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Open Access

Peer-reviewed

Research Article

ANCA-Associated Glomerulonephritis: Risk Factors for Renal Relapse

Abstract

Relapse in ANCA-associated vasculitis (AAV) has been studied previously, but there are few studies on renal relapse in particular. Identifying patients at high risk of renal relapse may aid in optimizing clinical management. We investigated which clinical and histological parameters are risk factors for renal relapse in ANCA-associated glomerulonephritis (AAGN). Patients (n = 174) were newly diagnosed and had mild–moderate or severe renal involvement. Data were derived from two trials of the European Vasculitis Society: MEPEX and CYCAZAREM. The Cox regression model was used to identify parameters increasing the instantaneous risk (= rate) of renal relapse (useful for instant clinical decisions). For identifying predictors of renal relapse during follow-up, we used Fine & Gray’s regression model. Competing events were end-stage renal failure and death. The cumulative incidence of renal relapse at 5 years was 9.5% (95% CI: 4.8–14.3%). In the Cox model, sclerotic class AAGN increased the instantaneous risk of renal relapse. In Fine & Gray’s model, the absence of interstitial infiltrates at diagnosis was predictive for renal relapse. In this study we used two different models to identify possible relationships between clinical and histopathological parameters at time of diagnosis of AAV with the risk of experiencing renal relapse. Sclerotic class AAGN increased the instantaneous risk of renal relapse. This association is most likely due to the high proportion of sclerosed glomeruli reducing the compensatory capacity. The absence of interstitial infiltrates increased the risk of renal relapse which is a warning sign that patients with a relatively benign onset of disease may also be prone to renal relapse. Renal relapses occurring in patients with sclerotic class AAGN and renal relapses occurring in patients without interstitial infiltrates were mutually exclusive, which may indicate that they are essentially different.

Citation: Göçeroğlu A, Berden AE, Fiocco M, Floßmann O, Westman KW, Ferrario F, et al. (2016) ANCA-Associated Glomerulonephritis: Risk Factors for Renal Relapse. PLoS ONE 11(12): e0165402. https://doi.org/10.1371/journal.pone.0165402

Editor: Masataka Kuwana, Nippon Medical School, JAPAN

Received: June 26, 2016; Accepted: October 11, 2016; Published: December 14, 2016

Copyright: © 2016 Göçeroğlu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data underlying the findings of this study are available upon request from the corresponding author (Email: a.goeceroglu@lumc.nl), as participants of this study did not provide permission to share their data publicly.

Funding: The CYCAZAREM trial was supported by contracts (BMH1-CT93-1078, CIPD-CT94-0307, BMH4-CT97-2328, and IC20-CT97-0019) with the European Union; the MEPEX trial was designed and launched as part of the European Community Systemic Vasculitis Trial project (BMH1-CT93-1078 and CIPD-CT94-0307) and finished as part of the ANCA Associated Vasculitis European Randomized Trial project (BMH4-CT97-2328 and IC20-CT97-0019) funded by the European Union.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA) are the major subtypes of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Approximately 80% of patients with GPA and 90% with MPA develop kidney involvement during the disease course.[1] ANCA-associated glomerulonephritis (AAGN) progresses to end-stage renal failure (ESRF) in approximately 20–40% of patients.[2–5] The gold standard for establishing AAGN is a renal biopsy, which typically shows a pauci-immune necrotizing crescentic glomerulonephritis,[6,7] which can be grouped into four classes.[8] Relapse in ANCA-associated vasculitis has been studied previously, but there are few studies on renal relapse in particular. It is important to find a balance between the risk of relapse and the risk of treatment-related adverse effects.

Identifying patients at high risk of renal relapse may aid in optimizing clinical management. Previous relevant studies mainly focused on relapse in general with clinical data,[2,5,9–19] identifying proteinase 3 (PR3)-ANCA, GPA, lung or cardiovascular involvement, and better renal function at presentation as associated with relapse in general.[2,10–14,16,17,19] Note that different statistical analyses were used in these reports to determine the influence of various parameters on relapse; some published studies employed Fine & Gray’s regression model while others used the standard Cox regression model. Both models are correct but address different research questions.

If more than one endpoint can occur, a competing risk analysis must be performed. In the case of renal relapse, ESRF and death are competing events, because the occurrence of one of them preclude the occurrence of renal relapse. Fine & Gray’s regression model is used to estimate the effect of a risk factor on the cumulative incidence of renal relapse (CIR), which denotes the probability of experiencing renal relapse before time t. The classical Cox regression model is used to investigate the effect of risk factors on the rate of renal relapse. The parameters in the Cox regression model are hazard ratios and the interpretation is the traditional one. Note that relationships between risk factors (or explanatory parameters) and cause-specific hazards do not lead to simple relationships between explanatory variables and cumulative incidence. It is important to emphasize that both approaches are valid but that they answer different research questions and may render different results. Thus the effect of a parameter on the CIR might be different from its effect on the rate of renal relapse. Estimation based on Fine & Gray’s model is useful for making predictions from the start of the disease, whereas the rate looks at parameters that increase the instantaneous risk of renal relapse and is useful for instant clinical decisions. In the present study, we apply both methods and discuss implications from their results.

