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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. REFERENCES

Objective

Antineutrophil cytoplasmic antibody–associated vasculitis (AAV) can present with a broad spectrum of signs and symptoms. The relative effects of different manifestations on health-related quality of life (HRQOL) are unknown.

Methods

We conducted an individual patient data meta-analysis of baseline Short Form 36 (SF-36) scores from 4 randomized controlled trials of patients with newly diagnosed AAV. We determined the associations between organ manifestations at trial entry and the SF-36 physical composite score (PCS) and mental composite score (MCS) using mixed-effects models adjusted for demographic factors. Associations with each of the 8 domains of the SF-36 were further explored using multivariate multiple regression.

Results

SF-36 data were available from 346 patients. Older age (−0.11 points/year [95% confidence interval (95% CI) −0.21, −0.012]; P = 0.029) and neurologic involvement (−5.84 points; P < 0.001) at baseline were associated with lower PCS. Physical functioning scores were the most affected and older age scores (−0.25 points/year [95% CI −0.38, −0.11]; P < 0.001) and neurologic involvement (−8.48 points [95% CI −12.90, −4.06]; P < 0.001) had the largest effects. The MCS was negatively affected only by chest involvement (P = 0.027), but this effect was not exerted in any particular domain.

Conclusion

In patients with newly diagnosed AAV, HRQOL is complex and incompletely explained by their organ system manifestations.


INTRODUCTION

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

Granulomatosis with polyangiitis (Wegener's) (GPA), microscopic polyangiitis (MPA), and renal-limited vasculitis are among the most common primary systemic vasculitides in adults. They are associated with circulating antineutrophil cytoplasmic antibodies (ANCAs) and, due to similarities in clinical features, histologic characteristics, treatment, and outcomes are frequently grouped together as ANCA-associated vasculitis (AAV). Earlier recognition of AAV and the widespread use of immunosuppressive treatment have significantly reduced its mortality (1, 2). Patients with AAV are faced with a chronic medical condition and health-related quality of life (HRQOL), the component of well-being attributed directly to health status, is an increasingly important consideration.

Measuring HRQOL has been facilitated in the last 20 years by the development and validation of generic HRQOL instruments such as the Medical Outcomes Study Short Form 36 (SF-36) (3, 4). These instruments allow investigators to reliably measure several facets or domains of HRQOL in a multitude of conditions.

Despite the chronic morbidity observed in patients with AAV, there is little known about how disease manifestations affect HRQOL. Small single-center studies examining what variables influence HRQOL have suggested that lung damage, joint involvement, and sinonasal involvement have each been potentially important determinants of physical components of HRQOL in different studies (5–7). Determining which disease manifestations influence HRQOL and in what domains they affect HRQOL may help focus treatment for patients with AAV and help evaluate newer therapies. We studied the association between patient characteristics and particular manifestations of AAV and HRQOL in a multicenter cohort of patients that covered the spectrum of disease activity and manifestations.

Significance & Innovations

  • This study is one of the largest studies of the determinants of health-related quality of life (HRQOL) in patients with a broad spectrum of antineutrophil cytoplasmic antibody–associated vasculitis severity and manifestations.

  • This study is one of the first studies to demonstrate the relative associations of several different organ system manifestations, including the large effect of neurologic involvement on physical domains of HRQOL.

PATIENTS AND METHODS

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

Patients.

The European Vasculitis Study Group conducted 4 trials that enrolled patients from 70 hospitals in 15 countries between 1995 and 2002 (8–11). All of the trials were conducted according to the 1964 Declaration of Helsinki and subsequent amendments. All of the patients were newly diagnosed with AAV (either GPA, MPA, or renal-limited vasculitis). One trial enrolled patients with early systemic AAV (creatinine level <150 μmoles/liter), two enrolled patients with generalized AAV (creatinine level between 150 and 500 μmoles/liter), and one enrolled patients with severe AAV (creatinine level >500 μmoles/liter or requiring dialysis). The individual trial eligibility criteria are summarized in Table 1.

