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Clinical and epidemiological research
Extended report
Is ASDAS better than BASDAI as a measure of disease activity in axial psoriatic arthritis?
  1. Lihi Eder1,
  2. Vinod Chandran1,
  3. Hua Shen2,
  4. Richard J Cook2,
  5. Dafna D Gladman1
  1. 1Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, Toronto, Ontario, Canada
  2. 2Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
  1. Correspondence to Dr Dafna Gladman, Centre for Prognosis Studies in the Rheumatic Diseases, University of Toronto Psoriatic Arthritis Clinic, Suite No 1E-410B, Toronto Western Hospital, 399 Bathurst Street, Toronto, Ontario, Canada M5T 2S8; dafna.gladman{at}utoronto.ca

Abstract

Objective To assess the discriminative ability and correlation of the Ankylosing Spondylitis Disease Activity Score (ASDAS) and Bath Ankylosing Spondylitis Activity Disease Activity Index (BASDAI) with disease activity in axial psoriatic arthritis (AxPsA).

Methods Patients with AxPsA were selected from a large prospective cohort study of psoriatic arthritis. Patient and physician global scores were used as constructs of disease activity. Patients were categorised into high and low disease activity states based on patient and physician global assessment scores and physician's decision to change treatment. Statistical analysis included descriptive statistics, linear and logistic regression.

Results 201 patients with AxPsA were included in the study. ASDAS and BASDAI showed good correlation with disease activity as reflected by the patient global score (correlation coefficients (r) for BASDAI 0.84, ASDAS-B 0.77, ASDAS-C 0.81, p<0.001) and the physician global score (r=0.53 for BASDAI, r=0.50 for ASDAS-B, r=0.55 for ASDAS-C, p<0.001). Both scores showed good discriminative ability between high and low disease activity states. However, there were no significant differences between areas under the curve for the models that compared ASDAS with BASDAI for each definition of disease activity state.

Conclusions In patients with AxPsA, ASDAS and BASDAI scores show similar good to moderate discriminative ability and correlation with different constructs of disease activity. ASDAS was not superior to BASDAI in its ability to discriminate between high and low disease activity states in AxPsA.

https://doi.org/10.1136/ard.2010.129726

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    Axial arthritis, including sacroiliitis and spondylitis, affects up to 40% of patients with psoriatic arthritis (PsA). Axial involvement in PsA (AxPsA) is usually milder, presents with less pain and less severe radiographic changes than ankylosing spondylitis (AS), and is often accompanied by degenerative spinal changes and mechanical back pain.1 The latter features complicate the assessment of spinal disease activity in PsA that is based on measurement tools borrowed from AS, mainly the Bath Ankylosing Spondylitis Activity Disease Activity Index (BASDAI).2 Two studies that assessed its validity in patients with PsA showed conflicting results.3 4

    The Ankylosing Spondylitis Disease Activity Score (ASDAS) is a novel measurement tool to assess disease activity in patients with AS.5 It was developed because of the limitations of BASDAI of being totally patient-derived with limited face and construct validity. ASDAS includes inflammatory markers that were aimed to increase its face validity by representing a different ‘objective’ domain of disease activity that was not included in BASDAI. ASDAS showed better correlation and higher discriminative ability than BASDAI in patients with AS.6 To date, ASDAS has not been evaluated specifically in patients with AxPsA.

    This study aimed to determine the validity of BASDAI and ASDAS as clinical tools for measurement of disease activity in AxPsA and to compare their correlation with disease activity.

    Methods

    Study population

    Adult patients with AxPsA from the University of Toronto PsA cohort were included. The patients are evaluated every 6–12 months according to a standard protocol. At each visit, symptoms, physical examination and laboratory findings are recorded. The BASDAI questionnaire has been routinely administered annually since 2007. The data are entered and stored in a computerised database. The database was searched for records of clinic visits with available BASDAI scores in patients fulfilling the CASPAR classification criteria for PsA.7

    Definition of AxPsA

    AxPsA was defined based on radiological evidence of either bilateral at least grade 2 sacroiliitis, unilateral grade 3 or 4 sacroiliitis or unilateral grade 2 sacroiliitis with either inflammatory type low back pain or limitation of spinal mobility.8 9

    Patient selection

    Only patients with evidence of AxPsA according to the above definition were included. For the purpose of this study we analysed only the first visit that had a BASDAI score and complete information required to calculate the ASDAS score for each patient.

