ARTHRITIS & RHEUMATOLOGY Vol. 66, No. 6, June 2014, pp 1625–1635 DOI 10.1002/art.38390 © 2014, American College of Rheumatology

Prediction of Pulmonary Complications and Long-Term Survival in Systemic Sclerosis Svetlana I. Nihtyanova,1 Benjamin E. Schreiber,1 Voon H. Ong,1 Daniel Rosenberg,2 Pia Moinzadeh,3 J. Gerrard Coghlan,1 Athol U. Wells,4 and Christopher P. Denton1 Objective. To assess survival and incidence of organ-based complications in a large single-center cohort of unselected systemic sclerosis (SSc) patients, and to explore predictors of survival and clinically significant pulmonary fibrosis (PF) and pulmonary hypertension (PH). Methods. The study cohort consisted of 398 consecutive SSc patients, followed up for up to 15 years. Cox proportional hazards analysis with demographic, clinical, and laboratory characteristics as predictor variables was used to develop prediction models for pulmonary complications and survival. Results. The overall survival estimate at the end of followup was 57% among patients with limited cutaneous SSc (lcSSc) and 50% among patients with diffuse cutaneous SSc (dcSSc) (P ⴝ 0.017). We found that greater age at disease onset, dcSSc, lower diffusing capacity for carbon monoxide (DLCO), lower hemoglobin levels, higher serum creatinine levels, and the presence of PH or cardiac involvement were independent predictors of worse survival. Over the entire followup period, 42% of

dcSSc patients and 22% of lcSSc patients developed clinically significant PF (P < 0.001). The variables that predicted clinically significant PF development were dcSSc, greater age at onset, lower forced vital capacity and DLCO, and the presence of anti–topoisomerase I antibody, while the presence of anticentromere antibody was protective. There was no difference in cumulative incidence of PH between the 2 subsets—24% in lcSSc and 18% in dcSSc (P ⴝ 0.558). Incidence rates were 1–2% per year. The PH prediction model demonstrated that greater age at onset, increase in serum creatinine levels, lower DLCO, and the presence of anti–RNA polymerase III or anti–U3 RNP antibodies were associated with increased risk of PH, while anti– topoisomerase I antibody positivity reduced the hazard. Conclusion. Our study provides data on longterm outcome of SSc and the timing and frequency of major organ complications. The predictive models we present could be used as clinical tools for patient risk stratification and could facilitate cohort enrichment for event-driven studies.

Supported by an Orphan Diseases Programme grant from the European League Against Rheumatism and an unrestricted educational grant from Actelion Pharmaceuticals. 1 Svetlana I. Nihtyanova, MBBS, Benjamin E. Schreiber, MA, MRCP, Voon H. Ong, PhD, MRCP, J. Gerrard Coghlan, MD, FRCP, Christopher P. Denton, PhD, FRCP: University College London Medical School, Royal Free Hospital, London, UK; 2Daniel Rosenberg, PhD: Actelion Pharmaceuticals, Allschwil, Switzerland; 3 Pia Moinzadeh, MD: University of Cologne, Cologne, Germany; 4 Athol U. Wells, MD: Royal Brompton Hospital, London, UK. Dr. Schreiber has received speaking fees, honoraria for Advisory Board service, and/or travel grants from Actelion, GlaxoSmithKline, and Pfizer (less than $10,000 each). Dr. Rosenberg owns stock or stock options in Actelion. Dr. Denton has received consulting fees, speaking fees, and/or honoraria from Actelion, GlaxoSmithKline, Sanofi-Aventis, and Roche (less than $10,000 each). Address correspondence to Christopher P. Denton, PhD, FRCP, Experimental Rheumatology, Centre for Rheumatology and Connective Tissue Diseases, Royal Free Hospital, Pond Street, London NW3 2QG, UK. E-mail: [email protected]. Submitted for publication May 12, 2013; accepted in revised form January 28, 2014.

Systemic sclerosis (SSc) has a high burden of mortality and morbidity, and over recent decades pulmonary complications have emerged as the major disease-related cause of death (1,2). Evidence reported in the literature suggests that regular monitoring and early intervention may be associated with better outcome (3). Early identification of patients at high risk of organ complications and death is of particular interest in the context of new emerging therapies, such as autologous hematopoietic stem cell transplantation after intensive immunosuppression, which are associated with substantial treatment-related mortality but which may improve long-term event-free survival in an appropriately selected subgroup of patients (4). Severity of organ disease is well known to predict survival (5–8). Multiple studies have investigated predic1625