We investigated whether diagnostic clinical and histological parameters are associated with renal relapse in patients with AAV with primary renal involvement. The study aim was to identify diagnostic tools that may be helpful in monitoring and managing patients with AAV, in particular in relation to renal relapse.

Material and Methods

Patients

Patients included in this study were newly diagnosed with AAV with either mild to moderate or severe renal involvement (serum creatinine ≤ or > 500 μmol/L (≤ or > 5.8 mg/dl)). Patients were derived from two international multicenter randomized clinical European Vasculitis Society (EUVAS) trials: MEPEX and CYCAZAREM.[11,20] Inclusion criteria for both trials are described elsewhere.[11,20] The diagnosis was based on a clinical presentation compatible with ANCA-associated vasculitis and substantiated by a positive ANCA serology and/or histology.

The MEPEX and CYCAZAREM trial follow-up continued until 12 and 18 months after diagnosis, respectively. During the trials, patients received protocolized treatment regimens.[11,20] After these follow-up periods, patients were treated according to their local physician’s standards. Patients were included and followed-up in the period June 1, 1995, through 30 November, 2006. Patients were included in this study only if histological data obtained from renal biopsy at the time of study entry, clinical data, and long-term follow-up data were available.

Disease definitions were adapted from the Chapel Hill Consensus Conference on the Nomenclature of Systemic Vasculitis and a previous European Union Study.[21,22] Both trials were conducted according to the 1964 Declaration of Helsinki and subsequent amendments. The trials were approved by the local ethics committees of the participating centers throughout Europe. All patients gave written informed consent. The ethics for the use of the data and material for subsequent studies, including this study, was approved by the West Midlands Multi-centre Research Ethics Committee, date 22/09/2004 (reference number: MREC/98/7/37). In addition, this study was performed according to the 'Netherlands Code of Conduct for Scientific Practice', an ethical code for performing observational studies with patient material approved by the Federatie van Medisch Wetenschappelijke Verenigingen (translated: Federation of Medical Scientific Organisations) together with the legal and ethical committee of the Koninklijke Nederlandse Akademie van Wetenschappen (translated: Royal Dutch Academy of Science) and the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (translated: Dutch Organisation for Scientific Research). The data of the patients were analyzed anonymously.

Clinical and histological parameters

Candidate parameters for clinical predictors of renal relapse in this study were serum creatinine levels, age, sex, diagnosis (GPA or MPA), ANCA-antigen specificity (PR3-ANCA or myeloperoxidase (MPO)-ANCA), and receiving plasma exchange during induction therapy. Patients were subdivided into two groups of GPA and MPA based on the clinical criteria. Renal-limited vasculitis was regarded as a form of MPA.

Candidate parameters for histological predictors were determined from paraffin sections of renal biopsies. Stains used for evaluation were silver, periodic acid–Schiff, hematoxylin and eosin, and trichrome. Sections were reviewed by two of a panel of five participating pathologists (IMB, FF, LHN, RW, and/or JAB). Both pathologists, blinded to patient data and the other observer’s results, scored the biopsies separately and according to a previously standardized protocol, which was proven to be comprehensive and reproducible when used for histologic analysis.[23] One of the histological parameters included in this previously standardized protocol is interstitial infiltrates. Interstitial infiltrates were scored according to the following categories:

  • None: <10% of the unscarred parenchyma infiltrated.
  • Mild: 10 to 25% of the unscarred parenchyma infiltrated.
  • Quite dense: 26 to 50% of the unscarred parenchyma infiltrated.
  • Very dense: >50% of the unscarred parenchyma infiltrated.

In this study, only biopsies with a minimum of seven whole glomeruli were analyzed for glomerular lesions and the histopathological classification system of AAGN.[8] During plenary meetings, the panel of five pathologists decided upon the final scores to achieve consensus for each biopsy.

Clinical outcomes

The clinical outcome parameter was first renal relapse. A renal relapse was defined as a rise in serum creatinine of >30% or a fall in estimated glomerular filtration rate >25% and/or new hematuria or proteinuria (all attributable to active vasculitis), as indicated by the Birmingham Vasculitis Activity Score.[24–26] Patients were followed up until the last visit or death.

Statistical analyses

In this study, more than one endpoint could occur, namely renal relapse, ESRF, or death. The event of interest was renal relapse, while ESRF and death were competing events. Two regression models used in the competing risks framework were estimated here: Fine & Gray’s model and Cox model. To study the effect of risk factors on the CIR the former model is employed while the latter is used to study the effects of risk factors on the rate of renal relapse, i.e. the cause-specific hazard. For more details concerning the difference between the two models, see Andersen et al. and Koller et al.[27,28] The technical aspects of competing risks were described previously by Putter et al. [29]

Univariate analyses with both methods were performed on every clinical and histological parameter. These analyses were performed on all patients without ESRF at baseline (n = 149), except for glomerular lesions and the histopathological class of AAGN. In addition, we performed a χ2-test to see whether the percent of renal relapse differed significantly between the histopathological classes. Also a Pearson correlation test was performed to investigate whether the presence of interstitial infiltrate was correlated with interstitial fibrosis. All baseline parameters were included as fixed covariates.

