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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Objective

To develop widely acceptable preliminary criteria of global flare for childhood-onset systemic lupus erythematosus (cSLE).

Methods

Pediatric rheumatologists (n = 138) rated a total of 358 unique patient profiles with information about the cSLE flare descriptors from 2 consecutive visits: patient global assessment of well-being, physician global assessment of disease activity (MD-global), health-related quality of life, anti–double-stranded DNA antibodies, disease activity index scores, protein:creatinine (P:C) ratio, complement levels, and erythrocyte sedimentation rate (ESR). Based on 2,996 rater responses about the course of cSLE (baseline versus followup), the accuracy (sensitivity, specificity, and area under the receiver operating characteristic curve) of candidate flare criteria was assessed. An international consensus conference was held to rank these candidate flare criteria as per the American College of Rheumatology recommendations for the development and validation of criteria sets.

Results

The highest-ranked candidate criteria considered absolute changes (Δ) of the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) or British Isles Lupus Assessment Group (BILAG), MD-global, P:C ratio, and ESR; flare scores can be calculated (0.5 × ΔSLEDAI + 0.45 × ΔP:C ratio + 0.5 × ΔMD-global + 0.02 × ΔESR), where values of ≥1.04 are reflective of a flare. Similarly, BILAG-based flare scores (0.4 × ΔBILAG + 0.65 × ΔP:C ratio + 0.5 × ΔMD-global + 0.02 × ΔESR) of ≥1.15 were diagnostic of a flare. Flare scores increased with flare severity.

Conclusion

Consensus has been reached on preliminary criteria for global flares in cSLE. Further validation studies are needed to confirm the usefulness of the cSLE flare criteria in research and for clinical care.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Systemic lupus erythematosus (SLE) is a complex, chronic, multisystem autoimmune inflammatory disease that primarily targets young women of nonwhite ancestry (1, 2). Up to 20% of patients are diagnosed during childhood, i.e., prior to age 16 years (cSLE), and their disease has a less favorable prognosis, particularly with respect to multiorgan and kidney involvement, when onset occurs early in life (3–5). The course of cSLE is characterized by episodes of clinically relevant worsening or disease flares, followed by periods of improvement that are generally the result of more intensive drug therapy. A flare of cSLE has been defined as “a measurable worsening of SLE disease activity in at least one organ system, involving new or worse signs of disease that may be accompanied by new or worse SLE symptoms; depending on the severity of the flare, more intensive therapy may be required” (6). However, at present, there are no generally accepted criteria or algorithms to determine whether a patient has experienced a flare of global disease in cSLE.

In an earlier phase of this project, an international consensus was reached about a set of cSLE flare descriptors for identifying flares in cSLE patients. Previous research demonstrated that the scores of a disease activity measure alone are inadequate for identifying flares (7). Moreover, there was consensus around the need to discriminate 3 levels of flare severity: mild or minor, moderate, and major or severe flares (6).

The objectives of this phase of the project were to apply consensus formation methodology to develop preliminary criteria of global flare of cSLE under consideration of the cSLE flare descriptors using patient profile ratings that were completed by an international group of pediatric rheumatologists, and ranking these candidate flare criteria under consideration of the American College of Rheumatology (ACR) suggested recommendations for development and validation of criteria sets (8).

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

The overall approach to this phase of the project (Figure 1) was based on the methodologic framework successfully employed in pediatric rheumatology in the past and as has been detailed by the Classification and Response Criteria Subcommittee of the ACR Committee on Quality Measures (8). The initial results of the consensus formation with respect to the domains and parameters to be considered in future cSLE flare criteria are described elsewhere (6). Briefly, pediatric rheumatologists who were members of the Childhood Arthritis and Rheumatology Research Alliance, the Juvenile Lupus Working Group of the Pediatric Rheumatology European Society, the Pan American League of Associations for Rheumatology, or the ACR were invited to answer Delphi surveys. Subsequently, responses to 2 Delphi surveys resulted in consensus around a common definition of cSLE global flares, the cSLE flare descriptors, followed by a data-driven exploration of candidate flare criteria (6). As opposed to previous criteria for flare in other pediatric rheumatic diseases, the latter suggested that uniform percentage changes are unlikely sufficient to capture cSLE flares with high sensitivity, and that other statistical techniques may yield cSLE flare criteria with higher accuracy.

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Figure 1. Study design. cSLE = childhood-onset systemic lupus erythematosus.

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We now present the phase of the project aimed at the development of preliminary criteria of global flare of cSLE. This encompassed patient profile ratings by pediatric rheumatologists from Australia, Africa, Asia, Europe, and the Americas (step 1). The interpretation of the “true” disease course of a given patient profile was done using 2 approaches, which resulted in 2 distinct data sets for the subsequent validation exercises (step 2). Various candidate flare algorithms were generated and their ability to discriminate patients with flare was tested using the patient profile ratings (step 3); subsequently, during a consensus conference, these candidate flare criteria were ranked under consideration of information from the medical literature, statistical performance, as well as reliability, feasibility, and face validity as per the ACR guidance document and the Outcome Measures in Rheumatology Clinical Trials filter (step 4) (8).

Step 1: patient profiles and ratings of disease course of a patient profile.

Six of the authors (HIB, DLM, LBT, GCH, LES, RM) conducted a pilot study to test the format of the patient profile. Built on this pilot study, we generated 358 unique patient profiles, using prospectively collected cohort data of patients with cSLE (6, 9–11). Information for 137 patient profiles was used previously to explore various statistical methods that might be utilized when developing cSLE flare criteria (6).