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tors of poor outcome in SSc patients either diagnosed as having or at risk of specific organ based complications (5–11), but very little has been published on prediction of organ complications in an unselected population of SSc patients. Although several recent reports describe disease characteristics in large unselected cohorts (12– 14), very few present long-term followup data (15,16). Likewise, the majority of proposed models for survival prediction are based on short-term followup of patients or are developed to predict vital status at specific time points, such as 2 or 5 years after disease onset (17–19). There is evidence that among patients who developed SSc in more recent years there has been improved survival and earlier detection of organ disease compared to historical cohorts (1,20). As a result, data from retrospective cohort studies that include patients who developed SSc more than 20 years ago are unlikely to produce results that accurately reflect contemporary disease course and outcome. In the present study we assessed survival and incidence of organ-based complications in a large singlecenter cohort of unselected SSc patients followed up for up to 15 years. We explored early predictors of survival and lung-based SSc complications and developed models for prediction of death, pulmonary fibrosis (PF), and pulmonary hypertension (PH). PATIENTS AND METHODS Patient cohort. The study cohort consisted of consecutive patients presenting to our center with disease onset between January 1995 and December 1999, allowing for a followup of up to 15 years from the time of SSc onset. All data were obtained from the Royal Free Hospital scleroderma research database and integrated records. We used the UK National Care Records Service to verify date of death, which allowed ascertainment of vital status for patients who were otherwise lost to followup. All patients being followed up in our center undergo pulmonary function tests (PFTs), echocardiography, electrocardiography, and blood tests as part of their routine care, either annually or more frequently if clinically indicated. Patients with new onset of shortness of breath or deterioration in existing shortness of breath as well as reduced forced vital capacity (FVC) or diffusing capacity for carbon monoxide (DLCO) (⬍80% predicted) undergo high-resolution computed tomography (CT) to evaluate for PF. Patients with echocardiography results suggestive of PH and anyone in whom there is unexplained shortness of breath and a clinical picture suggestive of PH is referred for right-sided heart catheterization (RHC). Disease subset was defined as limited cutaneous SSc (lcSSc) if skin thickening did not affect areas proximal to elbows and knees and as diffuse cutaneous SSc (dcSSc) if skin

thickening affected both distal and proximal areas. This classification was based on features observed over the entire disease course, and dcSSc was not reclassified to lcSSc even if skin thickening subsequently improved. We used consensus definitions of moderate-to-severe organ-based complications of SSc as outlined in previous reported studies (1). Thus, PF was confirmed by high-resolution CT, and clinically significant PF was defined as FVC or DLCO ⱕ55% predicted or a documented decline in FVC or DLCO of ⱖ15%. PH was defined as RHC with mean pulmonary artery pressure (PAP) of ⱖ25 mm Hg and normal pulmonary capillary wedge pressure. This included patients with pulmonary arterial hypertension (PAH) associated with connective tissue diseases and patients with PH associated with interstitial lung disease. Scleroderma renal crisis (SRC) was defined as new-onset systemic hypertension ⬎150/85 mm Hg and a documented decrease in estimated glomerular filtration rate of ⱖ30% or confirmed SRC features on renal biopsy. Cardiac involvement was defined as hemodynamically significant cardiac arrhythmias, pericardial effusion, or congestive heart failure requiring specific treatment in the absence of other known cardiac causes. SSc onset was defined as the time of the first non– Raynaud’s phenomenon manifestation of SSc that was reported by the patient or referring physician. Time to internal organ complications or death was calculated as the number of months between disease onset and the time point when a patient fulfilled the definition of significant organ-based complication. We recorded data on first available modified Rodnan skin thickness score (MRSS) (21), PFTs (FVC, DLCO, and DLCO corrected for alveolar volume [carbon monoxide transfer coefficient, or KCO], % of values predicted for age-, sex-, height-, and weight-matched healthy controls), results of blood tests (hemoglobin, erythrocyte sedimentation rate [ESR], creatinine, autoantibodies), proteinuria, Raynaud’s phenomenon severity grade, tendon friction rubs, esophageal involvement, and severe digital vasculopathy, including digital ulcers and/or gangrene. Statistical analysis. Two-sample t-test and Fisher’s exact test were used to compare demographic and serologic characteristics of the patients. A log rank test was used to compare Kaplan-Meier estimates of survival and time to significant organ-based complications. Survival probability at different time points was illustrated using Kaplan-Meier survival curves, while 1 ⫺ Kaplan-Meier estimates were used to report cumulative incidence of clinically significant organ complications. Standardized mortality ratios (SMRs) were calculated by dividing the number of observed deaths by the number of expected deaths. To build prediction models for pulmonary complications and survival, we used Cox regression analysis. Categorization of continuous variables with threshold analysis was done to make the models easier to apply in practice. Multiple random imputation of missing variables was performed. Details of the missing data analysis, SMR calculation, and prediction model building with threshold analysis are available at http://www.ucl.ac.uk/rheumatologyand-connective-tissue-diseases/publications. Statistical analyses were performed using IBM SPSS, version 21.0 and Stata, version 12.

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Table 1. Demographic and clinical characteristics of the patients with SSc*

Subset Male Caucasian Age at onset of Raynaud’s phenomenon, mean (95% CI) years Age at disease onset, mean (95% CI) years Disease duration at time of first assessment, mean (95% CI) months Duration of Raynaud’s phenomenon at time of disease onset, mean (95% CI) months Autoantibodies Anticentromere Anti–topoisomerase I Anti–RNA polymerase III Undefined autoantibodies Anti–U3 RNP Anti–U1 RNP Anti–PM-Scl Antinuclear antibody negative Organ-based complications developed during the entire observational period Death Clinically significant pulmonary fibrosis Pulmonary hypertension Cardiac scleroderma Scleroderma renal crisis Patients lost to followup, no. (%) At 5 years At 10 years

