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The Pediatric Rheumatology International Trials Organization criteria for the evaluation of response to therapy in juvenile systemic lupus erythematosusProspective validation of the disease activity core set.

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Vol. 52, No. 9, September 2005, pp 2854–2864
DOI 10.1002/art.21230
© 2005, American College of Rheumatology
The Pediatric Rheumatology International Trials Organization
Criteria for the Evaluation of Response to Therapy in
Juvenile Systemic Lupus Erythematosus
Prospective Validation of the Disease Activity Core Set
Nicolino Ruperto,1 Angelo Ravelli,1 Ruben Cuttica,2 Graciela Espada,3 Seza Ozen,4
Oscar Porras,5 Flavio Sztajnbok,6 Fernanda Falcini,7 Ozgur Kasapcopur,8 Helen Venning,9
Blanca Bica,10 Rosa Merino,11 Cecilia Coto,12 Joan Ros,13 Gordana Susic,14 Marı́a Luz Gamir,15
Kirsten Minden,16 Yvonne See,17 Yosef Uziel,18 Masha Mukamel,19 Phil Riley,20
Francesco Zulian,21 Alma Nunzia Olivieri,22 Rolando Cimaz,23 Hermann Girschick,24
Ingrida Rumba,25 Silvio Cavuto,1 Angela Pistorio,26 Daniel J. Lovell,27 and Alberto Martini,28
for the Pediatric Rheumatology International Trials Organization (PRINTO)
and the Pediatric Rheumatology Collaborative Study Group (PRCSG)
Objective. To validate and promulgate a core set
of outcome measures for the evaluation of response to
treatment in patients with juvenile systemic lupus erythematosus (SLE).
Methods. In 2001, a preliminary consensusderived core set of measures for evaluating the response
to therapy in juvenile SLE was established. In the
present study, the core set was validated through an
evidence-based, large-scale data collection process that
led to the enrollment of 557 patients from 39 different
countries. Consecutive patients with active disease were
assessed at baseline and after 6 months. The validation
procedures included assessment of feasibility, responsiveness, discriminant and construct ability, agreement
in the evaluation of response to therapy between physicians and parents, redundancy, internal consistency,
and ability to predict a therapeutic response.
Supported by a grant from the European Union (contract
QLG1-CT-2000-00514), the IRCCS G. Gaslini, Genoa, Italy, and the
NIH (grant RO3-AI-44046).
Nicolino Ruperto, MD, MPH, Angelo Ravelli, MD, Silvio
Cavuto, PhD: IRCCS G. Gaslini, Pediatria II, Reumatologia, Genoa,
Italy; Ruben Cuttica, MD: Hospital General de Ninos Pedro de
Elizalde, Buenos Aires, Argentina; 3Graciela Espada, MD: Hospital de
Ninos Ricardo Gutierrez, Buenos Aires, Argentina; 4Seza Ozen, MD:
Hacettepe University Children’s Hospital, Ankara, Turkey; 5Oscar
Porras, MD, PhD: Hospital Nacional de Ninos, San Jose, Costa Rica;
Flavio Sztajnbok, MD: Hospital Universitario Pedro Ernesto, Rio de
Janeiro, Brazil; 7Fernanda Falcini, MD: Ospedale A. Meyer, Florence,
Italy; 8Ozgur Kasapcopur, MD: Cerrahpasa Tip Fakultesi, Istanbul,
Turkey; 9Helen Venning, MD: Queen’s Medical Centre, Nottingham,
UK; 10Blanca Bica, MD: Federal University de Rio de Janeiro, Rio de
Janeiro, Brazil; 11Rosa Merino, MD: Hospital Universitario La Paz,
Madrid, Spain; 12Cecilia Coto, MD: Hospital Pediatrico Universitario
Docente Pedro Borras, Havana, Cuba; 13Joan Ros, MD: Hospital San
Joan de Deu, Barcelona, Spain; 14Gordana Susic, MD: Institute of
Rheumatology, Belgrade, Serbia and Montenegro; 15Marı́a Luz
Gamir, MD: Hospital Ramon y Cajal, Madrid, Spain; 16Kirsten
Minden, MD: Klinikum Berlin-Buch, Berlin, Germany; 17Yvonne See,
MD: KK Women’s and Children’s Hospital, Singapore; 18Yosef Uziel,
MD: Meir Medical Centre, Kfar Saba, Israel; 19Masha Mukamel, MD:
Schneider Children’s Hospital, Petach-Tikvah, Israel; 20Phil Riley,
MD: Great Ormond Street Hospital, London, UK; 21Francesco Zulian, MD: Clinica Pediatria, Padua, Italy; 22Alma Nunzia Olivieri, MD:
Seconda Università degli Studi di Napoli, Naples, Italy; 23Rolando
Cimaz, MD: Clinica Pediatrica de Marchi, Milan, Italy; 24Hermann
Girschick, MD: University School of Medicine Children’s Hospital,
Wurzburg, Germany; 25Ingrida Rumba, MD: University of Latria,
Riga, Latvia; 26Angela Pistorio, MD, PhD: IRCCS G. Gaslini, Servizio
di Epidemiologia e Biostatistica, Genoa, Italy; 27Daniel J. Lovell, MD,
MPH: Children’s Hospital Medical Center, Cincinnati, Ohio; 28Alberto Martini, MD: IRCCS G. Gaslini, Pediatria II, Reumatologia and
Università degli Studi, Genoa, Italy.
Address correspondence and reprint requests to Nicolino
Ruperto, MD, MPH, Pediatric Rheumatology International Trials
Organization (PRINTO), IRCCS G. Gaslini, Università di Genova,
Pediatria II–Reumatologia, Largo Gaslini, 5, 16147 Genoa, Italy.
