# A comparative analysis of risk stratification tools in systemic sclerosis-associated pulmonary arterial hypertension: a EUSTAR analysis.

H. J. Bjørkekjær, C. Bruni, K. Broch, C. Brunborg, P. E. Carreira, P. Airò, C.

P. Simeón-Aznar, M. E. Truchetet, A. Giollo, A. Balbir-Gurman, et al.

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## ► To cite this version:

H. J. Bjørkekjær, C. Bruni, K. Broch, C. Brunborg, P. E. Carreira, et al.. A comparative analysis of risk stratification tools in systemic sclerosis-associated pulmonary arterial hypertension: a EUSTAR analysis.. Rheumatology, 2025, Rheumatology (Oxford), 64 (6), p.3643-3656. 10.1093/rheumatology/keaf053. hal-05325776

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Clinical science

# A comparative analysis of risk stratification tools in SSc-associated pulmonary arterial hypertension: a EUSTAR analysis

Hilde Jenssen Bjørkekjær ID<fnref n="1" />,<fnref n="2" />, Cosimo Bruni ID<fnref n="3" />,<fnref n="4" />, Kaspar Broch ID<fnref n="5" />,<fnref n="6" />, Cathrine Brunborg ID<fnref n="2" />,<fnref n="7" />,
Patricia E. Carreira ID<fnref n="8" />, Paolo Airò ID<fnref n="9" />, Carmen Pilar Simeón-Aznar ID<fnref n="10" />, Marie-Elise Truchetet ID<fnref n="11" />,
Alessandro Giollo ID<fnref n="12" />,<fnref n="13" />, Alexandra Balbir-Gurman ID<fnref n="14" />, Mickael Martin ID<fnref n="15" />, Christopher P. Denton ID<fnref n="16" />,
Armando Gabrielli ID<fnref n="17" />, Francesco Del Galdo ID<fnref n="18" />,<fnref n="19" />, Madelon C. Vonk ID<fnref n="20" />, Håvard Fretheim ID<fnref n="2" />,<fnref n="21" />,
Helle Bitter ID<fnref n="1" />, Øyvind Midtvedt<fnref n="22" />, Arne Andreassen<fnref n="5" />, Sverre Høie<fnref n="2" />,<fnref n="23" />, Yoshiya Tanaka ID<fnref n="24" />,
Gabriela Riemekasten ID<fnref n="25" />, Ulf Müller-Ladner ID<fnref n="26" />, Marco Matucci-Cerinic ID<fnref n="27" />,<fnref n="28" />,
Ivan Castellví ID<fnref n="29" />, Elise Siegert ID<fnref n="30" />, Eric Hachulla ID<fnref n="31" />, Øyvind Molberg ID<fnref n="2" />,<fnref n="22" />, Oliver Distler ID<fnref n="3" />,
Anna-Maria Hoffmann-Vold ID<fnref n="3" />,<fnref n="22" />,*, on behalf of the EUSTAR Collaborators#

<footnote n="1">¹Department of Rheumatology, Hospital of Southern Norway, Kristiansand, Norway</footnote>

<footnote n="2">²Institute of Clinical Medicine, University of Oslo, Oslo, Norway</footnote>

<footnote n="3">³Department of Rheumatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland</footnote>

<footnote n="4">⁴Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy</footnote>

<footnote n="5">⁵Department of Cardiology, Oslo University Hospital, Rikshospitalet, Oslo, Norway</footnote>

<footnote n="6">⁶Institute for Experimental Medical Research, KG Jebsen Center, University of Oslo, Oslo, Norway</footnote>

<footnote n="7">⁷Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital—Rikshospitalet, Oslo, Norway</footnote>

<footnote n="8">⁸Department of Rheumatology, 12 de Octubre University Hospital, Madrid, Spain</footnote>

<footnote n="9">⁹UOC Reumatologia ed Immunologia Clinica, 9 Spedali Civili di Brescia, Scleroderma UNIT, Brescia, Italy</footnote>

<footnote n="10">¹⁰Systemic Autoimmune Diseases Unit, Department of Internal Medicine, University Vall d'Hebron Hospital, Barcelona, Spain</footnote>

<footnote n="11">¹¹Rheumatology Department, Bordeaux University Hospital, Bordeaux, France</footnote>

<footnote n="12">¹²Division of Rheumatology, Department of Medicine—DIMED, University and Hospital of Padua, Padova, Italy</footnote>

<footnote n="13">¹³Rheumatology Section, Department of Medicine, University of Verona, Verona, Italy</footnote>

<footnote n="14">¹⁴Rappaport Faculty of Medicine, Rheumatology Institute, Rambam Health Care Campus, Technion-Institute of Technology, Haifa, Israel</footnote>

<footnote n="15">¹⁵Department of Internal Medicine, Poitiers University Hospital, Poitiers, France</footnote>

<footnote n="16">¹⁶Centre for Rheumatology and Connective Tissue Diseases, University College London Division of Medicine and Royal Free Hospital, London, UK</footnote>

<footnote n="17">¹⁷Fondazione di Medicina Molecolare e Terapia Cellulare, Università Politecnica delle Marche, Ancona, Italy</footnote>

<footnote n="18">¹⁸Leeds Institute of Rheumatic and Musculoskeletal Medicine, LIRMM, Leeds, UK</footnote>

<footnote n="19">¹⁹NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals Trust, Leeds, UK</footnote>

<footnote n="20">²⁰Department of Rheumatology, Radboud Universiteit, Nijmegen, Netherlands</footnote>

<footnote n="21">²¹Lillehammer Hospital for Rheumatic Diseases, Lillehammer, Norway</footnote>

<footnote n="22">²²Department of Rheumatology, Oslo University Hospital, Rikshospitalet, Oslo, Norway</footnote>

<footnote n="23">²³Department of Cardiology, Hospital of Southern Norway, Arendal, Norway</footnote>

<footnote n="24">²⁴First Department of Internal Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan</footnote>

<footnote n="25">²⁵Department of Rheumatology and Clinical Immunology, University of Lübeck, Lübeck, Germany</footnote>

<footnote n="26">²⁶Department of Rheumatology and Clinical Immunology, Justus-Liebig University Giessen, Campus Kerckhoff, Bad Nauheim, Germany</footnote>

<footnote n="27">²⁷Unit of Immunology, Rheumatology, Allergy and Rare Diseases (UnIRAR) & Inflammation, fibrosis and ageing initiative (INFLAGE), IRCCS San Raffaele Hospital, Milan, Italy</footnote>

<footnote n="28">²⁸University Vita Salute San Raffaele, Milano, Italy</footnote>

<footnote n="29">²⁹Department of Rheumatology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain</footnote>

<footnote n="30">³⁰Rheumatology, Charite University Hospital, Berlin, Germany</footnote>

<footnote n="31">³¹Department of Internal Medicine and Clinical Immunology, Referral Centre for Centre for Rare Systemic Autoimmune Diseases North of France, North-West, Mediterranean and Guadeloupe (CeRAINOM), CHU Lille, Univ. Lille, Inserm, U1286 – INFINITE – Institute for Translational Research in Inflammation, Lille, France</footnote>

<footnote>*Correspondence to: Anna-Maria Hoffmann-Vold, Department of Rheumatology, Oslo University Hospital—Rikshospitalet, Pb 4950 Nydalen, 0424 Oslo, Norway. E-mail: a.m.hoffmann-vold@medisin.uio.no</footnote>

<footnote>#The EUSTAR Collaborators are listed in the acknowledgements and supplementary material.</footnote>
## Abstract

**Objectives:** The 2022 European Society of Cardiology and European Respiratory Society (ESC/ERS) guidelines for pulmonary arterial hypertension (PAH) recommend risk stratification to optimize management. However, the performance of generic PAH risk stratification tools in patients with SSc-associated PAH remains unclear. Our objective was to identify the most accurate approach for risk stratification at SSc-PAH diagnosis.