Patients with dcSSc

Patients with lcSSc

P

146 (36.7)/(31.9–41.4) 26 (17.8)/(11.6–24) 112 (80)/(73.4–86.6) 43 (41–45) 45 (43–47) 18 (16–20) 26 (13–38)

252 (63.3)/(58.6–68.1) 28 (11.1)/(7.2–15) 205 (84.4)/(79.8–88.9) 40 (38–42) 49 (48–51) 31 (27–36) 103 (85–122)

– 0.069 NS 0.098 0.003 ⬍0.001 ⬍0.001

1 (0.7)/(0–2.1) 40 (29)/(21.4–36.6) 28 (20.3)/(13.6–27) 28 (20.3)/(13.6–27) 16 (11.6)/(6.3–16.9) 7 (5.1)/(1.4–8.7) 7 (5.1)/(1.4–8.7) 7 (5.1)/(1.4–8.7)

103 (43.3)/(37–49.6) 40 (16.8)/(12.1–21.6) 3 (1.3)/(0–2.7) 39 (16.4)/(11.7–21.1) 7 (2.9)/(0.8–5.1) 17 (7.1)/(3.9–10.4) 5 (2.1)/(0.3–3.9) 6 (2.5)/(0.5–4.5)

⬍0.001 0.006 ⬍0.001 NS 0.001 NS NS NS

63 (43.2)/(35.1–51.2) 60 (41.7)/(33.6–49.7) 18 (12.3)/(7–17.7) 10 (6.8)/(2.8–10.9) 16 (11)/(5.9–16)

86 (34.1)/(28.3–40) 55 (21.8)/(16.4–26.6) 43 (17.1)/(12.4–21.7) 6 (2.4)/(0.5–4.3) 9 (3.6)/(1.3–5.9)

0.086 ⬍0.001 NS 0.035 0.005

10 (6.8) 22 (15.1)

6 (2.4) 28 (11.1)

0.035 NS

* Percentages are calculated as the proportion of patients for whom information was available. Data on ethnicity were missing for 15 patients, autoantibody specificities were not available for 22 patients, and information regarding the presence of clinically significant pulmonary fibrosis was missing for 2 patients. Except where indicated otherwise, values are the number (percent) of patients/(95% confidence interval [95% CI] of the percent). SSc ⫽ systemic sclerosis; dcSSc ⫽ diffuse cutaneous SSc; lcSSc ⫽ limited cutaneous SSc; NS ⫽ not significant.

RESULTS Demographic and clinical characteristics. A total of 398 SSc patients were included in this study, 146 with dcSSc and 252 with lcSSc. The demographic and clinical features of the patients are shown in Table 1. The incidence of clinically important cardiac complications was significantly higher among patients with dcSSc than among those with lcSSc (7% versus 1% at 5 years). While we did not observe any new cases of cardiac SSc among dcSSc patients after 5 years of followup, there were a few such cases among lcSSc patients, and those developed more than 10 years after disease onset. Nevertheless, over the entire followup period, the incidence of cardiac disease remained higher among dcSSc patients (7% versus 4%; P ⫽ 0.016). Similarly, the incidence of SRC was significantly higher among dcSSc patients (11% at 5 years and 12% at 10 years) than among lcSSc patients (3% at 5 years and 4% at 10 years) (P ⫽ 0.002). As previously observed, the annual rate of SRC development was highest in the first 3 years of disease, gradually declining thereafter, and the

longest disease duration at renal crisis we observed was 8 years. Survival. Survival from disease onset. Although in the first 5 years from SSc onset the survival rate was much lower for dcSSc patients than for lcSSc patients (85.5% versus 94%), over subsequent years annual survival rates became similar for the 2 subsets, suggesting that overall worse survival among dcSSc patients is mainly due to high early mortality. At 10 years and 15 years, survival was 81.7% and 69.2%, respectively, in lcSSc patients and 71.6% and 55.1%, respectively, in dcSSc patients (P ⫽ 0.017) (Figure 1A). There was no difference in overall survival between male and female patients during the first 5 years of followup (91% in both). Over subsequent years mortality rates among male patients were slightly higher, although the difference between the sexes did not reach statistical significance. At 10 years and 15 years, survival was 79.2% and 65.6%, respectively, among female patients and 70.4% and 56%, respectively, among male patients (P ⫽ 0.176). SMRs were calculated based on the

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Figure 1. A, Comparison of survival from disease onset among patients with limited cutaneous systemic sclerosis (lcSSc; L) and diffuse cutaneous SSc (dcSSc; D). B, Comparison of survival from disease onset among dcSSc and lcSSc patients with and those without clinically significant organ-based complications. C, Cumulative incidence of clinically significant pulmonary fibrosis in patients with lcSSc compared with that in those with dcSSc. D, Cumulative incidence of pulmonary hypertension in patients with lcSSc compared with that in those with dcSSc.

data from the first 13 years of followup (Table 2). As expected, dcSSc patients had a higher risk of death (SMRs 4.64 in male patients and 7.172 in female patients) compared to lcSSc patients (SMRs 2.055 in male patients and 3.127 in female patients). Interestingly, SMRs overall were higher in female patients than in