Table 1. Summary of included trial eligibility and treatment regimens*
TrialIncluded disease stageIncluded creatinine level, μmoles/literInduction treatmentMaintenance treatment
  • *

    MTX = methotrexate; CYC = cyclophosphamide; AZA = azathioprine; IV = intravenous; PE = plasma exchange; IVMP = intravenous methylprednisolone.

NORAMEarly systemic<150MTX vs. oral CYCMTX vs. oral CYC
CYCAZAREMGeneralized150–499Oral CYCOral CYC vs. AZA
CYCLOPSGeneralized150–499IV CYC vs. oral CYCAZA
MEPEXSevere>500PE + oral CYC vs. IVMP + oral CYCAZA

Measures.

HRQOL was evaluated with the SF-36 Health Survey, a generic self-reported health questionnaire administered in the patient's native language whenever possible. The SF-36 measures HRQOL in 8 domains, 4 physical (physical functioning, role physical, bodily pain, and general health) and 4 mental (social functioning, role emotional, mental health, and vitality). The score for each domain was normalized to UK population scores with a mean ± SD of 50 ± 10, with higher scores indicating better quality of life (12, 13). In addition, domains are summarized as a physical composite score (PCS) and a mental composite score (MCS), also with a population mean ± SD of 50 ± 10. A 5-point difference in scores is generally regarded as the minimum clinically important difference (MCID) (14).

Patients were assessed at baseline for manifestations of AAV in each organ system using the Birmingham Vasculitis Activity Score (BVAS), an instrument with 9 domains (general; cutaneous; mucous membrane/eye; ear, nose, throat [ENT]; chest; cardiovascular; abdominal; renal; and nervous system) (15). Each BVAS item was scored if the sign or symptom started or worsened over the 4 weeks prior to the evaluation. The BVAS produces a summary score for overall disease activity that can range from 0 to 63. The summary score is composed of the sum of each organ domain–specific score. For the purpose of this analysis, each organ domain was classified as actively involved or not on the basis of ≥1 item or no items present at baseline. Serum creatinine was measured at baseline and converted to an estimated glomerular filtration rate (GFR) using the 4-variable Modification of Diet in Renal Disease equation (16). AAV was subgrouped as GPA or MPA (including renal-limited vasculitis) according to the Chapel Hill Consensus Statement (17).

Statistical analyses.

Summary data are shown as the mean ± SD or median (interquartile range [IQR]) as appropriate for normal or non–normally distributed continuous variables, respectively. Baseline characteristics between those included and those excluded for analysis were compared by the Student's t-test for normally distributed continuous variables, Mann-Whitney test for non–normally distributed continuous variables, and Fisher's exact test for dichotomous data.

Associations between baseline characteristics and the PCS and MCS were determined using mixed-effects linear regression in which each trial served as a random effect. Identical models were fit for PCS and MCS data. Each model included age, sex, estimated GFR, diagnosis (GPA versus MPA or renal-limited vasculitis), and organ system involvement for each of the 9 BVAS organ systems. To explore whether each baseline variable was associated with certain physical or mental domains, we assessed all physical domains simultaneously in a multivariate regression model and all mental domains in a second model. Predictor variables in the multivariate regression models were specified in the same way as the multilevel models but without a random effect for trial. Models for physical functioning, role physical, bodily pain, and general health scores were simultaneously fit for physical domains and models for social functioning, mental health, role emotional, and vitality scores were simultaneously fit for mental domains. Missing predictor covariate data were imputed using chained equation multiple imputation techniques (18, 19). Ten imputation data sets were used to generate all final analyses. Sensitivity analyses using only complete cases were also conducted. Sensitivity analyses in which pulmonary hemorrhage (defined as hemoptysis or respiratory failure attributed to active vasculitis) was coded separate from other chest manifestations were also conducted to ensure that estimates for chest involvement were not driven solely by pulmonary hemorrhage. Further sensitivity analyses that included the summary BVAS as a measure of overall disease activity and excluded individual organ involvement variables were conducted. A P value less than 0.05 was considered statistically significant, with no corrections for multiple comparisons for the PCS and MCS models. In multivariate models, Type I errors due to multiple comparisons were contained by adjusting the significance level by the number of covariates in the model (i.e., adjusted P values less than 0.004 for significance) in the multivariate models. A point estimate of at least 5 points was required to be considered clinically significant. All analyses were performed on Stata, version 11.