**Methods:** In this multicentre, international cohort study from the European Scleroderma Trials and Research (EUSTAR) group database, we screened 11 risk stratification tools upon SSc-PAH diagnosis. We compared the performance of the three top-ranked tools to predict mortality with the ESC/ERS three-strata model, the currently recommended tool for baseline risk assessment. We also assessed the impact of incorporating SSc-specific characteristics into the tools. Kaplan-Meier analyses and Cox regression with area under the ROC curve (AUC) were conducted.

**Results:** The ESC/ERS three-strata model had a lower ability to predict mortality than the ESC/ERS four-strata model, 'SPAHR updated' and 'REVEAL Lite 2'. The ESC/ERS four-strata model divided 'intermediate-risk' patients into two groups with significantly different long-term survival rates and is the easiest applicable tool. Incorporating SSc-specific characteristics did not significantly improve the predictive ability of any model, but a low diffusing capacity of the lung for carbon monoxide (DLCO) was an independent predictor of mortality.

**Conclusion:** Considering its ability to predict mortality, risk segregation capabilities and clinical applicability, this study provides a rationale for using the simplified ESC/ERS four-strata model at SSc-PAH diagnosis as an alternative to the comprehensive ESC/ERS three-strata model. We propose considering DLCO as an individual prognostic marker in SSc-PAH.

## Graphical abstract

<figure><img src="image_3.png" /></figure>

**Keywords:** observational study, pulmonary arterial hypertension, risk stratification, systemic sclerosis.

## Rheumatology key messages

* The ESC/ERS four-strata model showed superior mortality prediction, risk segregation and applicability at SSc-PAH diagnosis.

* Incorporating SSc-specific characteristics did not improve predictive accuracy, but DLCO was an independent prognostic marker.

* Risk stratification was accurate in all SSc-PAH patients, regardless of pre-existing vascular-targeted therapies and haemodynamic thresholds.
## Introduction

Pulmonary arterial hypertension (PAH) develops in 6–12% of patients with SSc [1–3]. Despite often presenting with milder haemodynamic impairment, patients with SSc-PAH have a worse prognosis and respond less favourably to treatment compared with those with idiopathic PAH (IPAH) [4–7]. This may be attributed to the heterogeneity and complexity of SSc, including diverse pathogenic mechanisms and systemic organ involvement, which may lead to multiple mechanisms contributing to pulmonary hypertension [6, 8–10]. In recent years, studies suggest an improvement in the survival of patients diagnosed with SSc-PAH [11, 12], possibly due to enhanced screening, earlier diagnosis and novel treatment strategies [11–18].

The 2022 European Society of Cardiology and European Respiratory Society (ESC/ERS) guidelines for PAH, along with the updates from the Seventh World Symposium on Pulmonary Hypertension (WSPH), recommend risk stratification to predict mortality risk and guide treatment decisions [19, 20]. To assess the baseline risk, the guidelines recommend the comprehensive ESC/ERS three-strata model, which combines up to 18 risk parameters to define low-, intermediate- or high-risk status with estimated 1-year mortality rates of <5%, 5–20% and >20%, respectively [19]. At follow-up, a simplified four-strata model based on WHO functional class (WHO-FC), 6-min walk distance (6MWD) and brain natriuretic peptide (BNP) or N-terminal (NT)-proBNP is recommended [19, 21]. Several other risk stratification tools have been proposed [21–29], predominantly developed using data from patients with IPAH, thus not considering the distinctive characteristics of SSc-PAH, such as multiorgan involvement and potential unique prognostic markers.

Our objective was to identify the most accurate approach for risk stratification in SSc-PAH at the time of diagnosis by comparing various tools to the ESC/ERS three-strata model, the currently recommended tool for baseline risk assessment, and to assess the impact of incorporating SSc-specific characteristics to improve the accuracy of these tools.

## Study design and methods

### Study design

This multicentre, international cohort study included all SSc-PAH patients in the European Scleroderma Trials and Research (EUSTAR) database with right heart catheterizations (RHCs) and annual prospective data, extracted on 1 April 2022. Additional data were collected via specific case report forms through direct contact with the centres. The database structure has been previously described [30]. The project was approved by the EUSTAR board (project number: CP122). The study complies with the Declaration of Helsinki. Each participating centre obtained approval from the local ethics committee. The coordinating centre's protocol was approved by the Regional Committees for Medical and Health Research Ethics (REK) in Norway (approval number: 273870).

### Study subjects and inclusion criteria

We assessed patients who had at least one RHC between 2001 and 2021 and met the following criteria: (i) 2022 haemodynamic definition of PAH (mean pulmonary arterial pressure [mPAP] >20 mmHg, pulmonary artery wedge pressure [PAWP] ≤15 mmHg and pulmonary vascular resistance

[PVR] >2 Wood Units [WUs]) [19]; (ii) age ≥18 years and (iii) 2013 ACR/EULAR SSc classification criteria [31]. Patients with severe interstitial lung disease (ILD), defined as an extent of ILD on high-resolution computed tomography (HRCT) >20% or a forced vital capacity (FVC) <70% in the presence of ILD, without available quantification, were excluded [32]. We recorded demographic and clinical characteristics at RHC. SSc disease duration was defined as the time from the first non-Raynaud symptom to RHC. Treatment-naïve status was defined as no pre-existing therapies targeting vascular symptoms such as Raynaud phenomenon or digital ulcers (DUs) (e.g. endothelin receptor antagonists [ERAs], phosphodiesterase-5 inhibitors [PDE-5is] or prostacyclin pathway agents [PPAs]). Initial treatment strategies were defined as (i) upfront monotherapy with ERA, PDE-5i (including soluble guanylate cyclase stimulator), or PPA; or (ii) upfront dual or triple combination therapy with these drugs within four months of PAH diagnosis. Higher and lower mPAP and PVR threshold groups were defined according to the 2015 and 2022 haemodynamic criteria: mPAP ≥25 mmHg and PVR >3 WU, and mPAP 21–24 mmHg or PVR 2–3 WU, respectively.