Submitted for publication December 2, 2004; accepted in
revised form May 16, 2005.
Results. The following clinical measures were
found to be feasible and to have good construct validity,
discriminative ability, and internal consistency; furthermore, they were not redundant, proved responsive to
clinically important changes in disease activity, and
were associated strongly with treatment outcome and
thus were included in the final core set: 1) physician’s
global assessment of disease activity, 2) global disease
activity measure, 3) 24-hour proteinuria, 4) parent’s
global assessment of the patient’s overall well-being,
and 5) health-related quality of life assessment.
Conclusion. The members of PRINTO propose a
core set of criteria for the evaluation of response to
therapy that is scientifically and clinically relevant and
statistically validated. The core set will help standardize
the conduct and reporting of clinical trials and assist
practitioners in deciding whether a patient with juvenile
SLE has responded adequately to therapy.
Systemic lupus erythematosus (SLE) is a multisystem inflammatory disease that is characterized by
protean clinical manifestations, an unpredictable course,
and a substantial risk of morbidity. Although recent
improvements in care have markedly enhanced the
survival of SLE patients who have the most severe and
life-threatening manifestations, disease treatment remains largely empiric and unsatisfactory. One of the
leading factors that has hampered a rational therapeutic
approach to SLE is the lack of standardized and validated measures for assessing the response to therapy.
This deficiency leads to an inability to accurately evaluate or compare the efficacy of drug therapies. To
overcome this problem, the Outcome Measures in
Rheumatology Clinical Trials (OMERACT) group, the
American College of Rheumatology (ACR), and other
investigators (1–3) recommended that outcome measures should be included in SLE trials; however, until
now these proposals have not yet been formally validated in prospective studies or clinical trials.
Although children, adolescents, and adults with
SLE share many signs and symptoms of disease, they
differ in terms of the frequency and severity of disease
activity and damage features (4–8), and treatment approaches should consider the distinctive characteristics
of patients with juvenile SLE as well as their longer life
expectancy. Therefore, it cannot be assumed a priori
that the clinical measures developed for adults are
suitable for children and adolescents. Furthermore,
younger patients with juvenile SLE deserve to be assessed with specific instruments that take into account
the disease and physical/mental age-related issues that
are associated with growth and development. Therefore,
all outcome measures developed for adults need to be
subjected to a critical evidence-based evaluation of their
measurement properties in children and adolescents.
To standardize the evaluation of response to
therapy in patients with juvenile SLE, the Pediatric
Rheumatology International Trials Organization
(PRINTO) (9), in collaboration with the Pediatric
Rheumatology Collaborative Study Group (PRCSG),
and with the support of the European Union and the US
National Institutes of Health, undertook a multinational
effort that was aimed to develop, validate, and promulgate a core set of measures and a definition of clinical
improvement in patients with juvenile SLE, similar to
that already done with the ACR Pediatric 30 criteria for
juvenile rheumatoid arthritis (10–12).
The results of the first part of the study, published previously (13), led to the definition of a preliminary consensus-based (14) core set of domains. Here,
we report the results of the second phase of the project,
which was aimed at formally validating the preliminary
juvenile SLE core set for the evaluation of response to
therapy through a prospective, large-scale data collection process among the members of the PRINTO/
PRCSG networks. Our objective was to further define
and validate the preliminary juvenile SLE core set of
response variables in order to document the response to
therapy in patients with juvenile SLE.
Study design. Enrollment began in June 2001 and
ended in March 2004. The participating PRINTO/PRCSG
members were asked to assess all variables in the preliminary
core set, in all patients seen consecutively in their units who 1)
had a diagnosis of SLE according to the 1997 revised ACR
classification criteria (15,16), were younger than age 18 years,
and were experiencing an active phase of disease, defined as
either the need to start corticosteroid therapy and/or a new
immunosuppressive medication or, in those receiving ongoing
therapy, the need to receive a major increase in the dosage of
corticosteroids and/or immunosuppressive drugs. Six months
after the baseline evaluation, the core set variables were
reassessed in each patient. We chose this study protocol and
time frame to approximate what is usually done in a clinical
trial, with a baseline assessment at the time when treatment is
started and a final assessment at the end of the trial. Patients
were excluded from the study if, at baseline, they were
experiencing drug-induced or spontaneous clinical remission,
were receiving a stable therapeutic regimen, or had a concomitant serious illness.
In each center, written or verbal informed consent was
obtained from a parent or legal guardian, according to the
requirements of the local ethics committees.
Assessment of preliminary core set variables. The
following preliminary core set measures were assessed at
baseline and 6 months later: 1) the physician’s global assessment of the patient’s overall disease activity on a 10-cm visual
analog scale (VAS) (where 0 ⫽ no activity and 10 ⫽ maximum
activity); 2) the parent’s global assessment of the patient’s
overall well-being during the previous week on a 10-cm VAS
(where 0 ⫽ very well and 10 ⫽ very poor); 3) anti–doublestranded DNA (anti-dsDNA) antibodies, determined by
enzyme-linked immunosorbent assay (ELISA), immunofluorescence, or qualitative assessment as being positive or negative; 4) 24-hour proteinuria and the serum creatinine level; 5)
global assessment of disease activity according to the Systemic
Lupus Erythematosus Disease Activity Index (SLEDAI) (17)
(the SLEDAI is a validated measure of SLE disease activity,
also used in children [18], that contains 24 descriptors in 9
organ systems and is weighted to reflect the degree of activity
during the last 10 days; the total SLEDAI score can range from
0 to 105); and 6) health-related quality of life assessment via
the parent’s version of the Child Health Questionnaire (CHQ)
(19,20). Briefly, the CHQ is a generic instrument designed to
capture the physical, emotional, and social components of
health status in children at least 5 years of age; it includes 15
subscales and 2 summary measures, the physical health score
[PhS] and the psychosocial health score [PsS], standardized to
have a mean of 50 and SD of 10; higher scores in the scales
indicate better health-related quality of life.