RESULTS

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

Patient data.

A total of 535 patients were enrolled in the 4 trials. Of these, 346 (65%) had baseline SF-36 data for analysis. Eighty-four percent of NORAM patients, 72% of CYCAZAREM patients, 51% of CYCLOPS patients, and 57% of MEPEX patients completed baseline SF-36 evaluations. Patients with SF-36 data more frequently had GPA and general ENT manifestations, better renal function, and lower BVAS, and less frequently had renal manifestations compared to those who did not have SF-36 data (Table 2). Eighty-four percent of patients with SF-36 data had complete covariate data available for analysis; in the remaining 16%, at least one predictor variable was multiply imputed.

Table 2. Characteristics of patients included and excluded from this study*
 Included (n = 346)Excluded (n = 189)P
  • *

    GPA = granulomatosis with polyangiitis (Wegener's); BVAS = Birmingham Vasculitis Activity Score; GFR = glomerular filtration rate; IQR = interquartile range; ENT = ear, nose, throat.

  • P values from t-tests for continuous variables or Fisher's exact test for categorical variables, except where noted.

  • P value from Mann-Whitney test.

Age, mean ± SD years57.1 ± 13.958.4 ± 14.90.39
Women, %43.950.20.15
GPA, %58.541.6< 0.001
Baseline BVAS, mean ± SD17.6 ± 8.519.2 ± 8.40.041
Estimated GFR, median (IQR) ml/minute33.5 (10.9–70.0)18.9 (7.6–51.9)< 0.001
Organ involvement, %   
 General91.987.10.009
 Cutaneous23.523.90.91
 Mucous membrane/eye30.426.40.22
 ENT52.946.00.036
 Chest52.546.60.071
 Cardiac5.74.90.69
 Abdominal4.76.70.18
 Renal86.892.30.007
 Neurologic20.120.80.81

Distribution of SF-36 scores.

Figure 1 demonstrates the distribution of SF-36 scores for all patients. For the PCS, the mean ± SD was 27.6 ± 12.5 and the median was 26.7 (IQR 18.6–36.1). The mean ± SD MCS was 40.4 ± 11.9 and the median was 38.9 (IQR 30.9–50.5). Both the PCS and MCS were significantly lower than the population norm of 50 (P < 0.001 for both the PCS and MCS compared to population norms). Of the physical domains, physical functioning and role physical scores were the lowest, with medians of 28.6 (IQR 14.7–42.5) and 21.3 (IQR 21.3–29.7), respectively. Among the mental domains, social functioning scores were the lowest, with a median of 30.6 (IQR 17.8–43.4).

thumbnail image

Figure 1. Distribution of Short Form 36 scores in patients with antineutrophil cytoplasmic antibody–associated vasculitis. Population average is 50 (horizontal line). Boxes show the 25th to 75th percentiles with the median (embedded horizontal line). Whiskers show the 5th to 95th percentiles and solid circles show the outliers. PCS = physical composite score; PF = physical functioning; RP = role physical; BP = bodily pain; GH = general health; MCS = mental composite score; SF = social functioning; MH = mental health; RE = role emotional; VT = vitality.

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Associations with the PCS and MCS.

Older age was independently associated with a lower PCS (P = 0.029), although a 45-year age difference was required to reach the MCID (0.11 points per year of age). Neurologic activity was the only organ system independently associated with a statistically (P < 0.001) and clinically significant (−5.84 points; 95% confidence interval [95% CI] −9.09, −2.60 points) reduction in PCS (Table 3). Chest involvement was associated with a statistically (P = 0.027) but not clinically significant (−2.96 points; 95% CI −5.58, −0.33 points) reduction in MCS (Table 3). No other factors were associated with a significant reduction in MCS. Sensitivity analyses using only complete cases did not differ materially from analyses utilizing multiple imputations. Similarly, sensitivity analyses in which pulmonary hemorrhage was considered separately from other chest manifestations did not differ materially from primary analyses, and estimates for the effect of pulmonary hemorrhage were similar to the estimates for other chest manifestations.