### Outcomes

The primary outcome was all-cause mortality, defined from the date of SSc-PAH diagnosis by RHC until death, or the censoring date (lung transplantation or study end, defined as the date last known to be alive). We conducted a two-stage evaluation of generic PAH risk stratification tools, using the ESC/ERS three-strata model as the reference. First, we ranked these tools based on their applicability and performance in predicting mortality in the SSc-PAH cohort. To maintain a clear and focused analysis, we selected the three top performing tools for comparison against the reference (Fig. 1). We followed the ESC/ERS three-strata model guidelines, incorporating as many risk parameters as possible, including at least WHO-FC or 6MWD and BNP or NT-proBNP [19]. In the absence of a validated method for calculating a risk score, we applied an approach proposed by previous studies, assigning scores to parameters based on cut-off thresholds provided in the guidelines, with the mean score determining the risk category: <1.50 (low risk), 1.50–2.49 (intermediate risk) and ≥2.50 (high risk) [27, 28].

We evaluated the distribution of risk groups and compared observed 1-year mortality with expected mortality as estimated by the guidelines. We assessed transplant-free survival (TFS) by risk groups and compared each tool's ability to predict all-cause mortality against the ESC/ERS three-strata reference tool. Finally, we tested the impact of incorporating SSc-specific characteristics into the risk stratification tools, including predictors of outcomes in SSc, based on previous studies and expert opinions of the co-authors [32–40]. The final covariates for the multivariable models were selected through an evaluation of variable availability, multicollinearity, and model performance.

The outcome was assessed in all SSc-PAH patients and in predefined subgroup analyses: PAH treatment-naïve patients, patients categorized by haemodynamic thresholds and those meeting all risk stratification tool criteria.

### Statistics

Statistical analyses were performed with IBM SPSS, version 29, and STATA, version 18. Categorical variables were
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Description of the risk stratification tools and calculation of risk scores. ESC/ERS: European Society of Cardiology and European Respiratory Society; SPAHR: Swedish Pulmonary Arterial Hypertension Registry; REVEAL: Registry to Evaluate Early and Long-Term PAH Disease Management; WHO-FC: World Health Organization functional class; 6MWD: 6-min walk distance; NT-proBNP: N-terminal brain natriuretic peptide; SvO₂: mixed-venous oxygen saturation; VO₂: oxygen uptake; VE/VCO₂: ventilatory equivalents for carbon dioxide; TAPSE/sPAP: tricuspid annular plane systolic excursion/ systolic pulmonary artery pressure; cMRI: cardiac magnetic resonance imaging; RVEF: right ventricular ejection fraction; SVI: stroke volume index; RVESVI: right ventricular end-systolic volume index; eGFR: estimated glomerular filtration rate. The figure was created using BioRender.com</figcaption></figure>

compared using Pearson $\chi^2$ or Fisher exact test, and continuous variables with independent sample *t* test or Mann-Whitney *U* test, as appropriate. TFS was evaluated using Kaplan-Meier analysis and the log-rank test. Univariable and multivariable Cox regression models assessed the risk stratification tools' ability to predict all-cause mortality, presenting hazard ratios (HRs) and 95% CIs. Multicollinearity was evaluated using Pearson's and Spearman's correlation coefficients, with a cut-off of $\ge$0.7. Multivariable models required 10 outcome events per covariate. The predictive ability of the tools was compared using area under the ROC curve (AUC).

Sensitivity analyses were performed with multiple imputations for missing covariates in the multivariable regression model, except for the risk parameters, which were treated as the exposure variable in the analyses. Under the assumption of missing at random, 40 imputed datasets were generated using the multiple imputation chained procedure in STATA. Multivariable regression analyses were repeated across these datasets, with results pooled using Rubin's rules.