Patients whose disease course is reflected in the patient profile had a history of constitutional symptoms (89%) and cSLE features pertaining to the mucocutaneous (90%), musculoskeletal (86%), hematologic (86%), renal (69%), neurologic (35%), vascular (31%), cardiac (29%), and gastrointestinal (21%) systems. Therefore, the relative frequency of organ involvement was comparable to that reported in the literature (12).

Data selected for the patient profile included all available visit pairs (baseline to followup) of cSLE patients considered to have had a flare as per the treating physician. Using a random-number approach, we selected 50 visit pairs representing “stable disease” as per the treating pediatric rheumatologist.

Each patient profile provided data about a cSLE patient at the time of a baseline visit and a followup visit approximately 3–6 months later. For each patient profile visit, the cSLE flare descriptors were provided (6): 1) physician assessment of overall disease activity as measured on a visual analog scale (VAS), with a range from 0–10 (MD-global; where 0 = inactive disease and 10 = very active disease); 2) parent assessment of patient overall well-being as measured on a VAS, with a range from 0–10 (where 0 = very poor and 10 = very well); 3) health-related quality of life as measured by the Child Health Questionnaire (CHQ) physical summary score (PhS); 4) proteinuria as measured by timed urine collection or spot protein:creatinine (P:C) ratio; 5) erythrocyte sedimentation rate (ESR); 6) levels of the complements C3 and C4; 7) summary score of a validated disease activity index, i.e., the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) (13) and the British Isles Lupus Assessment Group (BILAG) (14, 15); and 9) levels of anti–double-stranded DNA (anti-dsDNA) antibodies, where changes between visits were categorized as follows: abnormal/newly normal, normal/normal, abnormal/abnormal, and normal/newly abnormal. Consensus was reached (Delphi surveys) that medication use should not be considered as a variable in future criteria sets of cSLE flare. Details on the format of the patient profile are provided in Supplementary Appendix A (available in the online version of this article at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2151-4658).

Patient profile raters were randomly assigned to assess the disease course of a maximum of 40 patient profiles. The disease course between visits was categorized by the patient profile raters as follows: major flare, moderate flare, minor flare, unchanged, or improved. Therefore, a global flare was considered as “present” whenever the disease course was rated as a minor, moderate, or major flare. The patient profile raters also provided feedback about which of the information provided was most important for their disease course assignment.

Step 2: adjudication of disease course of the patient profile.

A randomization scheme was preplanned to ensure that each patient profile was sent to approximately 10 raters, with the ratio of American and international raters matching that of the patient profile raters' pool (∼1:1). Patient profiles with fewer than 4 ratings were regarded as “invalid” or “unqualified” and were excluded from further consideration.

Only “qualified” patient profiles with successful adjudication were considered in step 3. Given that patient profile raters may not necessarily agree on the disease course, the “true” overall course of cSLE for a given patient profile was adjudicated using 2 approaches: 1) 67% rule: at least 2 of 3 of the raters agreed on a given disease course, and 2) majority rule: the majority of the raters of a patient profile agreed on a given disease course.

Step 3: generation of candidate flare criteria and assessment of performance.

Three principal strategies were employed to develop a series of candidate flare algorithms: 1) consideration of relative changes of core disease descriptors, a method utilized in other response criteria used in pediatric rheumatology; therefore, we generated candidate flare criteria using uniform percentage changes of the cSLE flare descriptors between 20% and 70% in 5% increments, and furthermore, we developed algorithms that considered absolute baseline to followup changes of the cSLE flare descriptors using 2 statistical techniques; 2) multinomial logistic regression, which yields a numerical “flare score” (or log odds of flare) calculated from the changes of several or all cSLE flare descriptor predictors; and 3) Classification Tree Analysis (CART) models, which use Boolean rules to identify global flares of cSLE (16) and also feature a “CART score.”

As was deemed important based on previous consensus, both strategies 2 and 3 also allow for the estimation of flare severity (minor versus moderate versus major) using the “flare score” or “CART score,” respectively (6).

Statistical analysis in preparation of the testing of candidate flare criteria.

Using the 67% rule and the majority rule data set, each cSLE flare descriptor was assessed for its association with another cSLE flare descriptor by multiple logistic regression analysis. Given the intended widespread use of the cSLE flare criteria, we tested whether there were systematic differences in the ratings provided by raters from different geographic regions or with varying professional experience as measured by the duration of medical practice. Agreement among raters was assessed using intraclass correlation coefficients (ICCs) and/or kappa statistics. An ICC or a kappa value can be interpreted as follows: poor agreement = ICC or κ <0.4; fair to good agreement = ICC or κ ≥0.4 to 0.75; and substantial to excellent agreement = ICC or κ >0.75 (17).

We also examined the cSLE flare descriptors for internal redundancy using partial Pearson's correlation coefficients (rp), which allow for the pairwise assessment of changes of the cSLE flare descriptors, while adjusting for the remaining cSLE flare descriptors. High (rp = ≥0.6) or very high (rp = ≥0.8) values support redundancy or indicate that algorithms containing both cSLE flare descriptor predictors could have the potential problem of colinearity, which may cause unstable estimates, i.e., whether the patient truly has experienced a flare or not.

Performance and accuracy.

Each candidate flare criterion was assessed for diagnostic accuracy using receiver operating characteristic (ROC) curve analysis. Specifically, the area under the ROC curve (AUC) was calculated, and the diagnostic accuracy was considered outstanding, excellent, good, fair, and poor if the AUC was in the range of 0.9–1.0, 0.81–0.90, 0.71–0.80, 0.61–0.70, and ≤0.60, respectively (18).