Table 3. Mixed-effects multivariable regression models for PCS and MCS of the Short Form 36 questionnaire*
 PCSMCS
β (95% CI)Pβ (95% CI)P
  • *

    PCS = physical composite score; MCS = mental composite score; 95% CI = 95% confidence interval; MPA = microscopic polyangiitis; GFR = glomerular filtration rate; ENT = ear, nose, throat.

Age (per year)−0.11 (−0.21, −0.012)0.0290.036 (−0.066, 0.14)0.49
Sex−2.38 (−4.98, 0.21)0.072−2.32 (−4.88, 0.24)0.076
Diagnosis (MPA)0.68 (−2.85, 4.22)0.712.34 (−1.19, 5.87)0.19
Estimated GFR (per 10 ml/minute)0.058 (−0.48, 0.59)0.830.38 (−0.11, 0.87)0.13
Organ involvement    
 Systemic−4.83 (−11.08, 1.41)0.13−4.98 (−11.23, 1.26)0.12
 Cutaneous−2.42 (−5.51, 0.66)0.122.22 (−0.87, 5.32)0.16
 Mucous membrane/eye−2.50 (−5.44, 0.45)0.096−0.48 (−3.45, 2.48)0.75
 ENT−1.79 (−5.04, 1.46)0.282.97 (−0.27, 6.22)0.072
 Chest−2.26 (−4.86, 0.35)0.089−2.96 (−5.58, −0.33)0.027
 Cardiac0.82 (−5.20, 6.84)0.79−0.97 (−7.08, 5.12)0.75
 Abdominal1.69 (−4.86, 8.25)0.615.20 (−1.45, 11.84)0.13
 Renal−2.85 (−7.18, 1.48)0.200.63 (−3.21, 4.48)0.75
 Neurologic−5.84 (−9.09, −2.60)< 0.0010.076 (−3.22, 3.37)0.96

Although few individual organ systems are associated with clinically and statistically significant differences in the PCS and MCS, their combined effects may result in clinically significant differences, particularly considering most patients have several organ systems involved at the time of diagnosis (median 4 systems). The estimated reduction in PCS for an individual with the 4 most common organ manifestations (systemic, renal, ENT, and chest) of 22 points (when considering age) is similar to the observed 22.6-point reduction in the PCS in our study compared to the normal population. Sensitivity analyses that included BVAS score as an overall measure of disease severity did not show any independent association between the BVAS and either the PCS or MCS. Therefore, for most patients with newly diagnosed AAV, overall HRQOL may largely be a function of having active disease rather than a function of activity in particular organs or severity of activity.

Associations with individual domain scores.

The results of multivariate regression to explore the association of baseline characteristics with each domain of the SF-36 are summarized in Table 4 (physical domains) and Table 5 (mental domains). A P value of <0.004 was required for statistical significance in the joint multivariate models to reduce the Type I error rate. Older age was associated with lower physical functioning (P < 0.001). An age difference of 20 years was required to reach the MCID of 5 points. Female sex and renal function demonstrated trends toward effects in several domains, but none of these met our significance threshold. Furthermore, the point estimates for the effect of female sex did not meet the MCID and for renal function, the difference in estimated GFR required to meet the MCID was approximately 80 ml/minute (i.e., the MCID was only met if comparing patients requiring dialysis to those with near normal renal function). There was no difference between those patients with GPA and those with MPA in any domain of the SF-36.

Table 4. Multivariate model of association of patient characteristics with physical domains of the Short Form 36 questionnaire*
 Physical functioningRole physicalBodily painGeneral health
β (95% CI)Pβ (95% CI)Pβ (95% CI)Pβ (95% CI)P
  • *

    95% CI = 95% confidence interval; MPA = microscopic polyangiitis; GFR = glomerular filtration rate; ENT = ear, nose, throat.