# Results

## Baseline characteristics

Of the 889 SSc patients in the EUSTAR database with RHC, 429 SSc-PAH patients from 43 centres were eligible (Supplementary Fig. S1, available at *Rheumatology* online). Among these, 288 (67%) were treatment-naïve, and 141 (33%) had pre-existing vascular-targeted therapies (Table 1). Treatment-naïve patients had shorter SSc disease duration, lower prevalence of DUs, higher diffusing capacity of the lung for carbon monoxide (DLCO), smaller right atrial area, higher occurrence of diastolic dysfunction and higher frequency of initiating upfront PAH therapy (Table 1). Over a median follow-up of 3.3 years (Q1–Q3: 1.4–5.6), 172 (40%) of the SSc-PAH patients died, and 14 (4%) underwent lung transplantation. The overall 1-, 3- and 5-year TFS rates were 93%, 78% and 64%, respectively. Treatment-naïve patients had a better long-term survival rate compared with those receiving pre-existing treatment (Table 1 and Supplementary Fig. S2, available at *Rheumatology* online). There were no
<table><caption>**Table 1.** Comparison of baseline characteristics in treatment-naïve patients vs patients with pre-existing vascular-targeted therapies</caption><thead><tr><th></th><th>No.</th><th>All SSc-PAH<br>(n = 429)</th><th>Treatment-naïve<br>(n = 288)</th><th>Pre-existing treatment<br>(n = 141)</th><th>P</th></tr></thead><tbody><tr><td>Age, years (SD)</td><td>429</td><td>65 ± 11</td><td>66 ± 11</td><td>65 ± 11</td><td>0.753</td></tr><tr><td>Male sex, no. (%)</td><td>429</td><td>60 (14.0)</td><td>37 (12.9)</td><td>23 (16.3)</td><td>0.331</td></tr><tr><td>SSc characteristics</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SSc duration, years (Q1-Q3)</td><td>406</td><td>9.7 (3.7-16.5)</td><td>8.5 (2.4-15.5)</td><td>12.7 (5.5-19.3)</td><td>0.0008</td></tr><tr><td>lcSSc, no. (%)</td><td>420</td><td>342 (81.4)</td><td>235 (83.9)</td><td>107 (76.4)</td><td>0.062</td></tr><tr><td>mRSS, mean (SD)</td><td>361</td><td>4.4 ± 6.2</td><td>4.5 ± 6.3</td><td>4.2 ± 6.0</td><td>0.659</td></tr><tr><td>ACA positive, no. (%)</td><td>427</td><td>273 (63.9)</td><td>181 (63.1)</td><td>92 (65.7)</td><td>0.593</td></tr><tr><td>Digital ulcers, no. (%)</td><td>423</td><td>170 (40.2)</td><td>97 (34.0)</td><td>73 (52.9)</td><td></td></tr><tr><td>Telangiectasia, no. (%)</td><td>417</td><td>352 (84.4)</td><td>236 (83.7)</td><td>116 (85.9)</td><td>0.556</td></tr><tr><td>Joint synovitis, no. (%)</td><td>387</td><td>57 (14.7)</td><td>36 (14.2)</td><td>21 (15.7)</td><td>0.703</td></tr><tr><td>Muscle weakness, no. (%)</td><td>359</td><td>60 (16.7)</td><td>30 (12.9)</td><td>30 (23.8)</td><td>0.008</td></tr><tr><td>Renal crisis, no. (%)</td><td>407</td><td>16 (3.9)</td><td>13 (4.7)</td><td>3 (2.3)</td><td>0.241</td></tr><tr><td>Lung characteristics</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FVC, % predicted (SD)</td><td>408</td><td>91.3 ± 21.1</td><td>90.8 ± 20.1</td><td>92.3 ± 23.0</td><td>0.485</td></tr><tr><td>DLCO, % predicted (Q1-Q3)</td><td>382</td><td>43 (33-52)</td><td>45 (34-53)</td><td>40 (33-50)</td><td>0.038</td></tr><tr><td>6MWD, m (SD)</td><td>306</td><td>341 ± 127</td><td>342 ± 130</td><td>340 ± 121</td><td>0.930</td></tr><tr><td>WHO-FC III and IV, no. (%)</td><td>418</td><td>211 (50.5)</td><td>142 (50.4)</td><td>69 (50.7)</td><td>0.942</td></tr><tr><td>ILD, no. (%)</td><td>429</td><td>187 (43.6)</td><td>130 (45.1)</td><td>57 (40.4)</td><td>0.355</td></tr><tr><td>Heart characteristics</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NT-proBNP, ng/L (Q1-Q3)</td><td>260</td><td>568 (203-1495)</td><td>623 (211-1599)</td><td>490 (176-1260)</td><td>0.467</td></tr><tr><td>Right atrial area, cm<sup>2</sup> (Q1-Q3)</td><td>111</td><td>17.6 (14.9-22.0)</td><td>16.8 (14.0-20.1)</td><td>20.5 (17.4-24.8)</td><td>0.036</td></tr><tr><td>Pericardial effusion, no. (%)</td><td>379</td><td>65 (17.2)</td><td>44 (16.5)</td><td>21 (18.8)</td><td>0.593</td></tr><tr><td>TAPSE/sPAP, mm/mmHg (Q1-Q3)</td><td>166</td><td>0.36 (0.23-0.49)</td><td>0.33 (0.22-0.48)</td><td>0.40 (0.25-0.50)</td><td>0.169</td></tr><tr><td>Diastolic dysfunction, no. (%)</td><td>300</td><td>132 (44.0)</td><td>97 (49.5)</td><td>35 (33.7)</td><td>0.009</td></tr><tr><td>mPAP, mmHg (Q1-Q3)</td><td>429</td><td>33 (26-43)</td><td>32 (26-44)</td><td>34 (27-42)</td><td>0.479</td></tr><tr><td>PAWP, mmHg (Q1-Q3)</td><td>429</td><td>9 (7-12)</td><td>10 (7-12)</td><td>9 (7-12)</td><td>0.893</td></tr><tr><td>PVR, WU (Q1-Q3)</td><td>429</td><td>5.3 (3.3-8.0)</td><td>5.1 (3.2-7.9)</td><td>5.6 (3.4-8.1)</td><td>0.255</td></tr><tr><td>CI, L/min/m<sup>2</sup> (Q1-Q3)</td><td>398</td><td>2.7 (2.2-3.2)</td><td>2.7 (2.2-3.2)</td><td>2.7 (2.2-3.2)</td><td>0.774</td></tr><tr><td>Lower mPAP/PVR, no. (%)</td><td>429</td><td>118 (27.5)</td><td>85 (29.5)</td><td>33 (23.4)</td><td>0.183</td></tr><tr><td>Other characteristics</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Upfront treatment, no. (%)</td><td>422</td><td>245 (58.1)</td><td>183 (65.1)</td><td>62 (44.0)</td><td>&lt;0.001</td></tr><tr><td>• Monotherapy, no. (%)</td><td>422</td><td>159 (37.7)</td><td>108 (38.4)</td><td>51 (36.2)</td><td>0.651</td></tr><tr><td>• Combination, no. (%)</td><td>422</td><td>86 (20.4)</td><td>75 (26.7)</td><td>11 (7.8)</td><td>&lt;0.001</td></tr><tr><td>Deaths, no. (%)</td><td>429</td><td>172 (40.1)</td><td>108 (37.5)</td><td>64 (45.4)</td><td>0.117</td></tr><tr><td>Lung transplants, no. (%)</td><td>338</td><td>13 (3.9)</td><td>8 (3.6)</td><td>5 (4.2)</td><td>0.784</td></tr><tr><td>Dx after 2015, no. (%)</td><td>429</td><td>237 (55.2)</td><td>153 (53.1)</td><td>84 (59.6)</td><td>0.207</td></tr><tr><td>Observation time, years (Q1-Q3)</td><td>429</td><td>3.3 (1.4-5.6)</td><td>3.6 (1.5-6.1)</td><td>2.9 (1.2-4.7)</td><td>0.027</td></tr><tr><td>1-, 3- and 5-year TFS (%)</td><td>429</td><td>93/78/64</td><td>93/80/69</td><td>93/73/53</td><td>0.006</td></tr></tbody></table>

Data are presented as no. (%), mean ± SD or median (Q1-Q3) as appropriate. SSc: systemic sclerosis; lcSSc: limited cutaneous systemic sclerosis; mRSS: modified Rodnan skin score; PAH: pulmonary arterial hypertension; ACA: anti-centromere antibody; FVC: forced vital capacity; DLCO: diffusing capacity of the lung for carbon monoxide; 6MWD: 6-min walk distance; WHO-FC: World Health Organization functional class; ILD: interstitial lung disease, limited extent; NT-proBNP: N-terminal brain natriuretic peptide; TAPSE/sPAP: tricuspid annular plane systolic excursion/systolic pulmonary artery pressure; mPAP: mean pulmonary arterial pressure; PAWP: pulmonary arterial wedge pressure; PVR: pulmonary vascular resistance; CI: cardiac index; Dx: diagnosis; TFS: transplant-free survival. P-values represent pairwise comparisons.

significant differences in survival according to the diagnostic period before and after 2015 (Supplementary Fig. S3, available at *Rheumatology* online).

## Risk stratification at baseline

We identified 11 published PAH risk stratification tools in addition to the ESC/ERS three-strata model, which we applied as the reference tool (Supplementary Table S1, available at *Rheumatology* online). Based on their applicability and performance in the SSc-PAH cohort, the top three tools were selected for comparison against the reference tool (Supplementary Tables S2 and S3, available at *Rheumatology* online): (A) the ESC/ERS three-strata model (used as the reference tool), (B) the ESC/ERS four-strata model, (C) ‘SPAHR updated’ and (D) ‘REVEAL Lite 2’ (Fig. 1).

The number of patients fulfilling the inclusion criteria of each risk stratification tool varied. Patients who met the reference tool’s criteria had shorter SSc disease duration at PAH diagnosis, less pre-existing treatment, more upfront treatment

and better TFS compared with those who did not (Supplementary Table S4, available at *Rheumatology* online). They were also more frequently diagnosed after 2015, when the ESC/ERS three-strata model and upfront combination therapy were introduced, potentially affecting tool fulfilment and outcomes (Supplementary Table S4, available at *Rheumatology* online). Due to overlapping populations across the four tools, statistical comparisons of patient characteristics were not feasible (Supplementary Table S5, available at *Rheumatology* online).