Additional clinical and laboratory assessments. As
additional tools for assessing global disease activity, we used
the Systemic Lupus Activity Measure (SLAM) (21) and the
European Consensus Lupus Activity Measurement (ECLAM)
(22,23). Both of these tools have been used in children (18,24).
Briefly, the SLAM assesses symptoms that occurred during the
preceding month; it includes 24 clinical manifestations and 8
laboratory parameters, and the maximum possible score is 90.
The ECLAM measures changes in SLE disease activity, with
respect to the last clinical observation (9 clinical manifestations
and 3 laboratory assessments); the total score ranges from 0 to
Because the usefulness of the Health Assessment
Questionnaire (HAQ) has been investigated in adults with
SLE (25,26), we used the Childhood HAQ (C-HAQ) (20,27).
Briefly, the C-HAQ measures a child’s functional ability in 8
activities of daily living. These 8 domains are then averaged to
calculate the C-HAQ disability index, with scores ranging from
0 to 3 (where 0 ⫽ best and 3 ⫽ worst). As part of the C-HAQ,
we also reported the parent’s global assessment of the child’s
pain during the previous week on a 10-cm VAS (where 0 ⫽ no
pain and 10 ⫽ very severe pain). The parent’s version of both
the C-HAQ and the CHQ have been translated and validated
in all of the languages of the participating countries (20).
Additional laboratory tests were a complete blood cell
count, the Westergren erythrocyte sedimentation rate (ESR),
and assessment of serum C3 and C4.
Standardization of laboratory tests. The test results
were standardized based on the normal values provided by
each local laboratory. All values were converted to the international standard (SI) unit system (28), according to the
following formulas: xref ⫽ [(rangeref ⫻ xref)/100] ⫹ minref,
where xref ⫽ value in the SI system, rangeref ⫽ normal range of
the SI system, and minref ⫽ minimum value in the SI system;
and xref ⫽ [(xi ⫺ minOMU)/rangeOMU] ⫻ 100, where xi ⫽ value
in the original measurement unit (OMU), rangeOMU ⫽ normal
range of the OMU, and minOMU ⫽ minimum value of the
OMU. For example, a C3 level of 72 mg/dl (OMU normal
values 80–120 mg/dl) was converted to 0.52 gm/liter (SI normal
values 0.7–1.6 gm/liter).
Validation procedures. Validation of the core set measures was conducted with the use of the OMERACT filter for
outcome measures in rheumatology (29,30). The feasibility or
practicality of the measures was determined by addressing the
issues of brevity, simplicity, ease of scoring, and percentage of
missing values. Face and content validity were based on results
of the previous consensus conference (13). Responsiveness was
examined by determining the ability of each variable to detect
clinically important change between baseline and 6 months and
was measured using the standardized response mean (SRM).
The SRM was calculated as the absolute mean change in score
divided by the SD of an individual’s change in score; 95%
confidence intervals (95% CIs) were also provided (31,32). An
absolute value of 0.2 is considered to represent a small effect,
a value of 0.5 represents a moderate effect, and values of ⱖ0.8
represent a large effect (33,34).
Discriminative ability was assessed by evaluating the
capacity to distinguish patients who experienced improvement
from those who did not. For this purpose, at the end of the
6-month treatment period physicians and parents were asked
(independently of each other) to judge whether the patient’s
disease had improved, was stable, or had worsened as compared with baseline. Physicians and parents were not allowed
to review their baseline global assessments or the results of
other core set variables. Patients who were judged as improving were compared with those who were judged as not
improving (i.e., disease remained stable or worsened) by t-test
or the Mann-Whitney U test, as appropriate. Moreover, the level
of agreement between physicians and parents in the evaluation of
response to therapy was assessed with the kappa statistic (35),
using the threshold proposed by Landis and Koch (36).
Convergent construct validity, which is a form of
validation that seeks to examine whether the construct in
question is related to other measures in a manner consistent
with a priori prediction, was also investigated. As a surrogate
measure, we chose the physician’s global assessment of the
patient’s overall disease activity by Spearman’s rank correlation (where a value of ⬎0.7 was considered high, a value of
0.4–0.7 was moderate, and a value of ⬍0.4 was low). We
predicted that correlation of the underlying construct of response to therapy with the surrogate gold standard measure
would be in the moderate range, and thus would provide a
different perspective and avoid redundancy (see below). The
issue of colinearity (or redundancy) of variables was investigated by means of Pearson’s correlation coefficient; a coefficient of ⱖ0.7 was considered to represent evidence of colinearity.
The internal consistency of the various scales was
determined by Cronbach’s alpha (37), with the following
cutoffs: ⬍0.6 ⫽ poor, 0.6–0.64 ⫽ slight, 0.65–0.69 ⫽ fair,
0.7–0.79 ⫽ moderate, 0.8–0.89 ⫽ substantial, and ⬎0.9 ⫽
almost perfect. We determined that a slight/fair Cronbach’s
alpha would be sufficient to demonstrate the internal consistency of the core set.