    Assessments

    For each patient the scores for each of the six individual questions of the BASDAI were available: (1) fatigue; (2) total back pain; (3) pain and swelling of peripheral joints; (4) pain at entheseal sites; (5) severity of morning stiffness; and (6) duration of morning stiffness. Scores from questions 2, 3 and 6 were used for the calculation of ASDAS. The patients rated their disease activity at the time of the assessment (patient global score). Following the rheumatological assessment, the physician rated the patients' disease activity (physician global score). Both of these were scored on a 0–10 numerical rating scale (10 representing the most severe symptom). The physical examination included an assessment of active enthesitis, peripheral arthritis and limitation in range of motion of the spine. Treatment changes were recorded. Blood samples were analysed for C reactive protein (CRP) and erythrocyte sedimentation rate (ESR).

    Calculation of ASDAS

    Four different formulae for ASDAS were developed by the Assessment of SpondyloArthritis International Society (ASAS). Two formulae included both ESR and CRP while the rest included only one of these markers in addition to patients' reported measures. In a recent validation study, all four tested formulae correlated similarly with different constructs of disease activity in patients with AS.6 Since most clinics do not measure ESR and CRP at the same time, the formulae that contained only one inflammatory marker were chosen by the ASAS group as the preferred versions for clinical and research use in patients with AS. In this study we tested these formulae that included ESR (ASDAS B) or CRP (ASDAS C). The formulae for calculating ASDAS-C and ASDAS-B are shown in table 1.5

    View this table:
    Table 1

    Formulae for calculation of ASDAS score

    Definition of high/low disease activity states

    Three different measures were used to categorise patients into high versus low disease activity states.

    1. Physician global assessment: high disease activity state was defined as a physician global score of ≥6.

    2. Patient global assessment: high disease activity state was defined as a patient global score of ≥6.

    3. Treatment decision: patients were categorised into a high disease activity group if their treatment was intensified at that visit. This included either the addition or increase in dosage of at least one of the following medications due to active peripheral or AxPsA: non-steroidal anti-inflammatory drugs, disease-modifying anti-rheumatic drugs or anti-TNFα agents.

    Statistical analyses

    Baseline descriptive statistics were computed with continuous variables summarised by their means and SD and categorical variables summarised by their proportions. Linear regression models were used with either BASDAI or ASDAS as independent variables and disease activity (either patient or physician global score) as the dependent variable. The covariate effect was considered statistically significant if the p value from the two-sided Wald test was <0.05. The proportion of explained variation (R2) was used to compare the independent variables (BASDAI or ASDAS) for their ability to predict the variability in the dependent outcome (disease activity). A logistic regression analysis was undertaken to assess the discriminative ability of the measurement between high and low disease activity states. The area under the curve (AUC) and its associated 95% CI was used to compare the ability of the two measures to predict the status of patients (high or low disease activity) and whether an increase in the intensity of treatment was required at that visit.10

    Results

    The Toronto PsA cohort includes 1076 patients. A recent study showed that 25% of the patients in the cohort have radiographic evidence of axial involvement at their first visit and an additional 23% developed axial involvement during their follow-up. HLA-B*27 was associated with axial involvement and was detected in 30% of the patients compared with 10% of those with only peripheral involvement.11

    Two hundred and one patients who satisfied the definition of AxPsA and had records of BASDAI and ASDAS were included in the current analysis. The demographic and clinical characteristics of the patients are summarised in table 2.