Depending on the risk stratification tool applied, the distribution of risk groups varied (Fig. 2A). The ESC/ERS three-strata model classified 3% of patients as high-risk, while the majority fell into the intermediate- (53%) or low- (44%) risk categories. ‘SPAHR updated’ showed similar results but further subdivided the intermediate-risk group into two groups. The ESC/ERS four-strata model and ‘REVEAL Lite 2’ had a more uniform distribution of risk groups, with a higher proportion of patients classified as high-risk. No significant
<figure><img src="image_5.png" /><figcaption>**Figure 2.** (A) Proportion of patients and (B) observed 1-year mortality across risk categories in the four risk stratification tools. ESC/ERS: European Society of Cardiology and European Respiratory Society; SPAHR: Swedish Pulmonary Arterial Hypertension Registry; REVEAL: Registry to Evaluate Early and Long-Term PAH Disease Management. The 1-year mortality rate was determined for patients who were either deceased or had at least a one-year observation period</figcaption></figure>

differences in the distribution of risk groups were observed between patients diagnosed before and after 2015 (Supplementary Table S6, available at *Rheumatology* online). Observed 1-year mortality rates by risk groups aligned with the expected rates as estimated by the guidelines for the ESC/ERS three- and four-strata models (Fig. 2B). Conversely, ‘SPAHR updated’ and ‘REVEAL Lite 2’ overestimated mortality for high-risk and intermediate- and high-risk groups, respectively.

TFS was differentiated across all risk strata using ‘REVEAL Lite 2’, while no significant differences were observed between the intermediate- and high-risk groups using the ESC/ERS three-strata model, or between the intermediate-high- and high-risk groups using ‘SPAHR updated’ (Fig. 3). The ESC/ERS four-strata model demonstrated significantly worse TFS in the intermediate-high-risk

group compared with the intermediate-low-risk group, whose survival rates were comparable to the low-risk group.

All the tools showed significantly greater ability to predict mortality compared with the ESC/ERS three-strata reference tool, as indicated by higher AUC values (Fig. 4). The ESC/ERS four-strata model and ‘SPAHR updated’, both of which stratify patients into four risk groups, showed a significantly higher mortality risk for the intermediate-high-risk groups compared with the intermediate-low-risk groups (Supplementary Table S7, available at *Rheumatology* online). The ESC/ERS three-strata model, the ESC/ERS four-strata model and ‘SPAHR updated’ did not significantly distinguish mortality risk between the intermediate- and high-risk groups, the low- and intermediate-low-risk groups, and the intermediate-high and high-risk groups, respectively (Supplementary Table S7, available at *Rheumatology* online).
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Transplant-free survival by risk groups in the four risk stratification tools. (A) ESC/ERS three-strata model, (B) ESC/ERS four-strata model, (C) 'SPAHR updated' and (D) 'REVEAL Lite 2'. P-values for pairwise comparison of the risk groups using the log-rank test. ESC/ERS: European Society of Cardiology and European Respiratory Society; SPAHR: Swedish Pulmonary Arterial Hypertension Registry; REVEAL: Registry to Evaluate Early and Long-Term PAH Disease Management</figcaption></figure>

Using the ESC/ERS four-strata model, WHO-FC, 6MWD and NT-proBNP were all significant predictors of intermediate-high risk classification compared to intermediate-low risk classification (Supplementary Fig. S4, available at *Rheumatology* online). NT-proBNP showed significantly higher predictive ability than WHO-FC (*P* = 0.007), while no significant difference was observed between WHO-FC and 6MWD (*P* = 0.55), or between 6MWD and NT-proBNP (*P* = 0.94).

## Impact of incorporating SSc-specific characteristics

The final covariates for the multivariable models were selected based on availability, multicollinearity and model performance (Supplementary Table S8, available at *Rheumatology* online). The addition of age, male sex, pre-existing vascular-targeted therapies, DLCO% predicted, ILD of limited extent and anti-centromere antibodies did not significantly improve the predictive ability of the risk stratification tools (Supplementary Table S9, available at *Rheumatology* online). DLCO was the only predictor of mortality independent of the risk stratification tools across all the models (Fig. 5).

## Subgroup and sensitivity analyses

All analyses were also performed in treatment-naïve patients, yielding results comparable to the total cohort (Supplementary Figs S5–S7, Supplementary Tables S10–S12, available at *Rheumatology* online). In the subanalysis based on haemodynamic thresholds, patients with mPAP 21–24 mmHg and/or PVR 2–3 WU (n = 118) demonstrated better risk profiles and TFS rates compared with those in the

higher threshold group (Supplementary Tables S13–S15, Supplementary Fig. S8, available at *Rheumatology* online). There were too few events to perform Cox regression analyses confined within the lower threshold group. However, incorporating mPAP and PVR threshold groups, along with age, male sex, DLCO and pre-existing vascular-targeted therapies into the multivariable analysis, resulted in findings consistent with the primary analysis (Supplementary Fig. S9, available at *Rheumatology* online).

We also repeated the comparative analyses on the subset of patients meeting all four tool requirements and obtained similar findings (Supplementary Figs S10–S12, Supplementary Tables S16–S18, available at *Rheumatology* online). A direct comparison of the two top-performing univariable tools, the ESC/ERS four-strata model and 'REVEAL Lite 2', showed no significant differences in their ability to predict mortality (AUC 0.73 [95% CI 0.66, 0.79] vs AUC 0.72 [95% CI 0.65, 0.78], P = 0.646).

The sensitivity analysis with multiple imputations resulted in no notable variations in the results (Supplementary Fig. S13, Supplementary Table S19, available at *Rheumatology* online).

## Discussion

In this study, we aimed to identify the most accurate approach for risk stratification at the time of SSc-PAH diagnosis, comparing several tools to the ESC/ERS three-strata model, the currently recommended tool for baseline risk assessment. We also explored whether incorporating
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 4.** Performance of the risk stratification tools in predicting all-cause mortality compared with the ESC/ERS three-strata model (reference) in unadjusted analysis. (A) ESC/ERS four-strata model compared to the reference, (B) 'SPAHR updated' compared to the reference and (C) 'REVEAL Lite 2' compared to the reference. Predictive abilities were evaluated using area under the ROC curve (AUC) based on univariable Cox regression analysis, and performance was compared to the ESC/ERS three-strata model (reference). P-values represent the statistical significance of differences in predictive performance between the risk stratification tools. The graphs were generated using STATA and assembled in BioRender.com. ESC/ERS: European Society of Cardiology and European Respiratory Society; SPAHR: Swedish Pulmonary Arterial Hypertension Registry; REVEAL: Registry to Evaluate Early and Long-Term PAH Disease Management; AUC: area under the ROC curve</figcaption></figure>
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 5.** Impact of risk stratification tools on predicting all-cause mortality in multivariable analysis. (A) ESC/ERS three-strata model (reference), (B) ESC/ERS four-strata model, (C) 'SPAHR updated' and (D) 'REVEAL Lite 2'. The multivariable models are adjusted for age, male sex, pre-existing vascular-targeted therapy, DLCO% predicted, ILD of limited extent and anti-centromere antibodies. Hazard ratios (HRs) and 95% CIs are shown for all variables. HRs for risk groups are referenced to the low-risk group. *P*-values represent the significance of the HRs obtained from multivariable Cox regression analyses. ESC/ERS: European Society of Cardiology and European Respiratory Society; SPAHR: Swedish Pulmonary Arterial Hypertension Registry; REVEAL: Registry to Evaluate Early and Long-Term PAH Disease Management; DLCO: diffusing capacity of the lung for carbon monoxide; ILD: interstitial lung disease, limited extent; Ab: antibodies; HR: hazard ratio</figcaption></figure>
SSc-specific characteristics could enhance the predictive accu-
racy of these tools.