Finally, the association between the 5 core set mea-
Table 1. Descriptive characteristics of the variables*
Final core set
Physician’s global assessment of patient’s overall disease
activity (0–10-cm scale)1
Parent’s global assessment of patient’s overall well-being
(0–10-cm scale)1
Proteinuria, gm/24 hours1
ECLAM (range 0–10)1
CHQ physical health summary score (range 40–60)2
Additional measures
SLEDAI (range 0–105)1
SLAM (range 0–90)1
CHQ psychosocial health summary score (range 40–60)2
C-HAQ disability index (0–3)1
Parent’s global assessment of child’s pain (0–10-cm
Anti-dsDNA, IU, by ELISA1
Anti-dsDNA titer, by immunofluorescence1
Anti-dsDNA, no. (%) positive1
Serum creatinine (normal 0.6–1.2 mg/dl)1
ESR (normal 0–30 mm/hour)1
C3 (normal 0.7–1.6 gm/liter)2
C4 (normal 0.2–0.4 gm/liter)2
Hemoglobin, gm/liter2
Month 0
Month 6
% change
(95% CI)
5.8 (3.8, 7.9)
1 (0.3, 2.6)
⫺168 (640, ⫺100)
1.3 (1.2–1.4)
4.5 (1.6, 6.9)
0.8 (0, 2.4)
⫺100 (⫺261, 0)
0.8 (0.7–0.9)
0.3 (0.1, 1.2)
6 (4, 8)
41 (28, 49)
0.1 (0, 0.4)
2 (1, 3)
52 (43, 54)
⫺91 (⫺248, 0)
⫺100 (⫺250, ⫺67)
16 (1, 40)
0.4 (0.3–0.4)
1.3 (1.2–1.4)
0.8 (0.7–0.9)
17 (11, 23)
15 (10, 20)
45 (36, 52)
0.4 (0, 1.4)
3 (0.4, 6.5)
4 (2, 8)
4 (2, 7)
49 (43, 55)
0 (0, 0.3)
0.2 (0, 1.5)
⫺119 (⫺325, ⫺77)
⫺200 (⫺500, ⫺88)
7 (⫺3, 20)
⫺95 (⫺100, 0)
⫺100 (⫺241, 0)
1.1 (1–1.2)
1.3 (1.2–1.4)
0.5 (0.4–0.6)
0.6 (0.5–0.7)
0.7 (0.6–0.8)
140 (40, 300)
160 (40, 321)
34 (53)
0.7 (0.6, 0.8)
57 (32, 91)
0.5 (0.2, 0.8)
0.2 (0.2, 0.2)
10.9 (9.4, 12.1)
⫺100 (⫺347, ⫺20)
⫺100 (⫺700, 0)
0 (⫺18, 14)
⫺135 (⫺342, ⫺28)
32 (2, 71)
13 (0, 28)
12 (2, 22)
0.3 (0.1–0.4)
0.4 (0.3–0.5)
0.1 (0–0.2)
0.9 (0.9–1)
0.6 (0.5–0.7)
0.4 (0.3–0.5)
0.8 (0.7–0.9)
54 (10, 146)
80 (20, 160)
18 (28)
0.7 (0.6, 0.8)
21 (12, 35)
0.8 (0.6, 1.1)
0.2 (0.2, 0.3)
12.5 (11.5, 13.6)
* Except where indicated otherwise, values are the median (interquartile range). 1 indicates that a higher score for that variable denotes worse
disease activity; 2indicates that a lower score denotes worse disease activity. SRM ⫽ standardized response mean; 95% CI ⫽ 95% confidence
interval; ECLAM ⫽ European Consensus Lupus Activity Measurement; CHQ ⫽ Child Health Questionnaire; SLEDAI ⫽ Systemic Lupus
Erythematosus Disease Activity Index; SLAM ⫽ Systemic Lupus Activity Measure; C-HAQ ⫽ Childhood Health Assessment Questionnaire;
anti-dsDNA ⫽ anti–double-stranded DNA; ELISA ⫽ enzyme-linked immunosorbent assay; ESR ⫽ erythrocyte sedimentation rate.
† Number of patients for whom both the baseline and 6-month evaluations were available.
sures and response to therapy as judged by the attending
physician was evaluated through a multivariate logistic regression analysis, after having dichotomized the core set measures
according to the best cutoffs obtained from the receiver
operating characteristic (ROC) curve analysis (38). Determination of the best cutoff for each core set variable will help
physicians decide whether a patient has improved based on the
absolute change in that particular measure.
Data were entered in an Access XP database and
analyzed by 2 of the authors (NR and AP) with Excel XP
software (Microsoft, Redmond, WA), XLSTAT-Pro 6.1.9 software (Addinsoft, Brooklyn, NY), Statistica 6.0 software (StatSoft, Tulsa, OK), and Stata version 7.0 software (Stata, College
Station, TX).
Demographic characteristics. A total of 557 patients were enrolled from 118 centers in 39 countries, as
follows: Argentina (n ⫽ 50), Australia (n ⫽ 1), Belgium
(n ⫽ 2), Brazil (n ⫽ 64), Bulgaria (n ⫽ 15), Canada (n ⫽
25), Chile (n ⫽ 8), Costa Rica (n ⫽ 10), Croatia (n ⫽ 5),
Cuba (n ⫽ 7), The Czech Republic (n ⫽ 7), Denmark
(n ⫽ 6), Finland (n ⫽ 4), France (n ⫽ 11), Germany
(n ⫽ 28), Greece (n ⫽ 11), Hungary (n ⫽ 5), India (n ⫽
6), Israel (n ⫽ 20), Italy (n ⫽ 91), Korea (n ⫽ 5), Latvia
(n ⫽ 6), Mexico (n ⫽ 11), The Netherlands (n ⫽ 10),
Norway (n ⫽ 2), Poland (n ⫽ 5), Portugal (n ⫽ 1), Saudi
Arabia (n ⫽ 2), Serbia and Montenegro (n ⫽ 23),
Singapore (n ⫽ 6), Slovakia (n ⫽ 12), Slovenia (n ⫽ 4),
Spain (n ⫽ 26), Sweden (n ⫽ 9), Switzerland (n ⫽ 3),
Tunisia (n ⫽ 1), Turkey (n ⫽ 20), the UK (n ⫽ 23), and
the US (n ⫽ 12).