Because of the number of parameters (13 in this study) and the relatively low number of events, inclusion of too many parameters carries the risk of “overfitting.”[30] Therefore, predefined smaller sets of entry parameters were included in the multivariate analyses, as follows: based on the original publication of the histopathological classification system of AAGN[8]; based on parameters described previously more than once as being associated with relapse; and based on only histological parameters.

We denoted hazard ratios estimated by employing Cox regression model as cause-specific hazard ratio (csHR) and the hazard ratios estimated by using Fine & Gray’s regression model as Fine & Gray’s HR (F&G HR). All hazard ratios are provided with 95% confidence intervals (CI). A P value of less than 0.05 was considered significant. Statistical analyses were performed in SPSS (version 20.0; SPSS Inc, Chicago, IL) and R 2–18 (http://cran.r-project.org). All analyses concerning competing risks were performed with the mstate library.[31,32]

Results

Patients

A total of 174 patients with newly diagnosed AAV and a renal biopsy at diagnosis were included in this study. Table 1 shows the baseline patient characteristics. The median follow-up time was 102 months (range: 38–136 months).

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Table 1. Baseline characteristics of all patients.

https://doi.org/10.1371/journal.pone.0165402.t001

Renal relapse

Of the 174 patients, 25 could not experience a renal relapse because they had ESRF at baseline. Of the remaining 149 patients, 22 were chronic kidney disease (CKD) stage 1–2 and 127 were CKD stage 3–5. Of these 22 CKD stage 1–2 patients, 5 (22.7%) developed CKD stage 3–5 within 5 years of follow-up. None of these 5 had experienced a renal relapse. In total, 31 patients experienced a renal relapse during follow-up. The CIR at 5 years was 9.5% (95% CI: 4.8–14.3%). A total of 19 patients developed ESRF during follow-up without experiencing renal relapse, and 29 died without experiencing renal relapse and without developing ESRF during follow-up. All patients who died during the follow-up period had CKD stage ≥ 3 at baseline. Seventy patients had none of these events during follow-up (Figs 1 and 2).

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Fig 1. Events.

Overview of different events experienced by 174 patients during follow-up. Twenty-five patients presented with ESRF at baseline. Nineteen of them died during follow-up. Thirty-one patients experienced renal relapse during follow-up; six of them developed ESRF, of whom four died, and three died without ESRF during follow-up. Nineteen patients developed ESRF without renal relapse (competing event 1), of whom 10 died at a later timepoint. Twenty-nine patients died without experiencing renal relapse or ESRF (competing event 2). Seventy patients experienced no event during follow-up. Abbreviations: DSF, disease-free survival; ESRF, end-stage renal failure; ESRF0, end-stage renal failure at baseline.

https://doi.org/10.1371/journal.pone.0165402.g001

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Fig 2. Cumulative incidence of renal relapse, end-stage renal failure or death.

Cumulative incidence of patients who experienced renal relapse (event of interest) and patients who developed ESRF or died (competing events). This figure illustrates the probability of experiencing a renal relapse and the probability of developing ESRF or dying without experiencing a renal relapse. Abbreviations: CI, cumulative incidence; ESRF, end-stage renal failure.

https://doi.org/10.1371/journal.pone.0165402.g002

Of the 149 patients, 113 had adequate renal tissue samples (at least seven whole glomeruli in the renal biopsy) for classification purposes. Their diagnostic renal biopsies were classified as follows: 23 focal class (20.4%), 58 crescentic class (51.3%), 18 mixed class (15.9%), and 14 sclerotic class (12.4%) (Table 2). Of these 113 patients, 24 experienced a renal relapse during follow-up. The numbers of patients having a renal relapse per class were 5/23 (21.7%) focal class, 9/58 (15.5%) crescentic class, 4/18 (22.2%) mixed class, and 6/14 (42.9%) sclerotic class (χ2-test: P = 0.167). The distribution of patient ages did not differ across classes. In particular, patients from the sclerotic class were not older than those in other classes. During the trials, therapies given to the patients did not differ among the four classes. Fourteen patients developed ESRF during follow-up without experiencing renal relapse (competing event 1). Twenty patients died without experiencing renal relapse and without developing ESRF during follow-up (competing event 2) (Fig 3).

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Table 2. Baseline characteristics of patients per histopathological class.

https://doi.org/10.1371/journal.pone.0165402.t002

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Fig 3. Events of the 113 patients with ≥7 glomeruli in their renal biopsy without end-stage renal failure at baseline.