In our cohort of newly diagnosed SSc-PAH patients, according to the 2022 haemodynamic definition, we found that the currently recommended ESC/ERS three-strata model had a lower ability to predict mortality than the ESC/ERS four-strata model, ‘SPAHR updated’ and ‘REVEAL Lite 2’. The ESC/ERS four-strata model effectively divided ‘intermediate-risk’ patients into two groups with significantly different long-term survival rates and includes the most clinically accessible risk parameters. Although incorporating SSc-specific characteristics did not significantly improve predictive ability, low DLCO was identified as an independent predictor of mortality.

Previous studies on risk stratification in SSc-PAH have of-
ten been limited to single-centre studies, subgroup analyses or
focused on treatment-naïve patients using the 2015 haemody-
namic criteria [12, 21–28, 41–44]. Our study uniquely evalu-
ates, to our knowledge, all published risk stratification tools
within a single comparator study, offering a comprehensive
assessment of these tools in a multicentre, international co-
hort of SSc-PAH patients from the EUSTAR database.
Importantly, our cohort includes patients with pre-existing
therapies for vascular symptoms, such as Raynaud phenome-
non and DUs, as well as those fulfilling the 2022 haemody-
namic definition of PAH.

We ranked the 11 identified PAH risk stratification tools by their applicability and performance in the SSc-PAH cohort, comparing the top three to the ESC/ERS three-strata model as a reference [19]. All three tools demonstrated a significantly greater ability to predict mortality than the ESC/ERS three-strata model. Notably, the ESC/ERS three-strata model did not significantly differentiate mortality risk between intermediate- and high-risk patients. This has important therapeutic implications, especially considering the different upfront treatment recommendations, including upfront triple therapy for the high-risk group, as outlined in the 2022 guidelines and further reinforced in the recent update from the seventh WSPH [19, 45].

Furthermore, the ESC/ERS three-strata model classified most patients as intermediate risk, with only 3% as high-risk. Previous studies have shown that subdividing the intermediate-risk group improves outcome differentiation and increases sensitivity to change during follow-up [21–23, 41]. In this study, both the ESC/ERS four-strata model and ‘SPAHR updated’ successfully divided intermediate-risk patients into subgroups with significantly different long-term survival rates. However, ‘SPAHR updated’ did not distinguish mortality risk between intermediate-high and high-risk patients, and only 4% of patients were classified as high-risk, with a lower than expected 1-year mortality rate. This suggests that the tool may overestimate mortality for the high-risk group.

The ESC/ERS four-strata model and ‘REVEAL Lite 2’ dem-
onstrated a uniform distribution of risk groups, with a higher
proportion stratified as high-risk. The tools demonstrated
significant discrimination of mortality risk across risk strata,
except between the low- and intermediate-low-risk groups in
the ESC/ERS four-strata model. However, since the primary
goal of baseline risk assessment is to identify high-risk
patients for upfront triple therapy, this distinction is of lesser
clinical importance [19–21]. There was no significant

difference in mortality prediction in direct comparison be-
tween the ESC/ERS four-strata model and ‘REVEAL Lite 2’. However, while the ESC/ERS four-strata model correctly aligned 1-year mortality rates with expected values, ‘REVEAL Lite 2’ overestimated mortality for intermediate- and high-risk patients in this cohort, leading to less precise risk stratification [19, 21, 41]. In addition to its predictive ability, effective subdivision of the intermediate-risk group and accurate estimation of 1-year mortality, the ESC/ERS four-strata model is practical for clinical use, relying on three easily accessible parameters (WHO-FC, 6MWD and BNP/NT-proBNP), which have previously shown the greatest prognostic value in PAH [24, 26–29]. Notably, these three risk parameters were all significant predictors when distinguishing between intermediate-low and intermediate-high risk groups, with NT-proBNP being the strongest.

The current treatment algorithm differentiates between low- and intermediate-risk vs high-risk patients [19, 45]. However, our study shows that intermediate-high-risk patients had a significantly worse prognosis compared to lower risk groups, suggesting that this subgroup may require a different management approach. Future randomized controlled trials are needed to determine the optimal treatment strategies for these patients. In addition to guiding treatment decisions, precise risk stratification is crucial for providing prognostic information and monitoring changes over time. While identifying intermediate-high-risk patients may not lead to immediate treatment changes, it enables the opportunity for closer surveillance and potentially earlier intervention. Given the poor prognosis in this group, we propose heightened awareness, including guideline-aligned treatment and careful monitoring.

Our study also assessed whether incorporating SSc-specific factors could improve predictive accuracy in the tools. Although including these factors did not significantly enhance the predictive ability of the tools, a low DLCO was an independent predictor of mortality. Numerous studies have shown that patients with SSc-PAH have lower DLCO than those with IPAH [4, 5, 46] and that a lower DLCO is associated with a poorer outcomes [33–36]. However, it remains unclear whether DLCO can improve following PAH-specific treatment, which is crucial when considering its potential role in risk stratification, particularly for follow-up assessments where sensitivity to change is essential. Some studies suggest that patients with very low DLCO may respond less effectively to therapy and that treatment may further impair gas exchange in these patients [47, 48]. While adding DLCO to risk stratification may not directly change treatment strategies, its prognostic role underscores its importance in a comprehensive risk evaluation. In a broader context, systemic organ involvement is well-documented to impact treatment response and disease outcomes in SSc patients [6, 9, 10, 32–40]. Therefore, even though SSc-specific factors did not improve the predictive accuracy of the tools *per se*, SSc-related organ involvement and comorbidities should still be considered in the overall clinical assessment for prognostic evaluation and treatment decisions, alongside other individual factors, as recommended by the guidelines [19, 20, 45].

Previous studies largely focused on treatment-naïve patients when evaluating baseline risk stratification, but many SSc patients in clinical practice are already receiving therapies, such as ERAs, PDE-5is and PPAs, to manage
vascular symptoms like Raynaud phenomenon and DUs. Our study reflects real-world conditions by including both treatment-naïve patients and those with pre-existing vascular-targeted therapies. Importantly, the subanalysis of treatment-naïve patients showed comparable performance in predicting mortality to that of the overall cohort, supporting the robustness of risk stratification at the time of SSc-PAH diagnosis, regardless of prior treatment status.