Each candidate flare algorithm derived by multinomial logistic regression consisted of several cSLE flare descriptor predictors, and provides multiple “flare scores” (or log odds of flare). Considering all possible flare scores, the overall diagnostic accuracy of an algorithm can be estimated by means of the AUC.

For each algorithm, a “flare score threshold” was defined for clinical use and for comparison of the statistical performance of the pool of candidate flare algorithms. When using a certain flare algorithm, the assignment of a patient's disease course (here: major/moderate/minor flare versus no flare) can be made by comparing the patient's “flare score” to the “flare score threshold.” Consensus conference participants concurred that the “flare score threshold” for a given algorithm should reflect the highest conditional AUC among all candidate thresholds on an ROC curve. The performance of the algorithm under this “flare score threshold” was then judged by its sensitivity and specificity.

Similar to algorithms derived by multinomial logistic regression, CART-based criteria yield “CART scores” that can be used to decide on the presence of a flare, including its severity. Different from disease course criteria derived by multinomial regression, CART-based flare algorithms result in a single discrete value for sensitivity and specificity.

Step 4: ranking of candidate flare criteria.

To support decision making when ranking the candidate flare criteria in terms of feasibility, reliability, and validity, consensus conference participants reviewed a syllabus that provided the results of the preceding Delphi surveys (6) as well as relevant published medical literature. Additionally, the results of the statistical analyses (see step 3) were available. Consensus conference participants consisted of 11 attending experienced pediatric rheumatologists from South America, North America, and Europe with substantial clinical and research experience in cSLE (HIB, CP, MWB, Andreas Reiff, DML, LBT, BAE, Angelo Ravelli, LES, CS-M, and MP). A priori, the consensus level was set at 75%, i.e., comparable or even somewhat higher than that chosen for similar studies in cSLE and SLE in the past (15–19). Using nominal group technique guided by an experienced moderator (EHG), the expert panel assessed each of the candidate flare algorithms according to 1) feasibility, i.e., practicability: can the items be measured easily?; 2) reliability, i.e., reproducibility: can the items be measured precisely?; 3) redundancy: are there 2 or more items included in the candidate criteria measuring the same aspect of the disease?; 4) face validity, i.e., credibility: are the criteria sensible?; 5) content validity, i.e., comprehensiveness: do the criteria sample all of the domains of the disease?; 6) criterion validity: do the criteria accurately (AUC) approximate the “gold standard,” i.e., the adjudicated disease course as per the 67% rule or majority rule?; 7) sensitivity and specificity: do the criteria effectively identify patients with cSLE flares and distinguish them from patients who do not have a flare of their cSLE?; and 8) discriminant validity: do the criteria detect the smallest clinically important change? (here: discriminate patients with minor flares from those with a stable disease course). Based on the above considerations, the consensus conference participants were asked to rank the candidate flare criteria from 1 (lowest) to 5 (highest validity).

The survey source data was batch processed, and open-source online survey software, Limesurvey, was used for response management and as a presentation layer (online at http://www.limesurvey.org/). All analyses were done using SAS software, version 9.2, and SYSTAT software, version 12. P values less than 0.05 were considered statistically significant.

Ethics review.

The study was approved by the institutional review boards of the participating pediatric rheumatology centers. Informed consent was obtained from all parents and, as appropriate, assent was given by the participants prior to the study procedures.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

A total of 2,996 ratings were provided by 96 pediatric rheumatologists and used for step 2. The response rate of the pediatric rheumatologists to the patient profile was 70% (47% from the US and Canada and 53% from Australia, Africa, South and Middle America, Asia, and Europe). Among the total 358 patient profiles, 352 patient profiles (98%) were rated by at least 4 raters, and thus considered “qualified” for inclusion in step 3. There were no significant differences of distribution of flares between qualified and unqualified patient profiles (Fisher's exact test, P = 0.62).

When the majority rule was applied to the 352 “qualified” patient profiles, there were 156 patient profiles representing global flares (103 minor flares, 44 moderate flares, and 9 major flares) and 196 patient profiles without cSLE flares (stable or improved cSLE). A total of 231 patient profiles (66% of 352 patient profiles) fit the criteria of the 67% rule; among them were 63 representing with flare (44 minor flares, 15 moderate flares, and 4 major flares) and 168 patient profiles without cSLE flare.

Patient profile raters from different geographic locations did not differ systematically in the disease course assignment for a given patient profile (North America versus other countries: ICC 0.88). Similarly, patient profile raters with different durations of medical experience agreed very well with respect to the interpretation of the disease course (ICC 0.9).

The cSLE flare descriptor most commonly cited by the patient profile raters as important for the assignment of the (overall) disease course included the physician global assessment of disease activity (MD-global), the summary scores of the SLEDAI or BILAG, and the P:C ratio (Table 1). The absolute baseline to followup changes of the cSLE flare descriptors per disease course are provided in Table 2. Irrespective of the data set (67% rule, majority rule), the MD-global and the scores of the BILAG and SLEDAI changed systematically and often significantly with cSLE global flares and their severity.

Table 1. Most important descriptors to determine the presence of a global childhood-onset systemic lupus erythematosus flare*
 Patient profile where descriptor was named among the most important data provided to decide about disease course, %
  • *

    VAS = visual analog scale; SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; BILAG = British Isles Lupus Assessment Group; P:C = protein:creatinine; anti-dsDNA = anti–double-stranded DNA.

  • A= 9, B = 3, C = 1, D or E = 0.

  • Random spot urine sample (urine protein [mg/dl] to urine creatinine [mg/dl]).