Twenty-four patients experienced a renal relapse during follow-up. Of these 24 patients, four developed ESRF, of which three died, and four died without ESRF during follow-up. Fourteen patients developed ESRF without renal relapse (competing event 1), of whom 7 died at a later timepoint. Twenty patients died without renal relapse and without ESRF (competing event 2). Fifty-five patients experienced no event during follow-up. Abbreviations: DSF, disease-free survival; ESRF, end-stage renal failure.

https://doi.org/10.1371/journal.pone.0165402.g003

Competing risks analyses

To investigate which parameters are associated with the CIR, Fine & Gray’s regression model was used. The traditional Cox regression model was used to estimate the effect of the parameters on the rate of renal relapse.[27]

Fine & Gray’s regression model

The univariate analyses showed that age, interstitial infiltrates, and intra-epithelial infiltrates were associated with CIR (Table A in S1 File). Older age was associated with a lower risk for experiencing a renal relapse. Higher scores of interstitial infiltrates and intra-epithelial infiltrates, which are both signs of acute disease activity, were associated with a lower risk for renal relapse. The histopathological class and CKD stage were not significantly associated with the risk of renal relapse.

Among all histological parameters, only interstitial infiltrates had a significant association on the risk of renal relapse (the CIR) in the multivariate analysis (Table 3). Although there was a correlation between interstitial infiltrates and interstitial fibrosis, this correlation was relatively weak (r = 0.236, P = 0.004). Therefore, interstitial fibrosis was not predictive for renal relapse. Patients with mild infiltrates had a four times lower risk for renal relapse than patients without interstitial infiltrates (F&G HR: 0.09; 95% CI: 0.02–0.39; P = 0.001). This association persisted when correcting for the patient cohort indicator.

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Table 3. Multivariate analysis with Fine & Gray’s model based on histological parameters.

https://doi.org/10.1371/journal.pone.0165402.t003

Among all clinical parameters, only age, corrected for other parameters, was associated with patients experiencing a renal relapse (Table B in S1 File).

Cox regression model for renal relapse

Among all baseline parameters, only the histopathological class was a significant risk factor for renal relapse in the univariate analyses (Table C in S1 File). CKD stage was not associated with renal relapse. After correction for age, baseline serum creatinine, and plasma exchange therapy, the histopathological class remained the only statistical significant risk factor for experiencing a renal relapse (Table 4). Focal and crescentic class biopsies were associated with a lower cause-specific hazard ratio compared to sclerotic class biopsies. Focal class had a 10.1 times lower rate than the sclerotic class (csHR: 0.10; 95% CI: 0.02–0.60; P = 0.01), and crescentic class had a 4.7 times lower rate than the sclerotic class (csHR: 0.21; 95% CI: 0.07–0.62; P = 0.004). Patient cohort (MEPEX or CYCAZAREM) did not affect these associations.

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Table 4. Multivariate analyses with both models based on the original publication of the histopathological classification system of ANCA-associated glomerulonephritis (Berden et al, 2010 [8]).

https://doi.org/10.1371/journal.pone.0165402.t004

With the inclusion of histopathological class, baseline serum creatinine, ANCA type, and diagnosis, only the histopathological class was a significant risk factor for renal relapse (Table 5). Again, focal and crescentic classes were associated with a lower rate compared to sclerotic class. In this model, focal class had a 10.8 times lower rate than the sclerotic class (csHR: 0.09; 95% CI: 0.02–0.55; P = 0.009), and crescentic class had a 4.8 times lower hazard rate than the sclerotic class (csHR: 0.21; 95% CI: 0.07–0.64; P = 0.006). There was no effect of patient cohort on these associations.

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Table 5. Multivariate analyses with both models based on previously described parameters associated with relapse.

https://doi.org/10.1371/journal.pone.0165402.t005

Discussion

This study shows that the histopathological class of AAGN in the renal biopsy at diagnosis is a risk factor for renal relapse. More specifically, sclerotic class was associated with a higher rate of renal relapse during long-term follow-up. It is important to emphasize that the effect of sclerotic class on the risk of renal relapse, i.e. the cumulative incidence, estimated by the Fine & Gray’s model is different from its effects on the rate, i.e. the cause-specific hazard, estimated by the Cox regression proportional hazard model. This is because the way in which risk factors (or explanatory variables) are associated with the cause-specific hazards may not coincide with the way these covariates are associated with the cumulative incidence. The sclerotic class in AAGN is defined by ≥50% globally sclerosed glomeruli, meaning that the majority of glomeruli are non-functioning and that the compensatory ability of the kidneys is relied on heavily. Therefore, in these patients, a renal relapse may become more readily apparent because the compensatory capacity of a sclerotic class kidney is reduced. Moreover, with fewer functioning glomeruli, these glomeruli may become more vulnerable to a second hit, i.e., a relapse. In patients with AAGN that is not in the sclerotic class, minor relapses may remain subclinical because of the relatively higher number of preserved glomeruli and their compensatory ability. Patients’ treatments were not based on the histopathological classification. Therefore, the sclerotic class may provide a setting in which renal relapse may be more likely to be detected than in the setting of another histopathological class.