Of the 557 patients enrolled, 24 patients (4%) did
not complete the study; 14 of these patients were lost to
followup, and the other 10 died (6 patients died of sepsis,
1 patient died of pancreatitis, 1 patient committed suicide,
1 patient had multiorgan failure, and 1 patient died of
unknown causes). Of the 533 patients who completed both
the baseline and 6-month assessments, 438 (82%) were
female and 95 (18%) were male; the median age at
diagnosis was 12.7 years (interquartile range [IQR] 11–14.7
years), and the median disease duration was 0.5 years (IQR
0.2–2.4 years). The median number of ACR classification
criteria at the time of SLE diagnosis was 6 (IQR 5–7).
Feasibility and responsiveness. Table 1 shows
the characteristics of each clinical variable in the 533
Figure 1. Ability of the variables (mean score changes) included in the core set to discriminate between patients
who improved versus patients who did not improve according to the physician’s and the parent’s evaluation after
6 months of therapy. Data are presented as box plots, where the squares inside the boxes represent the mean, and
the lines outside the boxes represent the 95% confidence interval. P values refer to the discriminant ability of the
variables according to the physician’s evaluation and to the parent’s evaluation of response to therapy. MD ⫽
physician; ECLAM ⫽ European Consensus Lupus Activity Measurement; CHQ ⫽ Child Health Questionnaire.
patients for whom the variable was measurable at both
time points. The frequency of missing data was uniformly ⬍10%, demonstrating that all variables had
excellent feasibility. At baseline, patients had, on average, a high level of disease activity, as shown by the
median of the physician’s and parent’s global assessment
and that of the SLEDAI. Of the 397 patients for whom
24-hour proteinuria values were available at both time
points, 181 (46%) had renal disease at baseline (defined
as 24-hour proteinuria of ⬎0.5 gm/day), and 78 patients
Table 2.
Construct validity for the variables included in the final core set, by Pearson’s correlation
24-hour proteinuria
CHQ physical health summary score
Parent’s global assessment of
patient’s overall well-being
Physician’s global
CHQ physical health
(0–10 cm)
proteinuria ECLAM
summary score
* Correlations for the absolute change in score (value at month 6 minus value at month 0) were performed
and were expected to be in the moderate range (0.4–0.7). A Pearson’s coefficient of ⱖ0.7 was considered
to represent evidence of redundancy. ECLAM ⫽ European Consensus Lupus Activity Measurement;
CHQ ⫽ Child Health Questionnaire.
(20%) had proteinuria of ⬎2 gm/day. Good responsiveness to clinical change (SRM ⱖ0.8) was demonstrated by
the physician’s and parent’s global assessment, the 3
global disease activity tools (with the SLAM and the
ECLAM being slightly superior to the SLEDAI), the
physical health summary score of the health-related
quality of life tool (the CHQ PhS), the ESR, and the
hemoglobin concentration.
Assessment of the C-HAQ score, the parent’s
global assessment of the child’s pain, and the C3 level
revealed intermediate responsiveness (SRM ⱖ0.6 and
⬍0.8), whereas 24-hour proteinuria, anti-dsDNA antibodies, the psychosocial health summary score of the
health-related quality of life tool (the CHQ PsS), the
serum creatinine level, and the C4 level proved to be
poorly responsive (SRM ⬍0.6). Notably, the SRM for
24-hour proteinuria rose to 0.6 when only the 181
patients who had renal disease at baseline (⬎0.5 gm/day)
were assessed and to 1.1 when only the 78 patients with
proteinuria of ⬎2 gm/day were assessed. The poor
performance of serum creatinine is probably attributable
to the low number of patients (33 of 514 [6%]) who had
an abnormal level at baseline (any value ⬎1.2 mg/dl).
For this reason, only 24-hour proteinuria was retained as
an indicator of kidney disease activity in subsequent
The results obtained for anti-dsDNA antibodies
should be regarded with caution, because the determination of these antibodies was performed with different
technical methods, which included ELISA in 228 patients (43%) and indirect immunofluorescence in 84
patients (16%); in 64 patients (12%), only a qualitative
evaluation (positive versus negative) was available. This
heterogeneity precluded combining results obtained
with different methods, and anti-dsDNA were therefore
excluded from further consideration (see below). The
white blood cell count with differential and the platelet
count showed poor responsiveness (data not shown).
Taken together, these results did not show a
major advantage for any of the additional variables over
the variables included in the preliminary core set (13).
However, due to the superior responsiveness to clinically
important change (and minor skewness) demonstrated
by the ECLAM as compared with the SLEDAI, the
ECLAM was selected for use instead of the SLEDAI;
moreover, the ECLAM is the only one of the 3 indexes
that uses the entire range of possible scores (range 0–10;
median score at baseline 5). Furthermore, since, of the 2
summary scales of the CHQ (PhS and PsS), only the PhS
yielded significant results in previous analyses, we used
only the CHQ PhS as a measure of health-related
quality of life in all subsequent evaluations.