Physician global assessment of disease activity (VAS, where 0 = inactive disease, range 0–10)48
SLEDAI summary score (range 0–105)48
BILAG summary score (range 0–72)28
Urine P:C ratio25
Anti-dsDNA antibodies21
Complement C319
Erythrocyte sedimentation rate, mm/hour15
Complement C413
Patient global assessment of well-being (VAS, where 0 = very poor, range 0–10)12
BILAG renal domain score (range 0–9)8
Absolute lymphocyte count7
Health-related quality of life (Child Health Questionnaire physical summary score)6
European Consensus Lupus Assessment Measure (range 0–10)6
Urine dipstick of protein6
Table 2. Change of descriptors in relation to cSLE disease course*
Patient profile raters' assessment of disease courseNo.Physician global assessment of diseasePatient global assessment of well-beingUrine P:C ratioSLEDAIBILAGCHQ PhSComplement C3Complement C4ESR, mm/hour
  • *

    Values are the change in mean ± SD. cSLE = childhood-onset systemic lupus erythematosus; P:C = protein:creatinine; SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; BILAG = British Isles Lupus Assessment Group; CHQ = Child Health Questionnaire; PhS = physical summary score; ESR = erythrocyte sedimentation rate.

  • The mean is different from that of “no change/improved” (P < 0.05).

67% rule          
 No change/improved168−0.02 ± 0.110.04 ± 0.15−0.16 ± 0.11−0.36 ± 0.24−0.82 ± 0.260.05 ± 0.87−1.47 ± 1.67−0.72 ± 0.76−1.24 ± 1.24
 Global cSLE flare631.49 ± 0.18−0.48 ± 0.250.57 ± 0.184.00 ± 0.414.29 ± 0.441.28 ± 1.50−9.28 ± 2.74−2.05 ± 1.266.28 ± 1.96
Majority rule          
 No change/improved1960.11 ± 0.11−0.02 ± 0.15−0.11 ± 0.10−0.23 ± 0.26−0.73 ± 0.32−0.51 ± 0.81−0.53 ± 1.690.63 ± 1.84−0.29 ± 1.44
 Global cSLE flare1561.60 ± 0.13−0.47 ± 0.170.59 ± 0.123.81 ± 0.314.14 ± 0.370.07 ± 0.98−9.26 ± 1.92−0.72 ± 2.117.83 ± 1.58

The only cSLE flare descriptors with moderate to high correlation to each other were the patient assessments of well-being and the CHQ PhS, suggesting that these variables are complementary in the setting of cSLE flare measurement (Table 3).

Table 3. Relationship of the changes in the childhood-onset systemic lupus erythematosus flare descriptors*
Partial correlation coefficientPatient global assessment of well-beingUrine P:C ratioSLEDAIBILAGCHQ PhSComplement C3Complement C4ESR, mm/hour
  • *

    P:C = protein:creatinine; SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; BILAG = British Isles Lupus Assessment Group; CHQ = Child Health Questionnaire; PhS = physical summary score; ESR = erythrocyte sedimentation rate.

  • Correlation of 2 covariates after adjusting for other covariates.

  • Significance of partial correlation coefficient at P < 0.01.

  • §

    Significance of partial correlation coefficient at P < 0.05.

Physician global assessment of disease−0.100.070.270.32−0.04−0.13§0.190.23
Patient global assessment of well-being 0.000.03−0.020.590.05−0.05−0.04
Urine P:C ratio  0.15§−0.030.020.020.010.00
SLEDAI   0.250.02−0.18−0.080.11
BILAG    0.07−0.04−0.060.19
CHQ PhS     −0.110.050.12
Complement C3      0.240.12

Consensus conference participants concurred that flare algorithms that allowed for the inclusion of either the BILAG or the SLEDAI were most suitable for use in clinical practice and research (consensus of 91%).

Delphi respondents and consensus conference participants alike regarded complement levels and anti-dsDNA antibodies as important cSLE flare descriptors. However, none of these variables importantly improved the accuracy (AUC), sensitivity, or specificity when considered in candidate flare algorithms.

Candidate criteria considering percentage changes of the cSLE flare descriptors.

Candidate criteria based on relative changes of all or some of the cSLE flare descriptors were generated. Despite often high specificity (maximum of 94%), none of these algorithms' sensitivities exceeded 63%. The consensus conference participants refuted the usefulness of these algorithms, given their overall poor accuracy as measured by the AUC (consensus of 100%).

Candidate criteria considering absolute changes of the cSLE flare descriptors.

Candidate flare algorithms derived by multinomial regression that showed superior statistical performance (AUC) are summarized in Table 4.

Table 4. Candidate flare algorithms based on multinomial logistic regression with the best overall performance to identify patients with flare as measured by the AUC*
Candidate criterionAbsolute change of flare descriptors considered67% ruleMajority rule
AUCSensitivity, %Specificity, %AUCSensitivity, %Specificity, %
  • *

    AUC = area under the receiver operating characteristic curve; SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; P:C ratio = urine protein:creatinine ratio from random urine sample; ESR = erythrocyte sedimentation rate; MD-global = physician global assessment of disease measured on a visual analog scale (VAS; range 0–10, where 0 = inactive disease); BILAG = British Isles Lupus Assessment Group; CHQ = Child Health Questionnaire; PhS = physical summary score; patient global = patient global assessment of well-being measured on a VAS (range 0–10, where 0 = inactive disease); anti-dsDNA = anti–double-stranded DNA.