Age−0.25 (−0.38, −0.11)< 0.001−0.10 (−0.19, −0.01)0.0270.061 (−0.07, 0.19)0.36−0.42 (−0.13, 0.045)0.34
Female sex−4.46 (−7.84, −1.08)0.010−1.42 (−3.71, 0.87)0.220.008 (−3.27, 3.29)0.99−2.03 (−4.21, 0.16)0.069
MPA0.61 (−4.03, 5.26)0.801.61 (−1.52, 4.75)0.31−0.20 (−4.70, 4.29)0.932.15 (−0.84, 5.14)0.16
Estimated GFR (per 10 ml/minute)0.64 (0.0024, 1.29)0.0490.17 (−0.26, 0.61)0.43−0.04 (−0.67, 0.58)0.900.25 (−0.18, 0.67)0.25
Organ involvement        
 General−5.32 (−4.72, 3.66)0.21−4.90 (−10.47, 0.68)0.085−3.48 (−11.49, 4.54)0.39−6.50 (−12.07, −0.93)0.022
 Cutaneous−0.53 (−4.72, 3.66)0.80−0.35 (−3.10, 2.40)0.80−3.02 (−7.03, 0.99)0.140.78 (−1.99, 3.53)0.58
 Mucous membrane/eye−2.40 (−6.37, 1.56)0.23−0.42 (−3.05, 2.21)0.76−3.55 (−7.35, 0.25)0.067−0.12 (−2.65, 2.40)0.92
 ENT−1.03 (−5.34, 3.26)0.64−0.29 (−3.19, 2.61)0.84−1.16 (−5.40, 3.08)0.591.29 (−1.46, 4.04)0.36
 Chest−3.46 (−6.89, −0.04)0.047−2.13 (−4.44, 0.17)0.070−2.27 (−5.60, 1.05)0.18−1.61 (−3.90, 0.66)0.16
 Cardiac−3.71 (−12.28, 4.86)0.39−2.37 (−7.70, 2.96)0.380.59 (−7.00, 8.18)0.884.96 (−0.51, 10.42)0.075
 Abdominal6.31 (−2.61, 15.23)0.173.81 (−2.11, 9.73)0.21−3.69 (−12.46, 5.08)0.413.27 (−2.55, 9.10)0.27
 Renal−3.64 (−8.69, 1.41)0.16−2.04 (−5.46, 1.37)0.24−0.42 (−5.36, 4.52)0.87−3.36 (−7.15, 0.43)0.081
 Nervous−8.48 (−12.90, −4.06)< 0.001−2.27 (−5.18, 0.64)0.13−4.98 (−9.14, −0.81)0.019−2.45 (−5.26, 0.36)0.088
Table 5. Multivariate model of association of patient characteristics with mental domains of the Short Form 36 questionnaire*
 VitalitySocial functioningRole emotionalMental health
β (95% CI)Pβ (95% CI)Pβ (95% CI)Pβ (95% CI)P
  • *

    95% CI = 95% confidence interval; MPA = microscopic polyangiitis; GFR = glomerular filtration rate; ENT = ear, nose, throat.