Table 2. SMRs based on the first 13 years of followup*

Male dcSSc lcSSc Total Female dcSSc lcSSc Total Total

SMR (95% CI)

P

4.64 (1.764–7.515) 2.055 (0.712–3.398) 2.907 (1.6–4.215)

⬍0.001 0.027 ⬍0.001

7.172 (4.95–9.395) 3.127 (2.315–3.939) 4.075 (3.264–4.885) 3.823 (3.127–4.519)

⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001

* SMR ⫽ standardized mortality ratio; 95% CI ⫽ 95% confidence interval; dcSSc ⫽ diffuse cutaneous systemic sclerosis; lcSSc ⫽ limited cutaneous SSc.

male patients (4.075 versus 2.907, for dcSSc and lcSSc patients combined). As expected, the presence of significant organbased complications was associated with a substantial increase in mortality in both the limited and the diffuse SSc subsets. In fact, survival rates were the same for both lcSSc and dcSSc patients with organ involvement (Figure 1B). Although dcSSc patients with no organ disease had similar mortality rates over the first 5 years of their disease compared to patients with organ involvement, later their annual mortality rates decreased, and toward the end of followup their survival was similar to that of lcSSc patients with no organ complications. Predicting survival. Table 3 summarizes the associations between disease characteristics and survival. The univariable analysis demonstrated that disease characteristics generally associated with extent of skin involvement (MRSS, subset, anticentromere antibody [ACA]), PFT results (FVC, DLCO, and KCO), age at disease onset, hemoglobin level, ESR, and serum creat-

PULMONARY COMPLICATIONS AND SURVIVAL IN SSc

Table 3. Prediction model for survival* Analysis, variable Univariable dcSSc MRSS Age at onset FVC DLCO KCO Hemoglobin ESR Creatinine ACA PF in first 3 years† PH in first 3 years† Cardiac SSc in first 3 years SRC in first 3 years† Multivariable dcSSc Age at onset DLCO Hemoglobin Creatinine PH in first 3 years† Cardiac SSc in first 3 years

HR (95% CI)

P

1.483 (1.071–2.054) 1.015 (1.000–1.029) 1.050 (1.034–1.065) 0.985 (0.975–0.995) 0.974 (0.964–0.985) 0.985 (0.971–0.999) 0.763 (0.664–0.876) 1.016 (1.008–1.025) 1.003 (1.002–1.004) 0.598 (0.393–0.911) 2.370 (1.625–3.455) 6.889 (3.193–14.861) 5.436 (2.396–12.332) 2.399 (1.382–4.162)

0.018 0.048 ⬍0.001 0.002 ⬍0.001 0.033 ⬍0.001 ⬍0.001 ⬍0.001 0.017 ⬍0.001 ⬍0.001 ⬍0.001 0.002

1.509 (1.043–2.182) 1.052 (1.036–1.068) 0.981 (0.970–0.991) 0.789 (0.674–0.925) 1.003 (1.001–1.004) 3.765 (1.583–8.955) 6.381 (2.739–14.866)

0.029 ⬍0.001 ⬍0.001 0.004 ⬍0.001 0.003 ⬍0.001

* HR ⫽ hazard ratio; 95% CI ⫽ 95% confidence interval; dcSSc ⫽ diffuse cutaneous systemic sclerosis; MRSS ⫽ modified Rodnan skin thickness score; FVC ⫽ forced vital capacity; DLCO ⫽ diffusing capacity for carbon monoxide; KCO ⫽ carbon monoxide transfer coefficient; ESR ⫽ erythrocyte sedimentation rate; ACA ⫽ anticentromere antibody; PF ⫽ pulmonary fibrosis; PH ⫽ pulmonary hypertension; SRC ⫽ scleroderma renal crisis. † Significant organ complication.

inine level strongly predicted survival. Analysis of the impact of different organ complications revealed that development of cardiac SSc increased the hazard of death more than 5-fold (hazard ratio [HR] 5.4), while development of PH within the first 3 years of SSc was associated with a nearly 7-fold greater hazard of death (HR 6.9) (Table 3). Clinically significant PF and renal crisis were also associated with worse survival (HR 2.4 for both). When included in a multivariable model, the covariates that remained significant predictors of higher mortality rates were greater age at disease onset, dcSSc subset, lower DLCO, lower hemoglobin level, higher serum creatinine level, and the presence of PH or cardiac involvement. Harrell’s c of the model was 0.75, indicating moderately good fit of the model. The model with categorical variables is described in Table 4. Rounded doubled ␤ values for each predictor were used as risk points, and using those as predictors of survival revealed 4 distinct risk groups. Patients at low risk had a total risk score of 0 or 1, and over the entire followup period 21% of them died (44 of 212). Of the patients