To investigate the effect of risk factors on the risk of renal relapse, i.e. the cumulative incidence, we applied Fine & Gray’s regression model. Results show that absence of interstitial inflammatory infiltrates is associated with the risk of renal relapse. Patients with these infiltrates had a lower risk for future renal relapse than patients without these inflammatory infiltrates. The association of interstitial infiltrates with renal relapse persisted when corrected for other histological parameters. Previous EUVAS studies focused on predictive clinical and serological parameters for relapse in general. Walsh et al. investigated clinical and serological parameters predictive for relapse in general in a European cohort consisting of 535 patients. In that study, PR3-ANCA, lower serum creatinine levels at presentation, cardiovascular involvement, and GPA were independently associated with an increased risk for relapse, whether in the kidney or any other organ.[19] Our study is based on the histopathological data of those patients from the previous study by Walsh et al. who had a renal biopsy with sufficient tissue for proper evaluation; thus, we could investigate which histological parameters are predictive for renal relapse. Our finding that the absence of interstitial infiltrates is predictive for renal relapse is in line with the finding by Walsh et al. that better renal function increases the risk for a relapse in general because absence of interstitial infiltrates also correlates with better renal function at the time of biopsy.[33,34] Experiencing a renal relapse has a negative influence on renal outcome.[35] Therefore, clinicians should realize that renal relapses must be identified and treated and keep in mind that those patients with a relatively benign clinical course at onset in particular will be prone to developing a renal relapse.

This study shows a higher cumulative risk for ESRF or death compared to renal relapse as shown in Fig 2. These results may have been influenced by the inclusion of patient from the MEPEX trial which included patients with serum creatinine >500 μmol/L or immediate dialysis dependency. Nineteen patients (11%) had ESRF during follow-up without experiencing a (clinical) renal relapse. It is possible that these patients had subclinical renal relapses, but they were not detected clinically. Based on this knowledge, we emphasize the need for chronic kidney disease management and renal protective strategies.

Our study has a number of limitations. Because of the sample size and relatively low number of events, we were limited in the size of the predefined multivariate analyses. To avoid bias, all multivariate analyses were predefined before the start of this study. To use the best possible predefined analyses, we constructed them based on the literature regarding the histopathological classification system of AAGN. Unfortunately, repeat biopsies during the time of renal relapse were not performed because it is generally considered that the risk of taking a biopsy would not weight against the benefit of a histologically proven renal relapse, keeping in mind that these can be diagnosed with a high level of certainty on the basis of the clinical findings.

In conclusion, we used two regression models to identify possible relationships between clinical and histopathological parameters at time of diagnosis of AAV with the risk of renal relapse, i.e. the cumulative incidence, and the effect on the rate of renal relapse, i.e. the cause-specific hazard. The effect of sclerotic class on the risk of renal relapse estimated by the Fine & Gray’s regression model was different from its effects on the rate estimated by the Cox regression model. Most likely, the lack of compensatory function in the largely sclerosed kidneys gives rise to the identification of a relatively high number of renal relapses. A strong predictive parameter for renal relapse was the absence of interstitial infiltrates as determined with the Fine & Gray model. Combining these results with those of previous studies, it seems that the patient with AAV characterized by a relatively benign clinical setting at onset is prone to a renal relapse. In this study, renal relapses occurring in patients with sclerotic class AAGN and renal relapses occurring in patients without interstitial infiltrates were mutually exclusive, which may indicate that they are essentially other kinds of relapses. Further studies are called to look further into the characteristics of renal relapses in AAV, in particular to find out whether the histopathological data at disease onset could serve as a guideline for the management of renal relapses in AAV.

Supporting Information

S1 File.

Table A. Univariate analyses with Fine & Gray’s model.

Abbreviations: 95% CI, 95% confidence interval; ANCA, anti-neutrophil cytoplasmic antibody; CKD, chronic kidney disease; GPA, granulomatosis with polyangiitis; MPA, microscopic polyangiitis; MPO, myeloperoxidase; NS, not significant PR3, proteinase 3; F&G dHR, Fine and Gray’s hazard ratio.

aReference group: Female.

bReference group: Granulomatosis with polyangiitis.

cReference group: Negative.

dReference group: No plasma exchange therapy received.

eReference group: No intra-epithelial infiltrates.

Table B. Multivariate analysis with Fine & Gray’s model based on clinical parameters and histopathological class.

Sample size: 112 patients

Abbreviations: 95% CI, 95% confidence interval; ANCA, anti-neutrophil cytoplasmic antibody; GPA, granulomatosis with polyangiitis; MPA, microscopic polyangiitis; MPO, myeloperoxidase; NS, not significant; PR3, proteinase 3; F&G HR, Fine and Gray’s hazard ratio.

aReference group: Female.

bReference group: Granulomatosis with polyangiitis.

cReference group: Negative.

dReference group: No plasma exchange therapy received.

Table C. Univariate analyses with Cox regression model.