    View this table:
    Table 2

    Clinical characteristics of the study population (n=201)

    Correlation with disease activity

    The correlations of BASDAI, ASDAS and inflammatory markers with disease activity, as reflected by physician and patient global scores, are shown in table 3. As expected from the fact that they are mainly patient-derived, both scores correlated well with disease activity when defined by patient global assessment (BASDAI, correlation coefficient (r)=0.84; ASDAS-B, r=0.77; ASDAS-C, r=0.81; p<0.001). The correlation of the two scores with disease activity when defined by physician global assessment was lower. However, both scores still showed similar moderate correlation with the physician global score (BASDAI, r=0.53; ASDAS-B, r=0.50; ASDAS-C, r=0.55; p<0.001). Within the individual components of the BASDAI score, the fatigue domain showed the weakest correlation with both patient and physician disease activity score.

    View this table:
    Table 3

    Correlation between patient and physician global scores and BASDAI and ASDAS scores

    CRP and ESR, which were added to the ASDAS formula as ‘objective’ measures of disease activity, showed poor correlation with patient- and physician-derived disease activity scores. While CRP showed only marginally significant and weakly positive correlation with patient or physician global scores (r=0.16, p=0.05 and r=0.17, p=0.03, respectively), ESR did not show any significant correlation with either of these scores (r=0.07, p=0.35 and r=0.09, p=0.22, respectively).

    Since ASDAS and BASDAI scores are different scales, we used the R2 statistic from the linear regression models to compare their explanatory power for disease activity. R2 represents the percentage of variability of the dependent variable (disease activity) that is explained by the independent variable (ASDAS or BASDAI scores). A higher R2 means that the score correlates better with disease activity. The comparison between ASDAS and BASDAI and disease activity is shown in table 4. As indicated by similar R2 values in the different models, there was essentially no difference in the correlation of BASDAI and either ASDAS-C or -B with disease activity defined by patient or physician global scores. Only one comparison showed an advantage for BASDAI over ASADAS-B when modelled against the patient global score, as evidenced by higher R2 (0.71 vs 0.59). Thus, as expected, BASDAI correlates better than ASDAS-B with the patient global score, being solely a patient-reported score.

    View this table:
    Table 4

    Linear regression models for predicting disease activity using BASDAI or ASDAS

    Discrimination between high and low disease activity states

    Disease activity scores such as BASDAI are often used to discriminate between high and low disease activity states, especially for treatment decisions. We therefore used a logistic regression analysis to compare the ability of BASDAI and ASDAS to discriminate between high and low disease activity states. Change of treatment and physician and patient global scores were used to define high versus low disease activity states.

    The results of the logistic regression analysis are shown in table 5. Owing to differences in scales, we used the AUC to assess the logistic regression models (ie, to compare the discriminative ability of ASDAS and BASDAI). Both scores showed good discriminative ability between high and low disease activity states as evidenced by a significant association of each score with high versus low disease activity and a relatively high AUC. As expected, the scores showed better discriminative ability when the definition of the disease activity state was based on patients (95% CI for AUC: BASDAI 0.88 to 0.95, ASDAS-B 0.85 to 0.94, ASDAS-C 0.86 to 0.96) rather than physician-derived scores (95% CI for AUC: BASDAI 0.67 to 0.88, ASDAS-B 0.71 to 0.91, ASDAS-C 0.64 to 0.91) and change of treatment (95% for AUC: BASDAI 0.63 to 0.76, ASDAS-B 0.61 to 0.76, ASDAS-C 0.61 to 0.77). However, there were no significant differences between AUC scores for the models that compared ASDAS with BASDAI for each individual definition of disease activity state.

    View this table:
    Table 5

    Logistic regression models for predicting high and low disease activity states and need for increase in treatment intensity using BASDAI or ASDAS

    AUC remained similar even after the definition for the change in treatment domain was changed to include only change in anti-TNF agents (AUC for BASDAI 0.69, ASDAS-B 0.65, ASDAS-C 0.66).