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with a risk score of 2 (patients at moderate risk), 43% died (42 of 97), and among those with a risk score of 3 or 4 (patients at high risk), 66% died by the end of followup (46 of 70). The group of patients with a risk score of ⬎4 was very small (n ⫽ 19), and 89% of them (17 patients) had died by the end of followup. The model using the risk score as a predictor had an area under the curve (AUC) of 0.73. PF. Cumulative incidence of clinically significant PF. PF was the most frequent organ complication in study patients. Of dcSSc patients, 68% had some degree of PF (96 of 142) compared to 44% of lcSSc patients (112 of 252) (P ⬍ 0.001). During followup, 42% of dcSSc patients and 22% of lcSSc patients developed clinically significant PF (P ⬍ 0.001) (Table 1). Approximately one-half of the patients developed clinically significant PF within the first 3 years of disease (Kaplan-Meier estimates 23% for dcSSc and 11% for lcSSc), and approximately three-fourths of the patients developed clinically significant PF within the first 5 years (34% for dcSSc and 16% for lcSSc) (Figure 1C). Information on smoking history was available for 343 patients. Eighty-six of those patients developed clinically significant PF at some point during their followup. There was a trend toward lower cumulative incidence of clinically significant PF among current smokers (13%) compared to past smokers and nonsmokers (30% each) (P ⫽ 0.065). Similarly, univariable Cox regression revealed only a trend in the association of smoking with development of clinically significant PF (HR 0.741, P ⫽ 0.061) in the complete case analysis, which disappeared after missing data imputation. Predicting clinically significant PF. In a univariable analysis, variables that significantly predicted development of clinically significant PF were male sex, dcSSc subset, MRSS, polymyositis/dermatomyositis overlap, PFT results (FVC, DLCO, and KCO), ACA, anti– topoisomerase I (anti–Scl-70), hemoglobin level, and ESR (Table 5). In the multivariable analysis, variables that remained significant predictors of clinically significant PF development were dcSSc subset, anti– topoisomerase I, lower FVC, and lower DLCO, while ACA was negatively associated with clinically significant PF. We forced age at disease onset into the multivariable model, as previous research has demonstrated an association between greater age at SSc onset and development of organ-based disease (5). Although it did not show significant association in the univariable analysis, greater age at SSc onset became a significant predictor of clinically significant PF and improved the fit of the final multivariable model. Proportional hazard assess-

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Table 4. Multivariable analysis with categorical variables and risk score points for survival, PF, and PH* Variable Survival dcSSc subset Age at onset ⬎60 years DLCO ⬍65% predicted Hemoglobin ⬍11.5 gm/dl Creatinine ⬎100 ␮moles/liter PH in first 3 years Cardiac SSc in first 3 years PF dcSSc Age at onset ⬎55 years FVC 65–80% predicted FVC ⬍65% predicted DLCO ⱕ55% predicted ACA Anti–topoisomerase I ⫻ time (4 years)† PH Age at onset ⬎55 years DLCO 55–65% predicted DLCO ⬍55% predicted Creatinine ⬎85 ␮moles/liter Anti–topoisomerase I Anti–RNA polymerase III Anti–U3 RNP SRC in first 5 years SRC in first 5 years ⫻ DLCO 55–65% predicted SRC in first 5 years ⫻ DLCO ⬍55% predicted

␤

HR (95% CI)

P

0.467 1.207 0.608 0.554 0.615 1.449 1.442

1.595 (1.123–2.266) 3.343 (2.272–4.919) 1.836 (1.249–2.699) 1.741 (1.102–2.750) 1.849 (1.150–2.972) 4.260 (1.822–9.960) 4.229 (1.807–9.895)

0.009 ⬍0.001 0.002 0.018 0.011 0.001 0.001

1 2 1 1 1 3 3

0.526 0.306 0.542 1.157 1.108 ⫺1.728 0.566

1.692 (1.052–2.721) 1.358 (0.866–2.127) 1.719 (1.005–2.939) 3.180 (1.765–5.727) 3.027 (1.752–5.231) 0.178 (0.061–0.519) 1.761 (1.227–2.527)

0.030 0.182 0.048 ⬍0.001 ⬍0.001 0.002 0.002

1 1 1 2 2 ⫺3 1

0.565 1.247 2.492 0.430 ⫺0.926 0.681 1.155 1.541 ⫺1.526 ⫺2.754

1.759 (0.991–3.124) 3.481 (1.161–10.442) 12.089 (4.591–31.83) 1.538 (0.805–2.939) 0.396 (0.172–0.915) 1.975 (0.839–4.649) 3.175 (1.192–8.454) 4.668 (0.847–25.715) 0.217 (0.015–3.097) 0.064 (0.006–0.674)

0.054 0.026 ⬍0.001 0.192 0.030 0.119 0.021 0.077 0.260 0.022

1 1 2 0 ⫺1 1 1 2 ⫺2 ⫺3

Points

* PF ⫽ pulmonary fibrosis; PH ⫽ pulmonary hypertension; HR ⫽ hazard ratio; 95% CI ⫽ 95% confidence interval; dcSSc ⫽ diffuse cutaneous systemic sclerosis; DLCO ⫽ diffusing capacity for carbon monoxide; FVC ⫽ forced vital capacity; ACA ⫽ anticentromere antibody; SRC ⫽ scleroderma renal crisis. † Interaction term of anti–topoisomerase I with time in years. One risk point is scored for every 4 years of followup for anti–topoisomerase I–positive patients.