1SLEDAI, P:C ratio, ESR, MD-global0.9080890.877485
2BILAG, P:C ratio, C3, MD-global0.8983830.857385
3BILAG, P:C ratio, ESR0.8983840.847285
4SLEDAI, ESR, MD-global0.8980860.847085
5BILAG, P:C ratio, ESR, MD-global0.8975900.867785
6BILAG, P:C ratio, CHQ PhS, MD-global0.8975910.857685
7SLEDAI, P:C ratio, ESR0.8975950.836885
8BILAG, P:C ratio, CHQ PhS0.8884860.837185
9BILAG, P:C ratio, C30.8880890.856885
10BILAG, P:C ratio, patient global, MD-global0.8880810.857185
11BILAG, P:C ratio, anti-dsDNA antibodies, MD-global0.8880820.857285
12BILAG, P:C ratio, patient global0.8880870.857185
13BILAG, P:C ratio, anti-dsDNA antibodies0.8880900.847286
14BILAG, P:C ratio0.8880880.837384
15BILAG, P:C ratio, MD-global0.8874890.857185
16SLEDAI, ESR0.8870940.856585
17SLEDAI, P:C ratio, patient global, MD-global0.8779870.847685
18SLEDAI, P:C ratio, anti-dsDNA antibodies, MD-global0.8779850.837485
19SLEDAI, P:C ratio, MD-global0.8777860.837185
20SLEDAI, P:C ratio, C3, MD-global0.8777860.837185
21BILAG, P:C ratio, C4, MD-global0.8771880.847185

The highest-ranked algorithms as per the consensus conference experts, under consideration of content validation and feasibility, are shown in Table 5 (67% rule data). Of note, analysis of the majority rule data set yielded comparable thresholds.

Table 5. Highest-ranked candidate flare criteria*
RankAlgorithmsAUCFlare score threshold
  • *

    Values are the area under the receiver operating characteristic curve (AUC) considering patient profile with consensus as defined by the 67% rule unless otherwise indicated. SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; P:C ratio = urine protein:creatinine ratio from random urine sample; MD-global = physician global assessment of disease measured on a visual analog scale (range 0–10, where 0 = inactive disease); ESR = erythrocyte sedimentation rate; BILAG = British Isles Lupus Assessment Group; CART = Classification Tree Analysis; NA = not applicable.

  • Numerical values greater than or equal to the flare score signify a flare; higher scores are seen with more severe flare.

  • Algorithm considers for the change (baseline to followup) of each of the flare descriptors included.

10.5 × SLEDAI + 0.45 × P:C ratio + 0.5 × MD-global + 0.02 × ESR0.901.04
20.4 × BILAG + 0.65 × P:C ratio + 0.5 × MD-global + 0.02 × ESR0.891.15
30.4 × SLEDAI + 0.33 × P:C ratio + 0.6 × MD-global0.870.88
40.4 × BILAG + 0.55 × P:C ratio + 0.5 × MD-global0.881.26
5 (CART)3 ≤ SLEDAI OR 2 ≤ MD-global OR 0.7 < P:C ratio0.89NA
6 (CART)2 ≤ BILAG OR 2 ≤ MD-global OR 0.7 < P:C ratio0.88NA

Consensus was achieved that criteria based on CART analysis are particularly useful for daily clinical care where any arithmetic manipulations may appear prohibitive due to limited time. CART models with superior statistical performance (AUC) included changes of the MD-global, ESR, P:C ratio, and the BILAG or SLEDAI. CART-based candidate flare criteria that considered changes of the SLEDAI as a disease activity measure had high sensitivity, specificity, and accuracy (AUC) of 89%, 85%, and 0.89 (67% rule data), respectively; CART-based candidate flare criteria that included changes of the BILAG (instead of the SLEDAI) had similar sensitivity, specificity, and accuracy of 87%, 82%, and 0.88, respectively. When using the data set derived by majority rule, comparable measurement properties were noted.

Severity of flares.

The logistic models yield scores that can be used to define flare severity. More pronounced worsening of cSLE can be quantified. For the highest-ranking flare criterion (rank 1, Table 5), flare scores of >2.7 and >3.2 are associated with moderate and severe flares, respectively. For the second-ranked criterion (rank 2, Table 5), flare thresholds for moderate and severe flares are at 3 and 3.5, respectively.

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

A set of preliminary criteria to measure global flares of cSLE has been delineated using consensus formation methodology. The highest-ranking criteria allow the use of either the BILAG or the SLEDAI and are based on a flare score that can easily be determined. The need of developing internationally acceptable criteria for disease flares has become more urgent since the introduction of randomized withdrawal trials in pediatric rheumatology, where time to flare or the proportion of patients who experience a flare are used as primary efficacy measures (19). Universally accepted criteria for flare are clinically desirable, since flares have been associated with a poor prognosis of cSLE.

Because of their popularity in pediatric rheumatology, we reexamined whether algorithms considering uniform percentage changes of the cSLE flare descriptors can be used to accurately recognize cSLE flares. However, this approach did not yield criteria with sufficient sensitivity, confirming our previous research (6). As has been suggested by our earlier studies (6), flare algorithms based on CART or regression models, both approaches that account for the differential importance of changes in individual cSLE flare descriptors for recognizing cSLE flares, proved most suitable from a statistical point of view.

cSLE flare algorithms derived by multinomial regression are reminiscent of the Disease Activity Score (DAS) used in rheumatoid arthritis (20). However, the DAS score considers the ln of the ESR and square root of the number of swollen or tender joints, while the preliminary cSLE flare criteria require at most simple arithmetic maneuvers to calculate a cSLE flare score, supporting their ease of use.

Given the simplicity of CART-based criteria, they appear particularly suited for clinical care, but a potential shortcoming of CART-based criteria is the so-called “overfitting of the mathematical model,” which can make them prone to less favorable statistical performance in subsequent validation studies. This is supported by our previous work, where we employed CART analysis to explore cSLE flare criteria based solely on statistical considerations and described a CART-based algorithm with different parameters (6).