    Since most of the study population had peripheral arthritis, we performed additional analyses to determine whether BASDAI can distinguish between axial and peripheral disease activity. We calculated a mini-BASDAI score that excluded the question about the severity of joint pain. We were unable to do the same with the ASDAS score since it is a weighted score. The results of the logistic models showed similar values for the goodness of fit measure AUC (table 6). The results indicated that the mini-BASDAI score, without the peripheral joint component, correlates well with constructs of disease activity.

    View this table:
    Table 6

    Logistic regression models for predicting high and low disease activity states and need for increase in treatment intensity using mini-BASDAI*

    Discussion

    In this study we compared two scores for assessment of disease activity in patients with AxPsA—the BASDAI and the newly developed ASDAS. Both scores showed good correlation with the construct of disease activity when based on patient-reported scores and moderate correlation with physician scores and change in treatment. However, the results suggest that the ASDAS and BASDAI scores perform similarly in patients with AxPsA. These scores showed overall similar correlation and discriminative ability with measures that represent disease activity. This finding led us to conclude that the ability of ASDAS or BASDAI to discriminate between high and low disease activity states—as defined by patient and physician global scores and change in treatment—is similar.

    Few studies have assessed the validity of the BASDAI score in patients with PsA. Taylor and Harrison concluded that BASDAI correlated well with patient perception of disease activity but was unable to discriminate well between high and low disease activity states, as defined by treatment change.3 However, that study had several limitations including a low proportion of patients with axial involvement and potential misclassification of patients with axial versus peripheral disease. A recent study by Fernández-Sueiro et al concluded that BASDAI performed well in differentiating between patients with AxPsA and those without axial involvement. However, it did not assess the performance of BASDAI against a construct of disease activity.4

    It was recently shown that ASDAS performs better than BASDAI in evaluating disease activity in patients with AS. It correlated better with patient and physician disease activity scores, discriminated better between high and low disease activity states and was more sensitive to change over time.6 12 These results differ from our findings which show essentially similar discriminative ability and correlation with disease activity state in AxPsA. Our results reflect the poor correlation between the inflammatory markers ESR and CRP and disease activity. Their addition to the ASDAS score may have contributed to its face validity but did not increase its construct validity, at least for AxPsA. We have previously shown that ESR tends to be significantly higher in patients with AS than in those with AxPsA, which may explain the differences between the results of our study and those of previous studies in patients with AS.13 In our study, only one-third of the patients had elevated inflammatory markers with the majority having normal ESR and relatively low CRP. These are characteristics of patients with PsA, and are limitations of these biomarkers as markers of disease activity in PsA.14

    There are limitations in using tools that were designed for AS in patients with AxPsA. Although there are several overlapping features between these conditions, the two are distinct. Axial involvement in PsA is less severe than in AS, and most of the study patients did not suffer from inflammatory back pain as judged by the physician. Unlike AS where peripheral arthritis is often mild, this domain is much more important in PsA where most patients suffer from polyarticular involvement. In fact, patients with AxPsA have more severe peripheral arthritis.11 Therefore, in order to use the BASDAI or ASDAS for assessing disease activity in AxPsA, the weights given to individual components of the BASDAI and ASDAS may need to be modified.

    In summary, in patients with AxPsA the ASDAS and BASDAI scores show similar good to moderate discriminative ability and correlation with different constructs of disease activity. The ASDAS score did not improve its discriminative ability compared with BASDAI. Therefore, because BASDAI is easier to calculate, it may be more practical for clinical use in patients with AxPsA.

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    Footnotes

    • Funding The University of Toronto PsA program is supported by a grant from the Krembil Foundation as well as by The Arthritis Society SPARCC National Research Initiative. LE is supported by a Fellowship grant from the Canadian Arthritis Network and an Abbott PsA Fellowship. VC is supported by a Canadian Institutes of Health Research – Clinical Research Initiative Fellowship and the Krembil Foundation. RJC holds a Canada Research Chair in Statistical Methods for Health Research.

    • Competing interests None.

    • Patient consent Obtained.

    • Ethics approval This study was conducted with the approval of the University Health Network Research Ethics Board.

    • Provenance and peer review Not commissioned; externally peer reviewed.