ment demonstrated that anti–topoisomerase I was time dependent; therefore, in the final model we used an interaction term of anti–topoisomerase I and disease duration in years. The final model using categorized variables is shown in Table 4. Rounded doubled ␤ values were used as risk points. The interaction of anti–topoisomerase I and disease duration in years had ␤ of 0.141; therefore, it could contribute a risk point for approximately every 4 years of followup (␤ ⫽ 0.566). Based on risk scores, patients were divided into 4 groups. Those with negative risk scores were unlikely to develop clinically significant PF; 1 of these 96 patients (1%) developed clinically significant PF over the entire followup period. Those with risk scores of 0 or 1 were at low risk; 18 of these 114 patients (16%) developed clinically significant PF. Finally, those with risk scores of 2 were at moderate risk; 16 of these 52 patients (31%) developed clinically significant PF. Patients with risk scores of ⱖ3 were at high risk of clinically significant PF; 80 of these 134 patients (60%) developed clinically significant PF by the end of

followup. The AUC for the risk score predictive model was 0.81. PH. Cumulative incidence of PH. The earliest cases of PH were observed within the first 3 years from SSc onset, and subsequent incidence rates were 1–2% per year, remaining unchanged over the followup period (Figure 1D). At 5 years 5% of lcSSc patients and 4% of dcSSc patients had developed PH, while at 10 years the cumulative incidence was 15% for lcSSc patients and 13% for dcSSc patients. Over the entire followup period, 24% of lcSSc patients and 18% of dcSSc patients developed PH (P ⫽ 0.558). Sixteen of the 43 lcSSc patients who developed PH over the entire followup period (37%) had coexisting clinically significant PF, compared to 8 of 18 dcSSc patients (44%) (P ⫽ 0.774). The cumulative incidences of PAH at 5, 10, and 15 years of followup for the entire cohort were 2%, 9%, and 15%, respectively, while the cumulative incidences of PH in combination with clinically significant PF were 2%, 6%, and 9%, respectively. There was no significant difference in the cumulative incidence of PAH or clinically signif-

PULMONARY COMPLICATIONS AND SURVIVAL IN SSc

Table 5. Prediction models for PF and PH* Analysis, variable PF Univariable dcSSc Male PM/DM overlap FVC DLCO KCO Hemoglobin ESR MRSS ACA Anti–topoisomerase I Multivariable dcSSc Age at onset FVC DLCO ACA Anti–topoisomerase I ⫻ time (years)† PH Univariable Age at onset FVC DLCO KCO FVC:DLCO Proteinuria Creatinine Anti–U3 RNP Anti–topoisomerase I Multivariable Age at onset DLCO Creatinine Anti–topoisomerase I Anti–RNA polymerase III Anti–U3 RNP SRC in first 5 years DLCO ⫻ SRC in first 5 years

HR (95% CI)

P

2.424 (1.681–3.496) 1.994 (1.283–3.099) 1.996 (1.217–3.275) 0.955 (0.941–0.969) 0.952 (0.937–0.967) 0.975 (0.960–0.990) 0.853 (0.735–0.989) 1.017 (1.008–1.026) 1.030 (1.015–1.045) 0.074 (0.027–0.201) 3.285 (2.251–4.794)

⬍0.001 0.002 0.006 ⬍0.001 ⬍0.001 0.002 0.036 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001

1.774 (1.069–2.941) 1.020 (1.002–1.039) 0.970 (0.952–0.989) 0.974 (0.955–0.992) 0.194 (0.065–0.576) 1.160 (1.060–1.269)

0.027 0.031 0.003 0.006 0.003 0.001

1.031 (1.009–1.054) 0.967 (0.954–0.980) 0.950 (0.934–0.966) 0.952 (0.930–0.975) 2.524 (1.406–4.532) 2.622 (1.094–6.287) 1.003 (1.001–1.005) 2.543 (1.145–5.650) 0.488 (0.221–1.080)

0.006 ⬍0.001 ⬍0.001 ⬍0.001 0.002 0.031 0.001 0.022 0.077

1.029 (1.004–1.056) 0.939 (0.920–0.959) 1.004 (1.000–1.007) 0.409 (0.174–0.959) 3.226 (1.177–8.843) 3.922 (1.285–11.969) 0.004 (0.000–0.248) 1.082 (1.013–1.156)

0.026 ⬍0.001 0.026 0.040 0.023 0.017 0.009 0.019

* PF ⫽ pulmonary fibrosis; PH ⫽ pulmonary hypertension; HR ⫽ hazard ratio; 95% CI ⫽ 95% confidence interval; dcSSc ⫽ diffuse cutaneous systemic sclerosis; PM/DM ⫽ polymyositis/dermatomyositis; FVC ⫽ forced vital capacity; DLCO ⫽ diffusing capacity for carbon monoxide; KCO ⫽ carbon monoxide transfer coefficient; ESR ⫽ erythrocyte sedimentation rate; MRSS ⫽ modified Rodnan skin thickness score; ACA ⫽ anticentromere antibody; SRC ⫽ scleroderma renal crisis. † Interaction term of anti–topoisomerase I with time in years.

icant PF–associated PH between patients with dcSSc and those with lcSSc. At the end of followup 10% of dcSSc patients and 17% of lcSSc patients had developed PAH (P ⫽ 0.464), and 9% of patients in both subsets had developed clinically significant PF–associated PH (P ⫽ 0.978). Kaplan-Meier curves are available at http:// www.ucl.ac.uk/rheumatology-and-connective-tissuediseases/publications.