Abbreviations: 95% CI, 95% confidence interval; ANCA, anti-neutrophil cytoplasmic antibody; CKD, chronic kidney disease; csHR, cause-specific hazard ratio; GPA, granulomatosis with polyangiitis; MPA, microscopic polyangiitis; MPO, myeloperoxidase; PR3, proteinase 3.

aReference group: Female.

bReference group: Granulomatosis with polyangiitis.

cReference group: Negative.

dReference group: No plasma exchange therapy received.

eReference group: No intra-epithelial infiltrates.

https://doi.org/10.1371/journal.pone.0165402.s001

(DOC)

Acknowledgments

In addition to the authors, the following investigators participated: D. Abramowicz, J. Sennesael, Free University of Brussels, Brussels, Belgium; M. Wissing, Erasmus Hospital and Edith Cavell Medical Institute, Brussels, Belgium; P. Madhoun, Edith Cavell Medical Institute, Brussels, Belgium; M. Dhaene, Clinique Louis Caty, Baudour, Belgium; D. Blockmans, University Hospital, Leuven, Belgium; J. Stolear, IMC de Tournai, Tournai, Belgium; V. Chabova, I. Rychlik, V. Tesar, Charles University Hospital, Prague, Czech Republic; A. Wiik, Statens Seruminstitutet, Copenhagen, Denmark; C. Grönhagen-Riska, A. Ekstrand, University of Helsinki, Helsinki, Finland; P. Lesavre, Hôpital Necker, Paris, France; L. Guillevin, Hôpital Cochin, Paris, France; P. Vanhille, Centre Hospitalier, Valenciennes, France; K. Andrassy, O. Hergesell, Heidelberg University Hospital, Heidelberg, Germany; F. van der Woude, R. Nowack, University of Mannheim, Mannheim, Germany; K. de Groot, University Hospital, Hannover, Germany; H. Rupprecht, P. Weber, S. Weidner, Klinikum Nürnberg, Nürnberg, Germany; W. Schmitt, University of Luebeck and Rheumaklinik Bad Bramstedt and University Hospital Mannheim, Luebeck and Mannheim, Germany; W. Gross, University of Luebeck and Rheumaklinik Bad Bramstedt, Luebeck, Germany; M. Schneider, C. Specker, Heinrich Heine Universität, Düsseldorf, Germany; M. Vischedyk, St. Vinzenz-Hospital Paderborn, Paderborn, Germany; C. Feighery, St. James Hospital, Dublin, Ireland; G. Gregorini, Spedali Civili, Brescia, Italy; R. Sinico, Ospedale San Carlo Borromeo, Milan, Italy; R. Confalonieri, Ospedale Niguarda, Niguarda, Italy; J. Dadoniené, University of Vilnius, Vilnius, Lithuania; C. Kallenberg, C. Stegeman, University Hospital, Groningen, Netherlands; E. van Gurp, Meander Medical Center, Amersfoort, Netherlands; C. Siegert, C. Verburgh, R. de Lind van Wijngaarden, Leiden University Medical Center, Leiden, Netherlands; J.W. Cohen Tervaert, Maastricht University Medical Center, Maastricht, Netherlands; A. Serra, Hospital Germans Trias i Pujol, Badalona, Spain; E. Mirapeix, Hospital Clinic i Provincial, Barcelona, Spain; M. Valles, Hospital Doctor Josep Trueta, Girona, Spain; R. Poveda, Hospital Universitari Bellvitge Princeps d’España, Barcelona, Spain; J. Ballarin, F. Calero, Fundació Puigvert, Barcelona, Spain; A. Bruchfeld, E. Pettersson, M. Heimburger, Huddinge University Hospital, Stockholm, Sweden; G. Germanis, Danderyds Sjukhus, Danderyds, Sweden; D. Selga, University of Lund, Lund, Sweden; Z. Heigl, I. Lundberg, E. Svenungssen, Karolinska Sjukhuset, Stockholm, Sweden; M. Segelmark, G. Sterner, University Hospital of Malmö, Malmö, Sweden; M. Tidman, Nephrology University Hospital, Orebro, Sweden; P. Mathieson, C. Tomson, Southmead Hospital, Bristol, UK; R. Watts, Ipswich Hospital, Ipswich, UK; J. Feehally, University Hospital, Leicester, UK; A. Burns, Royal Free Hospital, London, UK; R. Luqmani, Western General Hospital and Royal Infirmary, Edinburgh, UK; N. Turner, Royal Infirmary, Edinburgh, UK; D. Adu, C. Savage, L.Harper, P. Bacon, University of Birmingham, Birmingham, UK; P. Mason, Churchill Hospital, Oxford, UK; D. Oliveira, St. George’s Hospital, London, UK; J. Stevens, Southampton Hospital, Southampton, UK; A. Williams, Morriston Hospital, Swansea, UK.

We thank Herbert Hauer (Leiden) and Rob de Lind van Wijngaarden (Leiden) for data management. We also thank the Dutch Vasculitis Foundation (Vasculitis Stichting) for their support.

This study was presented in part at the 16th International Vasculitis & ANCA Workshop, April 14–17, 2013, Paris, France; Kidney Week 2013 (American Society of Nephrology), November 5–10, 2013, Atlanta, GA, USA; and has been published in part in abstract form.

