of the PDAS1 and PDAS2 with the DAS28 are shown in
Figure 2. The PDAS1 and PDAS2 also correlated highly
with the CDAI, with correlation coefficients of 0.69 (P ⬍
0.0001) and 0.73 (P ⬍ 0.0001), respectively.
Construct validity. The PDAS1 and PDAS2 showed convergent and divergent validity. Both showed relatively
high correlations with other measures of disease activity
and quality of life measures that capture arthritis symptoms such as pain and disability. These include assessor
28 tender joint counts and 28 swollen joint counts, VAS
fatigue scores, VAS assessor global scores, VAS pain
scores, C-reactive protein level, SF-36 physical component
scores, NHP physical domain scores, NHP pain scores, and
EuroQol scores. With the PDAS1, these correlations varied
from 0.45 for assessor 28 swollen joint counts to 0.72 for
VAS pain scores, and with the PDAS2, the correlations
Figure 1. Distribution of the Disease Activity Score in 28 joints
(DAS28), patient-based disease activity score with erythrocyte
sedimentation rate (PDAS1), and patient-based disease activity
score without erythrocyte sedimentation rate (PDAS2) in the validation study.
DAS28. The regression equation for the PDAS2 (excluding
ESR) was as follows:
PDAS2 ⫽ 0.021 ⫻ (PGA) ⫹ 0.483 ⫻ (HAQ) ⫹ 0.033
⫻ (patient 28 SJC) ⫹ 0.002 ⫻ (EMS)
where SJC ⫽ swollen joint count. The internal consistency
of the 8 items included in the questionnaire was high; this
was shown using Cronbach’s alpha, which gave a value of
0.72. For the 4 items in the PDAS1 and PDAS2, Cronbach’s
alpha was 0.5 and 0.4, respectively.
The maximum number of joints in the patient-assessed
joint counts was 50 for both tender and swollen joints. We
explored the possibility of reducing this to the 28 joints
used in the DAS28 score to reduce the demand on patients,
but found that for the tender joint count, 50 joints performed better than 28 joints.
PDAS validation. Criterion validity. The PDAS1 (with
ESR) and PDAS2 (without ESR) had distributions similar
to the DAS28, with some minor variations (Figure 1). Both
were less sensitive for detecting low disease activity with
the appearance of a floor effect. This floor effect was more
marked with the PDAS2. Using the DAS28, 54 (17%) patients had scores ⬍3.1, and 29 (9%) had scores ⬍2.6. By
Figure 2. Relationship of the Disease Activity Score in 28 joints
(DAS28) with patient-based disease activity score with erythrocyte sedimentation rate (PDAS1), and patient-based disease activity score without erythrocyte sedimentation rate (PDAS2) in the
validation study.
Choy et al
Table 2. Convergent and divergent validity of the PDAS1
and PDAS2 compared with the DAS28: Spearman’s rank
correlation coefficients with disease activity and generic
health measures in 322 patients in the validation study
(study 2)*
Assessor 28TJ
Assessor 28SJ
VAS fatigue
VAS assessor global
VAS pain
C-reactive protein
PCS (SF-36)
NHP physical
NHP pain
MCS (SF-36)
NHP sleep
NHP social
NHP emotion
* There is convergent validity with other measures of disease activity and divergent validity with other measures, specifically quality
of life measures. PDAS1 ⫽ patient-based disease activity score with
erythrocyte sedimentation rate; PDAS2 ⫽ patient-based disease activity score without erythrocyte sedimentation rate; DAS28 ⫽ Disease Activity Score in 28 joints; CDAI ⫽ Clinical Disease Activity
Index; 28TJ ⫽ 28 tender joint count; 28SJ ⫽ 28 swollen joint count;
VAS ⫽ visual analog scale; SF-36 ⫽ Short Form 36; PCS ⫽ physical
component summary; NHP ⫽ Nottingham Health Profile; MCS ⫽
mental component summary.
ranged from 0.37 for C-reactive protein level to 0.83 for
VAS pain scores (Table 2). The main differences of both
the PDAS1 and the PDAS2 compared with the DAS28
were higher correlations with VAS pain scores and VAS
fatigue scores. In contrast, both the PDAS1 and PDAS2
showed lower correlations with measures of generic health
such as sleep and social function. These correlations were
⬍0.37 with the PDAS1 and ⬍0.44 with the PDAS2
(Table 2).
Responsiveness to change. The sensitivity to change of
the PDAS1 and PDAS2 was evaluated in 56 patients starting a new DMARD or biologic agent who were followed up
6 months later. Effect sizes and standardized response
means for the PDAS1 and PDAS2 (Table 3) showed that
the PDAS1 and DAS28 had similar effect sizes (1.02 and
1.03, respectively), with the PDAS2 showing a smaller
effect size (0.8). The standardized response means were
similar (0.70 – 0.79). The effect size of CDAI was 0.7. The
PDAS1 and PDAS2 showed correlations to the DAS28
similar to other assessments of change in these cases.