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Predicting PH. For the PH prediction model we initially tested all predictor variables separately. The ones that demonstrated significant association were age at disease onset, PFT results (FVC, DLCO, KCO, and FVC:DLCO ratio), autoantibodies (anti–topoisomerase I and anti–U3 RNP [antifibrillarin]), proteinuria, and serum creatinine levels, and all of those were included in the multivariable analysis (Table 5). Because of the significant associations we observed between the markers of renal function and PH in the univariable analysis, anti–RNA polymerase III and SRC (presence or absence within the first 5 years of disease) were forced into the multivariable model to check for significant associations and interactions. Our final multivariable predictive model for PH included greater age at SSc onset, lower DLCO, higher serum creatinine level, anti–RNA polymerase III, and anti–U3 RNP as positive predictors, while the presence of anti–topoisomerase I reduced the hazard of PH. In addition, there was one significant interaction in the model between SRC and DLCO; therefore, SRC was also left in the model. Harrell’s c of the model was 0.83, indicating a very good fit. Details of the model using categorical variables are presented in Table 4. We used the rounded ␤ values for each predictor variable category from the Cox regression as risk score points. Even at the greatest separation point, creatinine had a relatively small ␤ value, which made its contribution insignificant. Based on risk score, we identified 4 distinct risk groups. Only 2 of the 128 patients (2%) with a risk score of ⫺1 or 0 developed PH over the entire followup period. The 142 patients with a score of 1 were at low risk of PH, and 15 of them (11%) developed it by the end of followup. Of the 81 patients with a score of 2 (the group at moderate risk), 23 (28%) developed PH, and of the 47 patients with a score of ⱖ3, 21 (45%) developed PH. The model using risk scores had an AUC of 0.79. DISCUSSION We report cumulative frequency of moderate-tosevere organ-based complications and survival in a cohort of 398 SSc patients followed up for up to 15 years. We also present predictive models for pulmonary complications and survival derived in this unselected population of SSc patients. The data reflect contemporary outcomes in SSc. In a complementary recent article reporting a study using independent cohorts, we highlighted improved survival among SSc patients seen in recent years, compared to those seen a decade ago (3). Comparisons

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between the survival and cumulative incidence of organ complications in patients in our 2010 study and patients in the current study show no significant differences, confirming that our findings apply to contemporary SSc cohorts. The majority of patients in the current study were followed up for at least 10 years, and a significant proportion of them were followed up for up to 15 years, allowing for more accurate estimates of long-term disease course and incidence of organ complications. Comparison of SMRs in this cohort and the findings of an analysis, by Bryan et al (22), of patients from our center seen between 1982 and 1992 confirms that the improvement in survival among our patients seen in the current era is not related only to the improving survival in the general population (20). Overall SMR in the older cohort was 4.054 female patients compared to 3.825 in our group. Similar to the findings of Bryan et al, we also observed a higher SMR in female patients compared to male patients, suggesting that the trend toward worse survival among male patients is due to higher male mortality in the general population, while after development of SSc the risk of death is much more increased for women compared to men. Multiple studies have shown that patients with dcSSc have much higher mortality than patients with lcSSc (20,23–28). We confirmed this, showing a significantly worse Kaplan-Meier survival estimate over the first 5 years of the disease among dcSSc patients. Nevertheless, after 5 years mortality rates converge in the 2 subsets, and the trend remains similar over the subsequent years (Figure 1A). This suggests that the difference in survival between the 2 subsets is mainly due to early increased mortality among dcSSc patients, and it also supports the findings of a recent large meta-analysis which show that over recent years the difference in survival between patients with lcSSc and patients with dcSSc is becoming smaller (20). This becomes even more apparent when comparing survival within the 2 subsets between subgroups of patients with and patients without any significant organ complications (Figure 1B). Authors disagree on whether SSc onset should be considered as the onset of Raynaud’s phenomenon or as the onset of the first non–Raynaud’s phenomenon symptom (29). In the present study, we defined disease onset as the onset of the first non–Raynaud’s phenomenon symptom, to better permit comparison of organ-based disease progression between dcSSc and lcSSc. However, duration of Raynaud’s phenomenon prior to SSc onset was recorded and included in the Cox analysis as one of the potential predictor variables. We found no associations between the duration of Raynaud’s phenomenon