Author Contributions

  1. Conceptualization: AG AEB ECH JAB DRWJ IMB.
  2. Data curation: AG AEB OF KWW ECH NR MW DRWJ.
  3. Formal analysis: AG AEB MF MW.
  4. Funding acquisition: KWW FF GG CDP ECH NR DRWJ.
  5. Investigation: AG AEB OF KWW FF GG CDP ECH LHN NR RW MW JAB DRWJ IMB.
  6. Methodology: AG AEB MF MW IMB.
  7. Project administration: AG IMB.
  8. Resources: AEB MF OF KWW FF GG CDP ECH LHN NR RW MW DRWJ IMB.
  9. Supervision: JAB DRWJ IMB.
  10. Validation: AG AEB MF OF KWW FF GG CDP ECH LHN NR RW MW JAB DRWJ IMB.
  11. Visualization: AG MF IMB.
  12. Writing – original draft: AG AEB MF IMB.
  13. Writing – review & editing: AG AEB MF OF KWW FF GG CDP ECH LHN NR RW MW JAB DRWJ IMB.

References

  1. 1. Jennette JC, Falk RJ. Small-vessel vasculitis. N Engl J Med. 1997;337: 1512–1523. pmid:9366584
  2. 2. Booth AD, Almond MK, Burns A, Ellis P, Gaskin G, Neild GH, et al. Outcome of ANCA-associated renal vasculitis: a 5-year retrospective study. Am J Kidney Dis. 2003;41: 776–784. pmid:12666064
  3. 3. Little MA, Nightingale P, Verburgh CA, Hauser T, de Groot K, Savage C, et al. Early mortality in systemic vasculitis: relative contribution of adverse events and active vasculitis. Ann Rheum Dis. 2010;69: 1036–1043. pmid:19574233
  4. 4. Slot MC, Cohen Tervaert JW, Franssen CFM, Stegeman CA. Renal survival and prognostic factors in patients with PR3-ANCA associated vasculitis with renal involvement. Kidney Int. 2003;63: 670–677. pmid:12631133
  5. 5. Westman KWA, Bygren PG, Olsson H, Ranstam J, Wieslander J. Relapse rate, renal survival, and cancer morbidity in patients with Wegener's granulomatosis or microscopic polyangiitis with renal involvement. J Am Soc Nephrol. 1998;9: 842–852. pmid:9596082
  6. 6. Falk RJ, Jennette JC. ANCA small-vessel vasculitis. J Am Soc Nephrol. 1997;8: 314–322. pmid:9048352
  7. 7. Bajema IM, Hagen EC, van der Woude FJ, Bruijn JA. Wegener's granulomatosis: a meta-analysis of 349 literary case reports. J Lab Clin Med. 1997;129: 17–22. pmid:9011587
  8. 8. Berden AE, Ferrario F, Hagen EC, Jayne DR, Jennette JC, Joh K, et al. Histopathologic classification of ANCA-associated glomerulonephritis. J Am Soc Nephrol. 2010;21: 1628–1636. pmid:20616173
  9. 9. Gordon M, Luqmani RA, Adu D, Greaves I, Richards N, Michael J, et al. Relapses in patients with a systemic vasculitis. Q J Med. 1993;86: 779–789. pmid:7906421
  10. 10. Hogan SL, Falk RJ, Chin H, Cai J, Jennette CE, Jennette JC, et al. Predictors of relapse and treatment resistance in antineutrophil cytoplasmic antibody-associated small-vessel vasculitis. Ann Intern Med. 2005;143: 621–631. pmid:16263884
  11. 11. Jayne D, Rasmussen N, Andrassy K, Bacon P, Cohen Tervaert JW, Dadoniené J, et al. A randomized trial of maintenance therapy for vasculitis associated with antineutrophil cytoplasmic autoantibodies. N Engl J Med. 2003;349: 36–44. pmid:12840090
  12. 12. Koldingsnes W, Nossent JC. Baseline features and initial treatment as predictors of remission and relapse in Wegener's granulomatosis. J Rheumatol. 2003;30: 80–88. pmid:12508394
  13. 13. Kyndt X, Reumaux D, Bridoux F, Tribout B, Bataille P, Hachulla E, et al. Serial measurements of antineutrophil cytoplasmic autoantibodies in patients with systemic vasculitis. Am J Med. 1999;106: 527–533. pmid:10335724
  14. 14. Lionaki S, Blyth ER, Hogan SL, Hu Y, Senior BA, Jennette CE, et al. Classification of antineutrophil cytoplasmic autoantibody vasculitides: the role of antineutrophil cytoplasmic autoantibody specificity for myeloperoxidase or proteinase 3 in disease recognition and prognosis. Arthritis Rheum. 2012;64: 3452–3462. pmid:23023777
  15. 15. Nachman PH, Hogan SL, Jennette JC, Falk RJ. Treatment response and relapse in antineutrophil cytoplasmic autoantibody-associated microscopic polyangiitis and glomerulonephritis. J Am Soc Nephrol. 1996;7: 33–39. pmid:8808107