Age0.0028 (−0.10, 0.11)0.960.009 (−0.13, 0.14)0.90−0.032 (−0.15, 0.087)0.60−0.014 (−0.13, 0.11)0.81
Female sex−2.50 (−5.14, 0.13)0.062−1.75 (−5.12, 1.61)0.31−2.01 (−4.98, 0.97)0.19−3.28 (−6.29, −0.27)0.032
MPA−0.44 (−4.05, 3.16)0.810.35 (−4.30, 5.01)0.882.17 (−1.95, 6.29)0.303.84 (−0.29, 7.98)0.068
Estimated GFR (per 10 ml/minute)0.60 (0.10, 1.11)0.0180.56 (−0.084, 1.20)0.0880.32 (−0.24, 0.89)0.260.24 (−0.33, 0.83)0.41
Organ involvement        
 General−3.73 (−10.12, 2.66)0.25−6.66 (−15.14, 1.81)0.12−7.24 (−14.55, 0.07)0.052−3.34 (−10.73, 4.06)0.38
 Cutaneous0.33 (−2.84, 3.51)0.84−2.11 (−6.28, 2.06)0.322.59 (−1.05, 6.25)0.162.20 (−1.46, 5.87)0.24
 Mucous membrane/eye−1.78 (−4.81, 1.24)0.25−3.31 (−7.30, −0.67)0.10−0.52 (−3.97, 2.93)0.77−0.20 (−3.72, 3.32)0.91
 ENT−0.94 (−4.29, 2.40)0.58−0.14 (−4.46, 4.17)0.952.80 (−0.97, 6.58)0.153.89 (0.045, 7.74)0.047
 Chest−3.02 (−5.70, −0.34)0.027−3.47 (−6.94, 0.0003)0.050−3.32 (−6.35, −0.29)0.032−2.78 (−5.88, 0.32)0.078
 Cardiac1.08 (−5.32, 7.47)0.743.29 (−4.74, 11.32)0.42−3.93 (−11.03, 3.17)0.28−3.38 (−10.87, 4.11)0.37
 Abdominal6.74 (−0.11, 13.59)0.0544.09 (−5.20, 13.39)0.395.94 (−1.78, 13.66)0.132.71 (−5.07, 10.48)0.49
 Renal0.53 (−3.36, 4.42)0.79−1.82 (−6.85, 3.19)0.48−0.98 (−5.46, 3.48)0.670.79 (−3.96, 5.53)0.74
 Nervous−0.95 (−4.32, 2.41)0.58−0.94 (−5.23, 3.34)0.67−1.91 (−5.70, 1.88)0.32−2.81 (−6.70, 1.08)0.16

In terms of organ involvement, general manifestations of AAV resulted in lower general health scores (−6.50 points; 95% CI −12.07, −0.93 points), but this did not meet the modified threshold for statistical significance (P = 0.022). Neurologic activity was associated with statistically and clinically significant lower physical functioning scores (−8.48 points [95% CI −12.90, −4.06 points]; P < 0.001), and there was a nonsignificant trend toward lower bodily pain scores (−4.98 points [95% CI −9.14, −0.81 points]; P = 0.019). Other organ manifestations were not associated with differences in HRQOL scores.

DISCUSSION

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

HRQOL is increasingly important to consider in the care of patients with AAV. Despite this, there are few studies demonstrating what features of AAV are important determinants of HRQOL. We have demonstrated in a large cohort that includes the full spectrum of severity of AAV that HRQOL, particularly in physical domains, is significantly reduced at the time of diagnosis. Neurologic manifestations of AAV affect HRQOL most dramatically, suggesting they may be an important therapeutic target to improve HRQOL.

HRQOL was substantially lower in our AAV patients than population norms. SF-36 scores in our patients also appeared lower than some other recent studies of HRQOL in patients with AAV (20). However, our study included only patients at the time of diagnosis, while others typically measured HRQOL in a mixture of patients with active and inactive disease. The finding that physical domains of HRQOL were more affected than mental domains is also similar to other studies, as was the lack of association between HRQOL and renal function or diagnosis (21). Unique to our study, however, is the assessment of each organ system involvement and the finding that neurologic activity most strongly affects HRQOL.

In our study, a finding consistent with others is that role physical scores were the lowest, suggesting treatments that affect this domain will be of greatest value for improving HRQOL in patients with AAV (22). Also, we found a possible association between neurologic manifestations and bodily pain that may have been an important determinant in other studies demonstrating that neuropathic pain was a significant source of reduced HRQOL. However, unlike other reports, we did not find ENT activity associated with clinically significant reductions in any domain of HRQOL (5, 7). In fact, those with ENT involvement appeared to have slightly better mental health scores compared to those without ENT involvement, although this may well be a spurious finding. The discrepancy between ours and other studies may be due to the fact that ours were newly diagnosed patients with active disease manifestations due to AAV as assessed by a physician at diagnosis. Other studies examined the associations between disease damage and persistent symptoms in patients with a preexisting diagnosis of AAV (7, 23). It is possible that persistent symptoms that may be due to organ scarring or active disease have a greater impact on a patient's HRQOL in the absence of acute illness and additional acute disease manifestations. This is consistent with the finding that chronic disease damage is associated with lower SF-36 scores in several domains (23, 24).