Although it is a nonspecific marker of inflammation, the ESR is included in the criteria set for cSLE flare. The ESR is considered in selected SLE disease activity indices (21), and some previous studies in adults support the association between ESR and disease flares and damage accrual in SLE (22), supporting the relevance of ESR changes in the setting of cSLE flares.

Other criteria for measuring flare have been proposed for use in adult SLE. We tested the Safety of Estrogens in Lupus Erythematosus: National Assessment flare tool using the patient profile ratings and found their sensitivity for cSLE global flares to be <50%. In contrast, the BILAG flare tool (major flare = ≥1 new A domain score or 2 new B domain scores; moderate flare = 1 new B domain score; mild flare = 1 new C score [previous domain score: D or E]) (23) appeared to be more useful (sensitivity 78%, specificity 81%) (data not shown). However, consensus conference participants and Delphi respondents alike rejected the solitary use of disease activity indices to measure cSLE flare.

We would like to stress the observation that patient profile raters from different parts of the world and those with different degrees of experience demonstrated excellent concordance (interrater agreement) in their assessment of the cSLE course, demonstrating the robustness of the preliminary criteria in different settings.

This study must be seen in the light of certain limitations. Our data sets contained a limited number of moderate flares or severe flares, making the estimation of flare severity less reliable. However, our principal goal was to develop preliminary criteria for cSLE global flares and only as a secondary goal we aimed at classifying their severity.

We chose 2 approaches to adjudicate the disease course (67% rule and majority rule) presented in the various patient profiles, which might have introduced bias. However, both approaches yielded comparable results. Additionally, we explored other selection criteria (50% rule, 75% rule) and found no systematic differences (data not shown).

Recently, response criteria for SLE considered both the SLEDAI and the BILAG (24). In exploratory analyses, we found evidence that consideration of both indices might improve the sensitivity of diagnosing minor flare without improving the overall accuracy of the highest-ranked criteria (data not shown).

The ACR has outlined a series of validation steps necessary before new criteria are to be widely used for clinical care or research (8). Among others, one step is to use data from clinical trials for developing response criteria. However, clinical trial data from interventions that impact cSLE disease activity are unavailable at present. In our study, the presence of a flare was based on the patient profile raters' perceptions of the course of cSLE rather than using data from a clinical trial. Given its prospective character and the expertise of the patient profile raters (65% with more than 10 years of pediatric rheumatology experience, mean ± SD number of cSLE patients treated per month 15 ± 18), we consider the quality of our data to be as high as that collected for clinical trials.

Besides criteria for global flare, criteria that help determine clinically relevant worsening of cSLE in specific organ systems are needed. As is clearly stated by the ACR, a single study can never suffice to adequately examine the measurement properties of a response criteria set. To confirm the accuracy of the preliminary criteria of flare, we are planning additional validation studies using other data sets and other criterion standards, such as changes in medication requirements.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Brunner 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 conception and design. Brunner, Ying, Giannini.

Acquisition of data. Brunner, Pilkington, Beresford, Reiff, Levy, Tucker, Eberhard, Ravelli, Schanberg, Saad-Magalhaes, Higgins, Onel, Singer, von Scheven, Klein-Gitelman, Punaro.

Analysis and interpretation of data. Brunner, Mina, Itert, Ying, Giannini.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

We are indebted to the members of the External Scientific Advisory Committee of this study for their advice in the study implementation, conduction, and its statistical analysis: Drs. Carol Wallace, Susan Bowyer, Vern Farewell, Rosalind Ramsey-Goldman, Nicola Ruperto, Carlos Rose, and James Witter.