  16. 16. Pagnoux C, Hogan SL, Chin H, Jennette JC, Falk RJ, Guillevin L, et al. Predictors of treatment resistance and relapse in antineutrophil cytoplasmic antibody-associated small-vessel vasculitis: comparison of two independent cohorts. Arthritis Rheum. 2008;58: 2908–2918. pmid:18759282
  17. 17. Stegeman CA, Cohen Tervaert JW, Sluiter WJ, Manson WL, De Jong PE, Kallenberg CGM. Association of chronic nasal carriage of Staphylococcus aureus and higher relapse rates in Wegener granulomatosis. Ann Intern Med. 1994;120: 12–17. pmid:8250451
  18. 18. Wada T, Hara A, Arimura Y, Sada KE, Makino H. Risk factors associated with relapse in Japanese patients with microscopic polyangiitis. J Rheumatol. 2012;39: 545–551. pmid:22174198
  19. 19. Walsh M, Flossmann O, Berden A, Westman K, Hoglund P, Stegeman C, et al. Risk factors for relapse of antineutrophil cytoplasmic antibody-associated vasculitis. Arthritis Rheum. 2012;64: 542–548. pmid:21953279
  20. 20. Jayne DRW, Gaskin G, Rasmussen N, Abramowicz D, Ferrario F, Guillevin L, et al. Randomized trial of plasma exchange or high-dosage methylprednisolone as adjunctive therapy for severe renal vasculitis. J Am Soc Nephrol. 2007;18: 2180–2188. pmid:17582159
  21. 21. Jennette JC, Falk RJ, Andrassy K, Bacon PA, Churg J, Gross WL, et al. Nomenclature of systemic vasculitides. Proposal of an international consensus conference. Arthritis Rheum. 1994;37: 187–192. pmid:8129773
  22. 22. Hagen EC, Daha MR, Hermans J, Andrassy K, Csernok E, Gaskin G, et al. Diagnostic value of standardized assays for anti-neutrophil cytoplasmic antibodies in idiopathic systemic vasculitis. EC/BCR Project for ANCA Assay Standardization. Kidney Int. 1998;53: 743–753. pmid:9507222
  23. 23. Bajema IM, Hagen EC, Hansen BE, Hermans J, Noël LH, Waldherr R, et al. The renal histopathology in systemic vasculitis: an international survey study of inter- and intra-observer agreement. Nephrol Dial Transplant. 1996;11: 1989–1995. pmid:8918712
  24. 24. Luqmani RA, Bacon PA, Moots RJ, Janssen BA, Pall A, Emery P, et al. Birmingham Vasculitis Activity Score (BVAS) in systemic necrotizing vasculitis. QJM. 1994;87: 671–678. pmid:7820541
  25. 25. Luqmani RA, Exley AR, Kitas GD, Bacon PA. Disease assessment and management of the vasculitides. Baillieres Clin Rheumatol. 1997;11: 423–446. pmid:9220084
  26. 26. Mukhtyar C, Lee R, Brown D, Carruthers D, Dasgupta B, Dubey S, et al. Modification and validation of the Birmingham Vasculitis Activity Score (version 3). Ann Rheum Dis. 2009;68: 1827–1832. pmid:19054820
  27. 27. Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in epidemiology: possibilities and pitfalls. Int J Epidemiol. 2012;41: 861–870. pmid:22253319
  28. 28. Koller MT, Raatz H, Steyerberg EW, Wolbers M. Competing risks and the clinical community: irrelevance or ignorance? Stat Med. 2012;31: 1089–1097. pmid:21953401
  29. 29. Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007;26: 2389–2430. pmid:17031868
  30. 30. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49: 1373–1379. pmid:8970487
  31. 31. de Wreede LC, Fiocco M, Putter H. Mstate: an R package for the analysis of competing risks and multi-state models. J Stat Softw. 2011;38: 1–30.
  32. 32. de Wreede LC, Fiocco M, Putter H. The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Comput Methods Programs Biomed. 2010;99: 261–274. pmid:20227129
  33. 33. Bajema IM, Hagen EC, Hermans J, Noël LH, Waldherr R, Ferrario F, et al. Kidney biopsy as a predictor for renal outcome in ANCA-associated necrotizing glomerulonephritis. Kidney Int. 1999;56: 1751–1758. pmid:10571783
  34. 34. Hauer HA, Bajema IM, Van Houwelingen HC, Ferrario F, Noël LH, Waldherr R, et al. Determinants of outcome in ANCA-associated glomerulonephritis: a prospective clinico-histopathological analysis of 96 patients. Kidney Int. 2002;62: 1732–1742. pmid:12371974
  35. 35. de Joode AA, Sanders JS, Stegeman CA. Renal survival in proteinase 3 and myeloperoxidase ANCA-associated systemic vasculitis. Clin J Am Soc Nephrol. 2013;8: 1709–1717. pmid:23846462