Discriminant validity. Figure 1 shows the variables included in the final core set, which demonstrated
significant ability in discriminating patients who were
improved or not improved at 6 months based on the
physician’s or parent’s assessment of the child’s response
to therapy. Other variables that were able to show a
statistically significant discriminant ability according to
the physician’s and/or the parent’s evaluation were antidsDNA antibodies tested by ELISA, the SLEDAI, the
SLAM, the ESR, the lymphocyte count, the hemoglobin
concentration, several subscales of the CHQ, all subscales of the C-HAQ, and the parent’s global assessment
of the child’s pain (data not shown). Notably, agreement
between physicians and parents in the evaluation of
response to therapy was substantial (␬ ⫽ 0.72 [95% CI
Construct validity and redundancy. Table 2
shows Pearson’s correlation coefficients for the baselineto–6-month change in the final core set variables. This
analysis was carried out to assess both the construct
Table 3. Internal consistency of the variables in the core set*
Cronbach’s ␣
Physician’s global assessment of the
patient’s overall disease activity
24-hour proteinuria
CHQ physical health summary score
Parent’s global assessment of the
patient’s overall well-being
* Values shown are Cronbach’s alpha when the individual variable is
removed. The performance of the final core set, including all 5
variables, was 0.62. ECLAM ⫽ European Consensus Lupus Activity
Measurement; CHQ ⫽ Child Health Questionnaire.
validity, using as the reference measure the physician’s
global assessment of the patient’s overall disease activity, and the colinearity (or redundancy) of clinical
variables. As expected, the correlation with the physician’s global assessment of the patient’s overall disease
activity was in the moderate range (r ⫽ ⫾0.4 to ⫾0.5)
for all variables, except for 24-hour proteinuria, which
showed poor correlation. These results demonstrate that
the final 5 core set variables have good convergent
construct validity. Finally, there was no redundancy
between the core set variables (Pearson’s correlation
coefficient ⬍0.7).
Internal consistency. Cronbach’s alpha was used
to assess the degree to which the final 5 core set
variables “hold together” or, in other words, the degree
to which they were measuring the same underlying
construct. As shown in Table 3, assessment of the
absolute change in scores for all 5 variables combined
yielded a Cronbach’s alpha of 0.62, meaning, as expected, that there was slight internal consistency. When
Table 4.
Table 5. Domains and suggested variables included in the final core
set for the evaluation of response to therapy in juvenile SLE*
Suggested variable(s)
Physician’s global assessment of the
patient’s overall disease activity
Parent’s global assessment of the
patient’s overall well-being
Renal involvement
Global juvenile SLE disease activity
Health-related quality of life
10-cm VAS
10-cm VAS
24-hour proteinuria
CHQ physical health
summary score
* SLE ⫽ systemic lupus erythematosus; VAS ⫽ visual analog scale;
ECLAM ⫽ European Consensus Lupus Activity Measurement;
SLEDAI ⫽ Systemic Lupus Erythematosus Disease Activity Index;
SLAM ⫽ Systemic Lupus Activity Measure; CHQ ⫽ Child Health
we add anti-dsDNA antibodies (determined by ELISA
or immunofluorescence), Cronbach’s alpha fell to 0.003,
suggesting that the inclusion of this measure in the core
set led to a disruption of its internal consistency.
Association between changes in each of the 5 core
set measures and overall outcome. The ability of the
change in each core set variable to predict the response
to therapy was analyzed in a multivariate analysis, which
used as explanatory variables the baseline-to–6-month
changes in each outcome measure and as the dependent
variable the physician’s judgment of the treatment outcome at 6 months (improvement or no improvement). In
the final model (Table 4), the physician’s global assessment of the patient’s overall disease activity and the
ECLAM appeared to be the strongest predictors of
response to therapy, whereas the predictive ability of the
other 3 variables did not reach statistical significance.
Logistic regression model to predict improvement according to the physician’s evaluation
OR (95% CI)
P, likelihood ratio test
Physician’s global assessment of
patient’s overall disease activity
24-hour proteinuria
CHQ physical health summary score
Parent’s global assessment of patient’s
overall well-being
5.2 (2.6–10.5)
2.1 (1.0–4.3)
1.3 (0.6–2.5)
1.5 (0.7–3.3)
1.5 (0.7–3.2)
* Predictions were based on the absolute change in the variables included in the final core set. Variables
were dichotomized according to the best cutoffs obtained from the receiver operating characteristics
(ROC) curve analysis. The area under the ROC curve of the model was equal to 0.79. The best cutoffs
(sensitivity and specificity) were as follows: for physician’s global assessment of the patient’s overall
disease activity, ⱕ⫺2.1 (sensitivity 78.8%, specificity 69%); for the European Consensus Lupus Activity
Measurement (ECLAM), ⱕ⫺3 (sensitivity 74.6%, specificity 67%); for 24-hour proteinuria, ⱕ⫺0.08
(sensitivity 59.4%, specificity 63.2%); for the Child Health Questionnaire (CHQ) physical health summary
score, ⬎4.26 (sensitivity 66.2%, specificity 60%); for parent’s global assessment of the patient’s overall
well-being, ⱕ⫺1.1 (sensitivity 69.3%, specificity 60.8%). OR ⫽ odds ratio; 95% CI ⫽ 95% confidence
Selection of the final core set. Taken together,
the results of the validation analyses showed that the
final core set for the evaluation of response to therapy in
juvenile SLE has excellent psychometric properties.
Table 5 presents the 5 domains and the related suggested variables used to measure each domain that is
included in the final core set.