The management approach for patients with milder haemodynamic impairment (mPAP of 21–24 mmHg or PVR of 2–3 WU) remains uncertain, with close monitoring and individualized treatment decisions recommended [19, 45]. Therefore, evidence on the efficacy of risk stratification in these patients is crucial. In our cohort, patients with lower mPAP and PVR thresholds demonstrated better risk profiles and prognoses, though some were still classified at higher risk despite their milder haemodynamic burden. Tools that do not incorporate haemodynamic variables may be influenced by other factors, such as heart failure from non-PAH causes, lung disease or musculoskeletal limitations [20]. In our study, intermediate-high and high-risk patients with milder haemodynamic impairment had no major differences in SSc-related organ manifestations compared with lower-risk groups. However, unmeasured factors or subtle clinical features may contribute to the elevated risk in these patients, highlighting the need for an individualized approach. We found that risk stratification was effective independent of haemodynamic thresholds, supporting its utility even in patients with milder haemodynamic impairment.

As with all registry analyses, our study has limitations, including missing data, lack of standardized follow-up and the inclusion of patients diagnosed over an extended period with evolving screening recommendations, diagnostic criteria and management strategies, which increases population heterogeneity. A potential limitation is that the results may not fully apply to patients outside expert centres. However, since the 2022 ESC/ERS guidelines recommend that all SSc-PAH patients be managed in expert centres, this should not significantly affect generalizability. Although patients diagnosed after 2015 more frequently met the risk stratification criteria, there were no significant differences in risk group distribution or survival. This suggests that the time of diagnosis did not significantly impact the effectiveness of risk stratification. The retrospective application of the 2022 haemodynamic definition partly explains why many patients did not receive upfront therapy. While pre-existing vascular-targeted therapies likely influenced upfront treatment decisions, including pretreated patients makes our results more reflective of clinical practice. The accuracy of risk stratification remained comparable between treatment-naïve and pretreated patients. Although missing data are inherent to registry studies, subanalysis of patients meeting all risk stratification criteria and sensitivity analyses using multiple imputation did not substantially alter our findings. Unfortunately, data required to assess risk stratification at follow-up were not available.

In conclusion, considering the overall ability to predict mortality, risk segregation capabilities and clinical applicability, this study provides a rationale for using the simplified ESC/ERS four-strata model in SSc-PAH at the time of diagnosis as an alternative to the comprehensive ESC/ERS three-strata model. Risk stratification was accurate in SSc-PAH patients, regardless of pre-existing vascular-targeted therapies or haemodynamic thresholds. We also propose considering

DLCO as a prognostic marker in baseline risk assessment for SSc-PAH patients, alongside other individual factors recommended by the guidelines [19, 20]. Further research is needed to optimize the management of intermediate-high-risk patients and to explore temporal changes in DLCO in SSc-PAH patients.

## Supplementary material

Supplementary material is available at Rheumatology online.

## Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

## Author contributions

**Guarantor statement:** H.J.B. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Acknowledgement of all authors' contributions to the research and manuscript:

Criterion 1:

a) Substantial contributions to study conception and design: H.J.B, Ø.Mo., A.-M.H.-V.

b) Substantial contributions to acquisition of data: H.J.B., Co.B., P.C., P.A., C.P.S.-A., M.-E.T., A.Gi., A.B.-G., M. M., C.P.D., A.Ga., H.F., Y.T., G.R., U.M.-L., M.M.-C., I.C., E.S., E.H., O.D., A.-M.H.-V.

c) Substantial contributions to analysis and interpretation of data: all authors.

Criterion 2: Drafting the article or revising it critically for important intellectual content: all authors.

Criterion 3: Final approval of the version of the article to be published: all authors.

Criterion 4: Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: all authors.

## Funding

No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this article.

**Disclosure statement:** H.J.B. received research grants from Janssen. Co.B. received research and educational grants from Scleroderma Clinical Trial Consortium, Scleroderma Research Foundation, Gruppo Italiano Lotta alla Sclerodermia, Novartis Foundation for Medical-Biological Research, EMDO Foundation, Iten-Kohaut Foundation and AbbVie and Wellcome Trust; received speaker fees from Eli Lilly and received travel grants from Boehringer Ingelheim and Hartmann-Muller Foundation. K.B. received consulting fees from Pfizer, Pharmacosmos, Boehringer and AstraZeneca and received speaker fees from Amgen, AstraZeneca, Boehringer, Novartis, Novo Nordisk, Pharmacosmos and Pfizer. P.A. received consulting fees from
Bristol Myers Squibb; received speaker fees from Bristol Myers Squibb, Bohringer Ingelheim and Novartis and received travel grants from CSL Behring, Janssen, Roche, Bristol Myers Squibb and Eli Lilly. C.P.S.-A. received consulting fees from Janssen and Boehringer Ingelheim; received speaker fees from Janssen, MSD and Boehringer Ingelheim; received travel grants from Janssen and Boehringer Ingleheim and served as a Member of the Scientific Committee of the Spanish Scleroderma Association (patients) (AEE) (unpaid). M.-E.T. received consulting fees from Abbvie, Boheringer, Pfizer, UCB and Lilly; received speaker fees from Novartis, Lilly, Galapagos and MSD and received travel grants from Abbvie and UCB. A.G. participated on a Data Safety Monitoring Board or Advisory Board for Boheringer. C.P.D. received research grants from Abbvie, Arxx Therapeutics, Horizon and GlaxoSmithKline; received consulting fees from Janssen, GlaxoSmithKline, Bayer, Sanofi-Aventis, Boehringer Ingelheim, Roche, CSL Behring, Corbus, Acceleron, Horizon, Arxx Therapeutics, Lilly, Novartis and Certa and received speaker fees from Janssen, GlaxoSmithKline and Boehringer Ingelheim. F.D.G. received research support from NIHR, MRC, Welcome Trust, Vs Arthritis UK, Abbvie, Arxx, AstraZeneca, Boehringer-Ingelheim, DeepCure, Mitsubishi-Tanabe, Ventus; received consulting fees from Abbvie, Argenx, Arxx, Boehringer-Ingelheim, DeepCure, GSK, Janssen, Mitsubishi-Tanabe, Novartis, Ventus and received speakers fees from Janssen, MSD. M.C.V. received consulting fees from Boehringer Ingelheim and Janssen Pharmaceutical Companies of Johnson; received speaker fees from Boehringer Ingelheim, Janssen Pharmaceutical Companies of Johnson & Johnson, MSD; received travel grants from Janssen Pharmaceutical Companies of Johnson & Johnson; Boehringer Ingelheim; participated in Minimize (Vs Arthritis UK) (unpaid) and served as a treasurer of EUSTAR and steering committee member of the ERN ReCONNET. H.F. received speaker fees from Boehringer Ingelheim. Y.T. received research grants from Mitsubishi-Tanabe, Eisai, Chugai and Taisho and received speaker fees from Eli Lilly, AstraZeneca, Abbvie, Gilead, Chugai, Boehringer-Ingelheim, GlaxoSmithKline, Eisai, Taisho, Bristol-Myers, Pfizer and Taiho. O.D. received research grants from Kymera, Mitsubishi Tanabe and Boehringer Ingelheim; received consulting fees from 4P-Pharma, Abbvie, Acceleron, Alcimed, Altavant Siences, Amgen, AnaMar, Argenx, Arxx, AstraZeneca, Blade Therapeutics, Bayer, Boehringer Ingelheim, Corbus Pharmaceuticals, CSL Behring, Galderma, Galapagos, Glenmark, Gossamer, Horizon, Janssen, Kymera, Lupin, Medscape, Merck, Miltenyi Biotec, Mitsubishi Tanabe, Novartis, Orion, Prometheus Biosciences, Redxpharma, Roivant, Topadur and UCB and received speaker fees from Bayer, Boehringer Ingelheim, Janssen and Medscape. Patent issued 'mir-29 for the treatment of systemic sclerosis' (US8247389, EP2331143). Co-founder of CITUS AG. O.D. is Chair of the Executive Committee for the FOREUM Foundation, Co-chair for the ERS/EULAR Guidelines, a Member of the Board of Trustees for the Swiss Clinical Quality Management in Rheumatic Diseases (SCQM), a Senat member of the Swiss Academy of Medical Sciences (SAMW) and a Member of the Board of Trustees for the Hartmann Müller Foundation. A.-M.H.-V. received research grants from Boehringer Ingelheim and Janssen; received consulting fees from Arxx Therapeutics, Roche, BMS,