There were high Spearman’s correlations with changes in
VAS assessor global and VAS pain scores (ⱖ0.59), moderate correlations with changes in assessor tender joint count
(ⱖ0.51), and no correlations with changes in SF-36 physical component summary and EuroQol scores.
Patient self-assessment of response comprised 37 (66%)
responders and 19 (34%) nonresponders. At baseline the
mean DAS28 score was 6.26 for nonresponders and 5.90
for responders. At 6 months the sample mean was 5.78 for
nonresponders and 4.27 for responders. The PDAS1 including the ESR produced baseline sample means of 6.2
and 5.87 for nonresponders and responders, respectively.
At the 6-month assessment the sample means were 5.77 for
nonresponders and 4.56 for responders. A comparison of
6-month scores for responders and nonresponders is
shown in Figure 3.
The PDAS defines an individual patient’s disease activity
on the day of his or her assessment, making it a useful
measure to assess both symptom impact and changes in
activity. The PDAS1 and PDAS2 have good psychometric
Table 3. Effect size and standardized response mean of the PDAS1 and PDAS2 in 56
patients concerning changes with treatment study*
Mean ⫾ SD change
Effect size
Standardized response mean
Assessor 28TJ
Assessor 28SJ
VAS fatigue
VAS assessor global
VAS pain
C-reactive protein
PCS (SF-36)
NHP physical
NHP pain
1.00 ⫾ 1.30
0.73 ⫾ 1.10
1.20 ⫾ 1.50
9.7 ⫾ 13.9
* Effect size was calculated as the difference between mean baseline scores and followup scores divided
by the standard deviation of baseline scores. Standardized response mean was calculated as the mean
observed change divided by the standard deviation of the change. See Table 2 for definitions.
Patient Disease Activity Score in RA
Figure 3. Disease Activity Score in 28 joints (DAS28), patientbased disease activity score with erythrocyte sedimentation rate
(PDAS1), and patient-based disease activity score without erythrocyte sedimentation rate (PDAS2) at 6 months in the responsiveness study.
properties and both meet the requirements of the Outcome
Measures in Rheumatology Clinical Trials (OMERACT)
filters as they are true (valid), show discrimination (sensitivity to change), and are feasible. They are tools that could
be adopted in future clinical trials, epidemiologic research, and routine practice. Although both measures
could be further simplified, for example, by removing the
HAQ from the PDAS1 because it contributes only minimally to the overall variance, there is little benefit to such
omissions because the HAQ is included in the OMERACT
and European League Against Rheumatism (EULAR) core
data set. For the PDAS2, EMS score can be omitted without
significantly affecting the validity and sensitivity of the
instrument. Use of the HAQ to assess disease activity
could be criticized because HAQ scores are also influenced
by structural damage; however, there is good evidence
from secondary evaluations of clinical trial data that HAQ
scores are sensitive indicators of disease activity (6) and
we consider these scores suitable for use in this context.
It is interesting that the patient-derived joint counts
were not entirely equivalent to the clinician-derived
counts in the modeling of the PDAS; instead, the HAQ
seemed to be an indicator of more importance, particularly
in the PDAS2 model without the ESR. Our initial assumption was that patient-derived joint counts could simply
substitute for those made by clinicians (24), but this
proved incorrect. However, we have demonstrated that
using a combination of self-assessment items, it is possible
to measure disease activity in a manner that is as efficient
as the DAS28. As with all clinical measures there are likely
to be substantial differences in the judgments of individual
clinicians and patients about whether or not active disease
is present; this has been previously studied in detail by
Kirwan and colleagues using “paper patients” to evaluate
clinicians’ views (27).
The use of laboratory measures in assessing disease activity in RA is complex. We developed 2 PDAS models, one
with ESR and the other without ESR. On a superficial level,
leaving out the ESR may be relatively disadvantageous for
clinical trials and epidemiologic studies because its inclusion provides a more representative reflection of the conven-
tional DAS28. However, there is considerable evidence that
patient-derived measures provide a better assessment of clinical outcomes than laboratory measures (7,26,28,29). The
balance of evidence indicates that a pooled index of patient
self-report questionnaire measures is equally as informative
as ACR 20% improvement criteria (ACR20) responses (30),
DAS28 scores, and pooled indices of all and assessor-derived
measures in the core data set for RA in distinguishing active
treatment from placebo. The PDAS instruments we have
developed reflect the benefits of patient self-assessments. It is
interesting that the PDAS1 uses patient-derived tender joint
counts in preference to patient-derived swollen joint counts
because a recent study of 82 patients with RA demonstrated
that within-patient and patient-physician correlations for
joint tenderness counts were high whereas patient-physician
correlations for joint swelling counts, although significant,
were much lower (31). This study together with our own
findings imply that patient-assessed joint tenderness is the
key measure.