In this report, we present the final validated
PRINTO core set of clinical and laboratory measures for
the assessment of response to therapy in patients with
juvenile SLE. To ensure an evidence-based selection
process, the evaluative properties of candidate variables
were carefully assessed using a large prospective data
collection procedure and a comprehensive validation
process that closely mimicked the design of a clinical
trial. The selected variables were shown to be feasible
and to have good construct validity, discriminative ability, and internal consistency; furthermore, they were not
redundant, they were responsive to clinically important
change, and they were strongly associated with treatment outcome. Validation of the PRINTO core set is a
fundamental step in the process of developing a definition of improvement in juvenile SLE (39).
We did not attempt to create specific combinations of variables for the various types of organ involvement in juvenile SLE. Rather, we attempted to make the
core set robust enough to cover all disease phenotypes,
focusing on the central features of the physician’s subjective estimation of the level of disease activity, global
disease activity scoring, parent’s global assessment of the
patient’s overall well-being, and the health-related quality of life; only a specific domain aimed to assess renal
disease, which represents one of the most important and
frequent organ involvements in juvenile SLE, was included. Although it is uncertain whether use of agents
with therapeutic benefit in juvenile SLE would lead to
improvement in all core set measures, it is important
that any therapy that is aimed at improving disease in
one organ system does not worsen disease elsewhere. It
should be kept in mind, however, that the recommended
variables are not more than a minimal core set, and that
investigators can measure as many other variables as
they deem appropriate for the major hypothesis that is
being tested. For example, serum creatinine, creatinine
clearance, the protein:creatinine ratio, urine casts, red
blood cells, and other variables would be considered for
inclusion in clinical trials specifically focused on juvenile
SLE nephritis (40); the same approach would apply to a
study on central nervous system disease, which would
require specific end points, such as findings on magnetic
resonance imaging, electroencephalogram, singlephoton–emission computed tomography, and psychometric tests.
Ideally, outcome criteria must be scientifically
valid, clinically relevant, and feasible from a practical
standpoint. However, identifying criteria for SLE that
fulfill all of these requirements has been a difficult task
(1–3,40,41). The OMERACT and the ACR groups
recommended that any trial in SLE should include a
disease activity measure (1–3). Thus far, however, there
has been no consensus as to which measure is preferable.
In our study, the decision to use the ECLAM was based
on the indication of the statistical validation; notably,
this index has been shown to be sensitive to change in
another study of juvenile SLE (24). In our analysis, the
responsiveness to change of the ECLAM was equal to
that of the SLAM and was slightly superior to that of the
SLEDAI. Interestingly, in the logistic regression model
to predict improvement according to the physician’s
evaluation (Table 4), we observed the same cutoff (less
than or equal to ⫺3) for the ECLAM as that reported in
a recent study by the ACR Ad Hoc Committee to define
response criteria for SLE based on measures of overall
disease activity (3).
Serologic markers, including anti-dsDNA antibodies, play an important role in the assessment of
disease activity in SLE, but they are imperfect indicators
of major changes in disease activity when used alone;
thus, most clinicians do not respond to isolated change
in these tests by modifying therapy (40). Anti-dsDNA
antibodies were included in the preliminary core set (13)
because they were thought to be an important measure
for the evaluation of response to therapy. The results of
the validation analysis led to deletion of this parameter
from the final core set, because it was found to be poorly
responsive to change, and its inclusion into the core set
of variables led to a marked decrease in internal consistency. Our findings should be regarded with caution,
however, because anti-dsDNA antibodies were detected
with 3 different methods across study centers. Use of
standardized methods and the availability of a central
laboratory are needed to address this issue more specifically. Concerning serum complement, this measure was
not incorporated in the final core set because its responsiveness was only intermediate, and because of the fact
that incomplete normalization due to inherited deficiencies may preclude its reliability in the definition of
clinical response (42,43).
Consensus conference attendees believed that
24-hour proteinuria, which is a single-organ measure, is
worth retaining in the criteria, because it is of foremost
importance in the clinical assessment of patients with
juvenile SLE. It was agreed, however, that in cases in
which a patient has no signs of renal disease at the start
of a trial, this feature can be omitted from the assessment of therapeutic response, and that only a 4-item
core set should be used. Because proteinuria may reflect
renal damage in the absence of renal activity, a careful
assessment of activity parameters of juvenile SLE nephritis should be done to confirm that the patient has
active renal disease. Furthermore, the fact that some
drugs, such as angiotensin-converting enzyme inhibitors,
may lead to a reduction in proteinuria should be taken
into account (40).
We believe this is the first study of juvenile SLE
or other chronic rheumatic diseases in which the assessment of health-related quality of life has been incorporated in the definition of a therapeutic response. Indeed,
in recent years, it has been increasingly recognized that
health-related quality of life measures must be included
in SLE trials because they address aspects of SLE and its
impact that are not fully captured by other outcome end
points (1,2,44,45). In general, the patient’s perspective in
outcome assessment is becoming increasingly important,
because it has been suggested that a patient’s perceptions regarding his or her juvenile SLE is frequently
different from those of the clinician (46). It is essential
that trials demonstrating improvement in a specific
organ or in disease activity demonstrate no or minimal
worsening in measures of quality of life. We previously
observed that the physical health scale of the CHQ (but
not the psychosocial health scale) had satisfactory psychometric properties and had the advantage of being
available in several different languages (20). Greater
impairment according to the ratings on the physical
health domain of the CHQ as compared with the
psychosocial health domain was recently demonstrated
in a large international cohort of patients with juvenile
SLE (47). Further insights into the measurement properties of the health-related quality of life tools, including
the potential impact of evaluations reported by children
versus those reported by parents, will be obtained when
these tools are used in the context of a clinical trial.