Investigators (data collection): Adam Huber (IWK Health Centre and Dalhousie University, Halifax, Nova Scotia, Canada), Alan Rosenberg (Royal University Hospital, Saskatoon, Saskatchewan, Canada), Alberto Martini (University of Genoa, Genoa, Italy), Alejandra Pringe (Hospital Pedro de Elizalde, Buenos Aires, Argentina), Ali Yalcindag (Rhode Island Hospital, Providence, RI), Amita Aggarwal (SGPGI, Lucknow, India), Anders Fasth (Göteborg University, Gothenburg, Sweden), Andrew Lasky (Children's Mercy Hospital, Kansas City, KS), Andrew Zeft (University of Utah, Salt Lake City, UT), Ann Reed (Mayo Clinic, Rochester, MN), Anne Stevens (Seattle Children's Hospital, Seattle, WA), AnneMarie Brescia (DuPont Hospital for Children, Wilmington, DE), Annet Van Royen (Wilhelmina Children's Hospital, Utrecht, The Netherlands), Basil Fathalla (Detroit Medical Center, Detroit, MI), Berit Flatø (Riks Hospital et University Hospital, Oslo, Norway), Blanca Elena Rios Gomes Bica (Federal University of Rio de Janeiro, Rio de Janeiro, Brazil), Brigitte Bader-Meunier (Hôpital Robert Debré, Paris, France), Carmen Laura De Cunto (Hospital Italiano, Buenos Aires, Argentina), Carol Wallace (Children's Hospital & Regional Medical Center, Seattle, WA), Carolina Duarte Salazar (Instituto Nacional de Rehabilitación, Mexico City, Mexico), Cassia M. P. L. Barbosa (Universidade Federal de São Paulo, Sao Paulo, Brazil), Cecilia Coto Hermosilla (Hospital Pedro Borras Astorga, Havana, Cuba), Christian Hümer (Abteilung fur Pädiatrie, Vorarlberg, Austria), Christina Boros (University of Adelaide, Adelaide, South Australia, Australia), Claire LeBlanc (University of Alberta, Edmonton, Alberta, Canada), Claudio A. Len (Universidade Federal de São Paulo, Sao Paulo, Brazil), Clovis Artur Almeida da Silva (University of Sao Paulo, Sao Paulo, Brazil), Consuelo Modesto (Hospital Vall d'Hebron, Barcelona, Spain), Deborah McCurdy (Mattel Children, Los Angeles, CA), Daniel Kingsbury (Legacy Emanuel Children's Hospital, Portland, OR), Daniel Lovell (Children's Hospital of Cincinnati, Cincinnati, OH), David Cabral (British Columbia Children's Hospital, Vancouver, British Columbia, Canada), David Sherry (Children's Hospital of Philadelphia, Philadelphia, PA), Deborah Rothman (Shriner's Hospital for Children, Springfield, MA), Delfor Alberto Giacomone (Hospital de Niños Sor María Ludovica, La Plata, Argentina), Diana Milojevic (University of California, San Francisco, CA), Dorothee Stichweh (Children's Medical Center, Dallas, TX), Dowain Wright (Children's Hospital Central California, Madera, CA), Dolores Teresa Cantera Oceguera (Hospital Pedro Borras Astorga, Havana, Cuba), Edsel Arce (Children's Hospital Central California, Madera, CA), Elizabeth Chalom (Saint Barnabas Medical Center, Livingston, NJ), Emilia Spangenberg (Sociedad Uruguaya de Reumatología, Montevideo, Uruguay), Esi Morgan DeWitt (Children's Hospital of Cincinnati, Cincinnati, OH), Eyal Muscal (Baylor College of Medicine, Houston, TX), Frank Dressler (Medizinische Hochschule Hannover, Hannover, Germany), Gaelle Chedeville (Montreal Children's Hospital, Montreal, Quebec, Canada), Gail Faller (Chris Hani Baragwanath Hospital, Johannesburg, South Africa), Gary Sterba (Hospital J. M. de los Rios, Caracas, Venezuela), Gerd Horneff (Asklepios Klinik für Kinder, Sankt Augustin, Germany), Giovany Beltrán Avendaño (Bogotá, Colombia), Graciela Espada (Hospital de Niños Ricardo Gutierrez, Buenos Aires, Argentina), Harry L. Gewanter (Pediatric & Adolescent Health Center, Midlothian, VA), Hartmut Michels (Mengel E. Rheumatic Children's Hospital, Garmisch-Partenkirchen, Germany), Hermann Girschick (University of Würzburg, Würzburg, Germany), Irama Maldonado (Centro Nacional de Enfermedades Reumáticas, Caracas, Venezuela), Ivan Foeldvari (Kinder- & Jugendrheumatologie, Hamburg, Germany), Jaime de Inocencio (Centro de Salud Jazmín, Madrid, Spain), Janis Dionne (British Columbia Children's Hospital, Vancouver, British Columbia, Canada), Jelena Vojinovic (University of Niš, Niš, Serbia), Jennifer Huggins (Children's Hospital of Cincinnati, Cincinnati, OH), Jennifer Weiss (Joseph M. Sanzari Children's Hospital, Hackensack, NJ), Jenny Soep (The Children's Hospital, Aurora, CO), Jim Jarvis (Oklahoma University Health Sciences Center, Oklahoma City, OK), Jing-Long Huang (Chang Gung Children's Hospital, Taoyuang Hsien, Taiwan), Johannes Roth (University Hospital Munich, Munich, Germany), Jordi Antón (Hospital Sant. Joan de Déu, Barcelona, Spain), Jose Goldenberg (Hospital Israelita, Sao Paulo, Brazil), Joyce Hsu (Lucile Packard Children's Hospital, Palo Alto, CA), Judy Olson (Medical College of Wisconsin, Milwaukee, WI), Juliana Sato (Istituto G. Gaslini, Genoa, Italy), Kathleen A. Haines (New York University, New York, NY), Kathleen O'Neil (Children's Hospital of Oklahoma, Oklahoma City, OK), Kelly Rouster Stevens (Wake Forest University Baptist Medical Center, Winston-Salem, NC), Ken Schikler (University of Louisville, Louisville, KY), Isabelle Kone-Paut (Hôpital de Bicêtre, Le Kremlin Bicêtre, France), L. Nandini Moorthy (Robert Wood Johnson Medical School, New Brunswick, NJ), Lawrence Jung (Creighton School of Medicine, Omaha, NE), Lenore Buckley (Virginia Commonwealth University School of Medicine, Richmond, VA), Leonard H. Sigal (Robert Wood Johnson Medical Group, New Brunswick, NJ), Leslie Abramson (Vermont Children's Hospital, Burlington, VT), Linda Wagner-Weiner (University of Chicago, Chicago, IL), Liora Harel (Schneider