There is debate about the value of summated assessment
measures in clinical practice and the levels of activity they
should represent (32). The DAS28 is widely used in much
of Europe, although there are simplified alternatives, including the Simplified Disease Activity Index reported by
Smolen et al (33), the Patient Activity Scales (PAS and
PAS-II) reported by Wolfe et al (34), the CDAI (26), and the
patient self-report questionnaire Routine Assessment of
Patient Index Data (RAPID) score reported by Pincus et al
(35). Some of these scales, such as the RAPID score, involve patient self-assessment. Interestingly, when Gulfe
and colleagues (36) compared ACR20 responses, DAS28
responses, and RAPID to identify individual responses in
184 outpatients, they found good agreement at the ACR20
level but poor agreement at the ACR50 level. They recommended that this discordance should be taken into account when using response criteria to guide clinical decisions. Similar concerns have been expressed about using
the DAS28 to define the need for tumor necrosis factor
inhibitors (37,38). Despite these concerns, the DAS28 is
recommended for use in clinical practice in the UK (39,40)
and the balance of evidence indicates its use is beneficial
for routine practice (41– 43).
There is no doubt that patient-based assessments, which
have been championed for many years by Pincus and Sokka
(44,45), are of key importance. In this context it is relevant to
consider the relative merits of using existing self-assessed
measures with joint counts (such as the RADAI [21]), without
joint counts (such as the patient activity scale [34]), and the
PDAS score we have developed. In this context Wolfe and
colleagues have argued that the simplicity of instruments
such as the PAS makes them particularly useful in the clinic
(34). Interestingly, there is also evidence that the HAQ alone
performs well in aiding clinical assessments of disease activity (6). On balance we consider there is no single strong
reason to prefer one measure to another. They are all likely to
have benefits and drawbacks. Instead, we suggest that their
use must reflect the circumstances in which patients are
being assessed. In a clinical environment when a physicianbased composite measure such as the DAS28 has been
widely used, it would be possible to replace it with the PDAS
without a major change in the nature of the data being col-
lected. One specific difficulty with patient-based measures,
highlighted by Kievit and colleagues (46), is the concept of
response shift. Kievit et al studied 624 newly diagnosed
patients with RA who had completed 3 years of followup and
found that although the DAS28 and the VAS assessment for
global health were significantly associated, the explained
variance was low (6.7%). Longitudinal regression modeling
showed that VAS assessment for global health improved
during the course of RA, independent of change in DAS28
score, and this was in keeping with a change in patients’
perceptions of the disease. They consider that this type of
response shift mitigates against using patient-generated
One important benefit of replacing the DAS28 with the
PDAS is that the PDAS will involve patients far more directly
in assessing their disease, which is widely considered to be
important in optimizing care (47,48). Measures such as the
PDAS could also be used in Web-based recording of disease
activity, which may well become of growing importance in
future years. Interactive technology including touch-screen
programs and Internet access to questionnaires may also
facilitate the assessment of disease activity. Greenwood and
colleagues have demonstrated that touch-screen computer
systems can be used in rheumatology clinics as a means of
collecting reliable, user-friendly outcome data from patients
(49). Athale et al (50) showed that Web-based computer
health assessment surveys could be undertaken by patients
with RA and that they provided information comparable
with paper versions. The PDAS could readily be adopted in
such a Web-based system for patient assessment. We recognize that in some specific circumstances, such as the recognition of near remission, other patient-generated assessments, such as the RAPID score proposed by Pincus et al (35),
may have advantages, particularly as we have not evaluated
the ability of the PDAS to detect remission or near remission
in RA.
We believe the PDAS is a suitable clinical tool to highlight individual patient concerns and help monitor
progress, including the effectiveness of treatments over
time. It could also be used in epidemiologic studies, and
may even be converted into an economic utility tool. Single-handed practitioners and clinicians working in an environment in which resources are limited could adopt
patient-derived measures of disease activity such as the
PDAS. Overall, the PDAS shows good reliability, validity,
and responsiveness. The main area in which this type of
assessment appears to be of limited use is in determining
the presence of low disease states or remission. Further
work is needed to establish the smallest detectable difference and minimal clinically important difference, low disease activity, and disease remission of the PDAS, as well as
cross-cultural validation. It may also be important to elicit
patients’ views on completing such questionnaires, and
whether or not they believe such an approach can enhance
their involvement in managing their own disease.
We thank Professor G. S. Panayi and Dr. B. Kirkham (Guy’s
Hospital, London) and Dr. N. Chung (Queen Mary Hospital, Sidcup) for their help.
Choy et al
Dr. Choy had full access to all of the data in the study and takes
responsibility for the integrity of the data and the accuracy of the
data analysis.
Study design. Choy, MacGregor, Scott.
Acquisition of data. Choy, Khoshaba.
Analysis and interpretation of data. Choy, Khoshaba.
Manuscript preparation. Choy, Khoshaba, Scott.
Statistical analysis. Choy, Cooper, MacGregor.
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