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prior to SSc onset and either survival or development of pulmonary complications. In the final model disease subset and the MRSS were interchangeable, and disease subset was chosen as the easier variable to assess. Published reports suggest that increased ESR is associated with worse prognosis (24,25,30,31). Nevertheless, in the current study, although ESR was significantly associated with survival in the univariable analysis, it dropped out of the final model. Identifying patients at particular risk of organ disease and monitoring them more regularly are of great importance. As clinically significant cardiac complications are comparatively rare and SRC often develops very early in the disease course, frequently being the presenting feature in newly developed cases (3), we focused on pulmonary complications of SSc, which are a major contributor to SSc-related death (1,20). Fifty-two percent of our patients had some degree of PF, similar to the percentage in other reported SSc cohorts (32). As previously demonstrated (3), clinically significant PF was twice as common among dcSSc patients as among lcSSc patients. Nevertheless, in both lcSSc patients and dcSSc patients it developed mostly within the first 5 years of disease. On the other hand, PH had a comparatively constant yearly incidence rate, and although it is generally believed to be a late complication of SSc, the earliest cases we observed developed within 3 years of disease onset. In addition, our findings contradict the commonly accepted association between lcSSc and PH, showing no difference in Kaplan-Meier estimates of the cumulative incidence of PH in dcSSc patients and lcSSc patients. We present novel models for prediction of lung complications in an unselected SSc population, using routinely assessed and readily available clinical and laboratory variables, reflecting real-life patient care. As expected, the strongest predictors of clinically significant PF were PFT results (FVC and DLCO) (2) and autoantibody specificities. In particular, as previously shown (33–38), ACA positivity was strongly protective, while the presence of anti–topoisomerase I significantly increased the risk of clinically significant PF. The model predicting development of PH is of particular interest. Although multiple studies have evaluated predictors of PH, many either use poorly characterized cohorts in which PH has not been confirmed by RHC or are based on enriched populations in which PH is clinically suspected, which makes their results not easily generalizable (39,40). Studies in unselected cohorts that do use RHC to confirm PH diagnosis have

PULMONARY COMPLICATIONS AND SURVIVAL IN SSc

demonstrated that age, FVC, DLCO, KCO, N-terminal pro–brain natriuretic peptide, and estimated systolic PAP can be used as predictors of development of PH (10,41–43). Our findings confirm previously observed association between PH and DLCO values. Nevertheless, although the FVC:DLCO ratio and KCO were significantly associated with PH in the univariable analysis, they dropped out of the final multivariable model. We also confirm the association of anti–U3 RNP antibodies and PH (44,45), but we show that in a multivariable analysis, anti–RNA polymerase III is also an independent predictor of PH. In addition, we could not find any association between ACA and PH in either univariable or multivariable analysis, which is consistent with findings in other reported well-characterized unselected cohorts (10) while contrasting with the findings of studies generally based on enriched populations (46,47) or using only echocardiography for PH diagnosis (48). This indicates that in an unselected population of SSc patients, positivity for this antibody does not confer increased risk of PH. Further analysis (available at http://www.ucl. ac.uk/rheumatology-and-connective-tissue-diseases/ publications) confirmed that even when PH patients are separated into those with PAH alone and those with clinically significant PF–associated PH, antibody associations remained similar to those observed in the main analysis, and ACA showed no association with PAH, while it had a protective role in relation to clinically significant PF–associated PH. Limitations of this study include the retrospective analysis and the observational nature of our cohort. This led to the issue of missing data, which we have addressed thoroughly using a valid statistical approach. Despite the fact that the overall followup of the cohort was 15 years, the numbers of patients at risk after 10 years of followup fell significantly due to patients reaching the event of interest, being censored due to competing risks, or being lost to followup. Nevertheless, the proportion of patients lost to followup at 10 years was relatively small—15% in the dcSSc subset and 11% in the lcSSc subset. In addition, cases seen in a tertiary referral center may not be reflective of those seen in other centers and are likely to be more severe, with a higher frequency of dcSSc and organ-based complications. There are also major limitations in terms of the assessments that are directed by clinical practice and that do not follow a study protocol. Some important complications are not ascertained, for example, gastrointestinal tract involvement. We also have not ascertained the disease burden of nonlethal manifestations such as digital ulceration and impacts on

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quality of life and economic and social well-being that are increasingly recognized as important (49,50). Although the prediction rules may potentially be useful tools for cohort enrichment and could aid development of inclusion criteria for treatment trials, they will require validation in order to be used reliably in clinical practice. The clinical usefulness of the prediction rules will need to be examined in the future through prospective studies involving unselected SSc patients. Strengths of this study include the real-life nature of the patient series and the single-center assessment with a limited number of observers and reasonable uniformity in assessment techniques. The most important aspect of the study is the delineation of the timing and frequency of major organ-based complications of SSc over a 15-year period, which the study defines in a much more robust way than most recently reported studies. In conclusion, our study provides valuable reallife insight into the long-term outcome of SSc, the timing and frequency of major complications, and the impact of these complications on outcome. Our survival and lung complication predictive models could be used as a clinical tool for patient risk stratification. They also provide a reference point for event-driven interventional clinical trials and potentially facilitate cohort enrichment in such studies. Our results disprove some commonly accepted assumptions based on selected populations, demonstrating the importance of cohort representativeness for the applicability of study results to a general population of SSc patients. AUTHOR CONTRIBUTIONS 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. Denton 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. Nihtyanova, Ong, Moinzadeh, Coghlan, Denton. Acquisition of data. Nihtyanova, Schreiber, Ong, Moinzadeh, Coghlan, Denton. Analysis and interpretation of data. Nihtyanova, Schreiber, Ong, Rosenberg, Moinzadeh, Coghlan, Wells, Denton.

ROLE OF THE STUDY SPONSOR Actelion Pharmaceuticals had no role in the study design or in the collection or analysis of the data or in the decision to submit the manuscript for publication. Publication of this article was not contingent upon approval by Actelion Pharmaceuticals.

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