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Abstract Number: 3000

Comparison of Systemic Sclerosis Subsets As Predictors of Mortality and Morbidity

Hebah Alhajeri1, Marie Hudson2,3, Canadian Scleroderma Research Group CSRG4 and Murray Baron5, 1Rheumatology, McGill University, Montreal, QC, Canada, 2Rheumatology, Lady David Institute for Medical Research and Jewish General Hospital, Montreal, QC, Canada, 3Division of Rheumatology, McGill University, Montreal, QC, Canada, 4McGill University, Montreal, QC, Canada, 5Pavillion A, Rm 216, Lady David Institute for Medical Research and Jewish General Hospital, Montreal, QC, Canada

Meeting: 2014 ACR/ARHP Annual Meeting

Keywords: autoantibodies, morbidity and mortality, skin and systemic sclerosis

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Session Information

Title: Systemic Sclerosis, Fibrosing Syndromes and Raynaud's - Clinical Aspects and Therapeutics III: Updates in Predictors and Outcomes in Systemic Sclerosis

Session Type: Abstract Submissions (ACR)

Background/Purpose: Identifying systemic sclerosis (SSc) subsets that predict mortality and morbidity could provide useful prognostic information. We undertook this study to compare the predictive ability of different approaches to subsetting SSc.

Methods: SSc subjects from the Canadian Scleroderma Research Group cohort were studied. Three approaches to subsetting were used: Leroy subsets based on skin involvement (limited (lcSSc) and diffuse cutaneous (dcSSc) subsets), serological subsets (anti-centromere (ACA), anti-topoisomerase I (ATA) and anti-RNA polymerase III (RNAP)), and unsupervised cluster analysis based on the items in the ACR/EULAR 2013 classification criteria, of which 3 clusters were identified (cluster 1 (ACA negative subjects with digital ulcers (DU)), cluster 2 (ACA positive subjects), and cluster 3 (ACA negative subjects with no DU)). Morbidity was defined as forced vital capacity (FVC) < 70% predicted, interstitial lung disease (ILD), pulmonary hypertension (PH) and impaired health-related quality of life (defined as SF-36 Physical Component Summary (PCS) score < 40). Kaplan Meier curves were generated to compare the time to event between the various subsets. Log rank p values < 0.05 were considered statistically significant.

Results: 805 subjects were included (86.1% (N=693) female, 49.8% (N=401) dcSSc, disease duration since onset of first non-Raynaud’s disease manifestation 10.8+9.2 years). Subsetting based on autoantibodies and unsupervised clustering (but not Leroy classification) predicted mortality, with ACA having better survival than RNAP and cluster 1 having better survival than the 2 other clusters. All three approaches to subsetting predicted FVC < 70% and development of ILD: dcSSc was worse than lcSSc, ACA was better than ATA and RNAP, and cluster 1 was better than the other clusters. None of the 3 approaches to subsetting predicted time to PH. Subsetting based on Leroy classification and autoantibodies, but not clusters, predicted time to SF-36 PCS < 40, with dcSSc worse than lcSSc, and ATA worse than ACA.

Conclusion: Different approaches to subsetting provide different prognostic information. Subsetting based on clinical and serological profiles remains a challenge in SSc. In the future, subsetting based on molecular profiles may improve the predictive ability of SSc subsets.

Mortality

FVC<70% predicted

ILD

PH

SF-36 PCS < 40

N

N of events

Time to event

(years)

P

N

N of events

Time to event

(years)

P

N

N of events

Time to event

(years)

P

N

N of events

Time to event

(years)

P

N

N of events

Time to event

(years)

P

Leroy subsets

dcSSc

384

66

13.0

ns

251

30

12.7

*

215

68

11.8

*

258

18

12.4

ns

145

69

12.0

*

lcSSc

350

50

13.9

228

15

13.4

229

34

13.1

233

19

13.3

126

36

12.8

Serology 

ACA

250

31

14.2

176

14

14.0

185

26

13.6

162

10

13.3

ns

102

37

12.4

ATA

107

16

11.6

68

21

11.7

*

46

17

10.1

*

68

5

11.1

40

18

9.6

*

RNAP

127

29

11.9

*

80

9

11.0

*

74

27

10.6

*

87

8

11.5

37

17

12.3

Clusters#

Cluster 1

240

26

14.1

173

3

14.0

187

27

13.7

159

11

13.3

ns

103

37

12.6

ns

Cluster 2

237

44

12.1

*

161

19

11.8

*

153

44

11.3

*

174

15

11.7

91

36

11.9

Cluster 3

268

49

14.0

*

150

24

13.5

*

108

32

12.1

*

163

11

13.7

79

32

12.5

ns – not statistically significant; * p value < 0.05;ACA is the reference group; # Cluster 1 is the reference group


Disclosure:

H. Alhajeri,
None;

M. Hudson,
None;

C. S. R. G. CSRG,
None;

M. Baron,
None.

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