Few organ manifestations were found to have a clinically and statistically significant association with reduced HRQOL in our study, despite the finding that overall HRQOL was very impaired. This could be due to a relatively small contribution to reduced HRQOL from individual organ manifestations. Most patients present with a constellation of organ manifestations when diagnosed with AAV that together may result in significantly impaired HRQOL. Consistent with this was the observation that summing the contribution of the most common manifestations and adjusting for the average age of our patients suggests a similar reduction in PCS as was observed for the overall group. Alternatively, it is possible that generic HRQOL instruments are insensitive to the effects of many manifestations of AAV on HRQOL. Therefore, instruments like the SF-36 may be suitable to quantify overall changes in HRQOL but insufficiently sensitive to capture changes in specific areas of HRQOL that result from the treatment of very particular AAV symptoms. For example, although SF-36 may adequately quantify changes in HRQOL as patients transition from acute disease to complete remission, it may be inadequate to quantify the impact of treating ENT symptoms on HRQOL. Studies that seek to measure improvement in specific areas of HRQOL may therefore be best served by using domain-/symptom-specific HRQOL instruments in addition to generic instruments as is recommended in other diseases (25).

Our study has several notable strengths. It is, to our knowledge, the largest study of HRQOL in patients with AAV and it covers the full spectrum of disease severity. As such, we are able to make more precise estimates of the effects of disease manifestations on HRQOL that are relevant to a broader scope of patients than previous studies. Further, all patients are newly diagnosed, therefore limiting confounding by duration of disease that may occur in cross-sectional studies. Finally, the use of a generic instrument allows us to compare the HRQOL of our patients with patients with AAV in other studies and to patients with other diseases.

Our study must also be interpreted within the context of its limitations. A substantial number of patients did not complete the SF-36, and these patients tended to have more severe disease and be older than those who did complete the questionnaire. It is difficult to predict how this responder bias affects our results. However, it seems likely that the patients who did not complete the questionnaire were the most ill with the most severe manifestations of AAV and the lowest HRQOL. Their exclusion would likely result in an underestimation of the effect of severe manifestations of AAV such as neurologic manifestations and severe renal disease. Our sample is also taken from randomized controlled trials, which may limit how representative our patients are compared to a true inception cohort of patients with AAV. However, this limitation is unlikely to have affected the generalizability of the effect estimates of organ manifestations on HRQOL (i.e., the relative effects of a given organ manifestation on HRQOL are unlikely to differ substantially between trial and nontrial patients). Lastly, although patients were newly diagnosed, for some cases, disease activity had been present for some months prior to diagnosis, and some disease manifestations may have caused damage and then become quiescent prior to diagnosis. These potentially confounding effects were not assessed in this study.

In conclusion, patients with AAV have significantly reduced HRQOL at the time of the initial diagnosis. Neurologic involvement appears to be an important determinant of HRQOL and may be an important target for treatment and future research. However, additional research is required to determine which aspects of AAV determine HRQOL in order to help direct future research and clinical care for these patients. Finally, our study highlights the need to evaluate HRQOL in clinical trials in AAV because the information it conveys is not encompassed by other, more traditional, vasculitis-specific outcome measures.

AUTHOR CONTRIBUTIONS

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

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. Walsh 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. Walsh, Mukhtyar, Mahr, Herlyn, Luqmani, Merkel, Jayne.

Acquisition of data. Walsh, Mukhtyar, Luqmani, Jayne.

Analysis and interpretation of data. Walsh, Mukhtyar, Mahr.

REFERENCES

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