Our study has certain limitations, which include
the fact that it was not conducted in the context of a real
clinical trial, and that the use of corticosteroids or
immunosuppressive drugs as intervention therapy was
not standardized based on recent recommendations (48)
and might have led to changes in the level of disease
activity much greater than those that would be expected
in trials of novel immunosuppressive or biologic agents.
The main strength of the study is the large amount of
prospectively collected data, which ensured an evidencebased validation analysis. To our knowledge, this is the
first time that clinical measures of juvenile SLE have
been tested longitudinally for their statistical performance, individually and as a group.
In conclusion, we have presented the validated
PRINTO core set of outcome domains for the evaluation of response to therapy in juvenile SLE, which will
constitute the basis for creating a definition of improvement to be used in randomized clinical trials. This will
allow improved assessment of the efficacy of new therapeutic agents or regimens, with greater validity and
We are indebted to Drs. Anna Tortorelli, Monica
Tufillo, and Elisabetta Maggi for their help in data handling,
their organization skills, and overall management of the
project. We are also thankful to Dr. Luca Villa and Mr.
Michele Pesce for their help in database development.
We thank the following members of PRINTO who
participated as investigators in the trial and whose enthusiastic
efforts made this work possible: Maria Apaz, MD, Stella
Garay, MD, Silvia Meiorin, MD, Ricardo Russo, MD (Argentina); Kevin Murray, MD (Australia); Rik Joos, MD (Belgium); Carlos Henrique M. Da Silva, MD, Virginia Ferriani,
MD, Odete Hilario, MD, Claudia Machado, MD, Sheila
Oliveira, MD, Iloite Scheibel, MD, Clovis Artur Silva, MD
(Brazil); Dimitrina Mihaylova, MD (Bulgaria); Miroslav Harjacek, MD, Lana Tambic-Bukovac, MD (Croatia); Ximena
Norambuena, MD, Arnoldo Quezada, MD (Chile); Santa
Gomez Conde, MD (Cuba); Pavla Dolezalova, MD, Dana
Nemcova, MD, Katerina Jarosova, MD (Czech Republic);
Troels Herlin, MD, Susan Nielsen, MD (Denmark); Pekka
Lahdenne, MD (Finland); Brigitte Bader-Meunier, MD,
Michel Fischbach, MD, Sylvie Gandom-Laloum, MD, Chantal
Job Deslandre, MD, Isabelle Kone Paut, MD, Anne-Marie
Prieur, MD, Danièle Sommelet, MD (France); Jurgen Brunner, MD, Guenther Dannecker, MD, Stephan Ehl, MD, Ivan
Foeldvari, MD, Gerd Ganser, MD, Gerd Horneff, MD, Hartmut Michels, MD, Johannes Roth, MD, Claudia Sengler, MD,
Stephanie Schaueburg, MD (Germany); Christina Dracou,
MD, Vasiliki Galanopoulou, MD, Florence KanakoudiTsakalidou, MD, Jenny Pratsidou-Gertsi, MD, Olga Voygioyka, MD (Greece); Zsolt Balogh, MD, Ilonka Orban, MD
(Hungary); Amita Aggarwal, MD (India); Judith Barash, MD,
Riva Brik, MD, Liora Harel, MD, Joseph Press, MD, Tsivia
Tauber, MD (Israel); Maria Alessio, MD, Roberto Barcellona,
MD, Fabrizia Corona, MD, Elisabetta Cortis, MD, Rosario Di
Toro, MD, Franco Garofalo, MD, Loredana Lepore, MD,
Alfredo Maria Lurati, MD, Sara Garozzo, MD, Valeria Gerloni, MD, Silvana Martino, MD, Lucia Trail, MD (Italy);
Sang-Cheol Bae, MD, Chung Il Joung, MD (Korea); Valda
Stanevicha, MD, Andrejs Scegolevs, MD (Latvia); Ruben
Burgos-Vargas, MD, Carolina Duarte, MD, Raul Gutierrez
Suarez, MD (Mexico); Lisetta Van Suijlekom Smit, MD,
Rebecca Ten Cate, MD, Marion Van Rossum, MD, Nico
Wulffraat, MD (The Netherlands); Berit Flato, MD (Norway);
Anna Maria Romicka, MD, Malgorzata Wierzbowska, MD
(Poland); Manuel Salgado, MD (Portugal); Sulaiman AlMayouf, MD (Saudi Arabia); Mirjana Jovanovic, MD, Aleksandra Minic, MD, Srdjan Pasic, MD, Jelena Vojinovic, MD
(Serbia and Montenegro); Helena Koskova, MD, Dagmar
Mozolova, MD, Jozef Rovensky, MD, Veronika Vargova, MD,
Richard Vesely, MD (Slovakia); Tadej Avcin, MD (Slovenia);
Immaculada Calvo, MD, Julia Garcia Consuegra, MD, Consuelo Modesto, MD, Maria Rosa Roldan Molina, MD (Spain);
Anders Fasth, MD, Stefan Hagelberg, MD, Bo Magnusson,
MD (Sweden); Michael Hofer, MD, Traudel Saurenmann,
MD, Marie-Josephe Sauvain, MD (Switzerland); Rym Hajri
Ben Ammar, MD (Tunisia); Aysin Bakkaloglu, MD, Huri
Ozdogan, MD, Rezan Topaloglu, MD (Turkey); Virginia
Brown, Helen Foster, MD, Madeleine Rooney, MD, Pat Woo,
MD (UK); Edward H. Giannini, MSc, DrPH, Gloria Higgins,
MD, Sampath Prahalad, MD, Marilyn Punaro, MD, Anne
Reed, MD, Robert Rennebohm, MD, Peter Reuman, MD
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