Children's Medical Center, Petah Tikva, Israel), Lisa Rider (National Institutes of Health, Bethesda, MD), Manuel Ferrandiz (Instituto de Salud del Niño, Lima, Peru), Mara Becker (The Children's Mercy Hospital, Kansas City, MO), María Martha Katsicas (Hospital de Pediatría “Prof. Dr. Juan P. Garrahan,” Buenos Aires, Argentina), Maria Odete Esteves Hilario (Universidade Federal de São Paulo, Sao Paulo, Brazil), Maria Teresa Apaz (Universidad Católica Clinica Reina Fabiola, Cordoba, Argentina), Maria Teresa Terreri (Universidade Federal de São Paulo, Sao Paulo, Brazil), Mario J. Moreno (Hurtado 202 y Machala Edificio Crespo 1er, Ceunca, Ecuador), Mathew Adams (Children's Hospital of Michigan, Detroit, MI), Matthew Stoll (Children's Medical Center - Dallas, Dallas, TX), Mauricio Alegria Mendoza (Colonia Médica, San Salvadore, El Salvador), Melissa Elder (University of Florida, Gainesville, FL), Michael Henrickson (Children's Hospital of Cincinnati, Cincinnati, OH), Michael S. Borzy (Ohio State University, Columbus, OH), Monica Patricia Velasquez Mendez (Universidad Nacional De Colombia, Bogotá, Columbia), Nico Wulffraat (UMC Utrecht, Utrecht, The Netherlands), Nicola Ruperto (University of Genoa, Genoa, Italy), Norm Ilowite (Children's Hospital at Montefiore, New York, NY), Patricia Woo (Great Ormond Street Children Hospital, London, UK), Pavla Dolezalova (Charles University in Prague, Prague, Czech Republic), Peter Blier (Baystate Children's Hospital, Springfield, MA), Peter Chira (Stanford University, Palo Alto, CA), Pilar Guarnizo (Universidad del Rosario Bogotá, Bogotá, Colombia), Polly Ferguson (University of Iowa, Iowa City, IA), Prieur Anne-Marie (Hôpital Necker-Enfants-Malades, Paris, France), Hans-Iko Huppertz (Hess-Kinderklinik, Bremen, Germany), Pierre Quartier (Hôpital Necker-Enfants Malades, Paris, France), Raju Khubchandani (Jaslok Hospital & Research Center, Mumbai, India), Randy Cron (University of Alabama, Birmingham, AL), Raphael Hirsch (Children's Hospital Pittsburgh, Pittsburgh, PA), Raúl Gutiérrez Suárez (Hospital General de México, Mexico City, Mexico), Ricardo Russo (Hospital de Pediatría “Prof. Dr. Juan P. Garrahan,” Buenos Aires, Argentina), Richard Vehe (University of Minnesota, Minneapolis-Saint Paul, MN), Richard Vesely (Detska Fakultna Nemocnica, Košice, Slovakia), Rik Joos (Jan Palfijn Ziekenhuis, Merksem, Belgium), Rita Jerath (Medical College of Georgia, Augusta, GA), Riva Brik (Rambam Medical Center, Haifa, Israel), Rob Nickeson (University of South Florida, Tampa, FL), Robert Sundel (Boston Children's Hospital, Boston, MA), Roberto Carreño Manjarrez (Hospital Infantil De Mexico, Mexico City, Mexico), Rolando Cimaz (AOU Meyer and University of Florence, Florence, Italy), Rosario Jurado (Sanitorio Americano, Montevideo, Uruguay), Rotraud K. Saurenmann (University Children's Hospital, Zurich, Switzerland), Ruben Burgos-Vargas (Hospital General de México, Mexico City, Mexico), Ruben Cuttica (Hospital General de Ninos Pedro de Elizalde, Buenos Aires, Argentina), Ruth Eraso (Universidad De Antioquia, Medellín, Colombia), Sheila Knupp Feitosa de Oliveira (Instituto de Puericultura e Pediatria Martagão Gesteira, Rio de Janeiro, Brazil), Shirley Tse (The Hospital for Sick Children, Toronto, Ontario, Canada), Silvia Magni-Manzoni (Fondazione IRCCS Policlinico San Matteo, Pavia, Italy), Silvia Meiorin (Hospital de Niños Ricardo Gutierrez, Buenos Aires, Argentina), Stacy Ardoin (Ohio State University, Columbus, OH), Stefan Hagelberg (Karolinska University Hospital, Stockholm, Sweden), Stella M. Garay (Hospital Sor Maria Ludovica, La Plata, Argentina), Susa Benseler (The Hospital for Sick Children, Toronto, Ontario, Canada), Susan Nielsen (Juliane Marie Centret, RigsHosp.et, Copenhagen, Denmark), Tadej Avcin (University Medical Center, Ljubljana, Slovenia), Terry L. Moore (St. Louis University School of Medicine, St. Louis, MO), Thomas Griffin (Children's Hospital of Cincinnati, Cincinnati, OH), Tim Beukelman (University of Alabama, Birmingham, AL), Tracy Ting (Children's Hospital of Cincinnati, Cincinnati, OH), Witske Kuis (Wilhelmina Children's Hospital, Utrecht, The Netherlands), Wineke Armbrust (University Medical Center Groningen, Groningen, The Netherlands), Yosef Uziel (Sapir Medical Center, Kfar Saba, Israel), Yuki Kimura (Hackensack University Medical Center, Hackensack, NJ).

Other: Cincinnati Children's Hospital Medical Center (CCHMC): Shannen Nelson (overall study coordination), Carmela Sagcal (consensus conference support), Joshua Pendl, and Jamie Meyers-Eaton (site coordination and database management); CCHMC Biomedical Informatics (web-based data management application development); Texas Scottish Rite Hospital: Shirley Henry (site coordination); University of Chicago Comer Children's Hospital: Becky Pupluva (site coordination); Children's Memorial Hospital: Dina Blair (site coordination); British Columbia Children's Hospital: America Uribe (consensus conference support); Morgan Stanley Children's Hospital: Candido Batres (site coordination), Lisa Imundo, and Andrew Eichenfield (data collection); MetroHealth Medical Center and Case Western Reserve University: Elizabeth Brooks, Kabita Nanda (data collection).

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  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
  10. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
ACR_20507_sm_Appendix.doc709KSUPPLEMENTARY APPENDIX A

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