Boehringer Ingelheim, Genentech, Janssen, Werfen, Pliant Therapeutics, Abbvie and Medscape; received speaker fees from Boehringer Ingelheim, Janssen, Medscape, Merck Sharp & Dohme, Novartis and Roche; received travel grants from Boehringer Ingelheim, Medscape and Roche and served as a CTD-ILD ERS/EULAR convenor for the management of CTD-ILD and a EULAR study group leader on the lung in rheumatic and musculoskeletal diseases. Ca.B, P.E.C., A.B.-G., M.M., A.Ga, H.B., Ø.Mi, A.A., S.H., G.R., U.M.-L., M. M.C., I.C., E.S., E.H. and Ø.Mo declare no conflicts of interest.

## Acknowledgements

EUSTAR collaborators: Serena Guiducci, Florenzo Iannone, Simona Rednic, Yannick Allanore, Carlomaurizio Montecucco, Gábor Kumánovics, Michele Iudici, Gianluca Moroncini, Kristofer Andréasson, Luca Idolazzi, Jörg Henes, Johannes Pflugfelder, José António Pereira da Silva, Michael Hughes, Valeria Riccieri, Andra Balanescu, Ana Maria Gheorghiu, Christina Bergmann, Francesco Paolo Cantatore, Ellen De Langhe, Branimir Ani, Carolina de Souza Müller, Kamal Solanki, Edoardo Rosato, Britta Maurer, Lesley Ann Saketkoo, Massimiliano Limonta, Vivien M. Hsu, Lorinda S Chung, Yair Levy, Petros Sfikakis, Susana Oliveira, Masataka Kuwana.

## Ethics statement

This study complies with the Declaration of Helsinki. Each participating centre obtained approval from the local ethics committee. As the coordinating centre, the project was approved by the regional committees for medical and health research ethics (REK) in Norway, approval number 273870. The project was approved by the EUSTAR board (project number: CP122).

## AI statement

During the preparation of this work, the authors used QuillBot (https://quillbot.com/) and OpenAI's ChatGPT (GPT-4; https://openai.com/chatgpt/) for assistance in refining the manuscript, including grammar checking, optimizing phrasing, and improving conciseness. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Some figures in this manuscript were created using BioRender.com and are credited accordingly in the figure legends.

## References

1. Fretheim H, Halse A-K, Seip M *et al*. Multidimensional tracking of phenotypes and organ involvement in a complete nationwide systemic sclerosis cohort. Rheumatology (Oxford) 2020;59:2920–9.

2. Rubio-Rivas M, Homs NA, Cuartero D, Corbella X. The prevalence and incidence rate of pulmonary arterial hypertension in systemic sclerosis: systematic review and meta-analysis. Autoimmun Rev 2021;20:102713.

3. Mukerjee D, St George D, Coleiro B *et al*. Prevalence and outcome in systemic sclerosis associated pulmonary arterial hypertension: application of a registry approach. Ann Rheum Dis 2003; 62:1088–93.
4. Chung L, Liu J, Parsons L *et al.* Characterization of connective tissue disease-associated pulmonary arterial hypertension from REVEAL: identifying systemic sclerosis as a unique phenotype. Chest 2010;138:1383-94.

5. Ramjug S, Hussain N, Hurdman J *et al.* Idiopathic and systemic sclerosis-associated pulmonary arterial hypertension: a comparison of demographic, hemodynamic, and MRI characteristics and outcomes. Chest 2017;152:92-102.

6. Sobanski V, Launay D, Hachulla E, Humbert M. Current approaches to the treatment of systemic-sclerosis-associated pulmonary arterial hypertension (SSc-PAH). Curr Rheumatol Rep 2016;18:10.

7. Rhee RL, Gabler NB, Sangani S *et al.* Comparison of treatment response in idiopathic and connective tissue disease-associated pulmonary arterial hypertension. Am J Respir Crit Care Med 2015; 192:1111-7.

8. Overbeek MJ, Vonk MC, Boonstra A *et al.* Pulmonary arterial hypertension in limited cutaneous systemic sclerosis: a distinctive vasculopathy. Eur Respir J 2009;34:371-9.

9. Le Pavec J, Humbert M, Mouthon L, Hassoun PM. Systemic sclerosis-associated pulmonary arterial hypertension. Am J Respir Crit Care Med 2010;181:1285-93.

10. Launay D, Sobanski V, Hachulla E, Humbert M. Pulmonary hypertension in systemic sclerosis: different phenotypes. Eur Respir Rev 2017;26:170056.

11. Khanna D, Zhao C, Saggar R *et al.* Long-term outcomes in patients with connective tissue disease-associated pulmonary arterial hypertension in the modern treatment era: meta-analyses of randomized, controlled trials and observational registries. Arthritis Rheumatol 2021;73:837-47.

12. Hassan HJ, Naranjo M, Ayoub N *et al.* Improved survival for patients with systemic sclerosis-associated pulmonary arterial hypertension: the Johns Hopkins Registry. Am J Respir Crit Care Med 2023;207:312-22.

13. Distler O, Ofner C, Huscher D *et al.* Treatment strategies and survival of patients with connective tissue disease and pulmonary arterial hypertension: a COMPERA analysis. Rheumatology (Oxford) 2024;63:1139-46.

14. Kuwana M, Blair C, Takahashi T, Langley J, Coghlan JG. Initial combination therapy of ambrisentan and tadalafil in connective tissue disease-associated pulmonary arterial hypertension (CTD-PAH) in the modified intention-to-treat population of the AMBITION study: post hoc analysis. Ann Rheum Dis 2020;79:626-34.

15. Coghlan JG, Galiè N, Barberà JA, AMBITION investigators *et al.* Initial combination therapy with ambrisentan and tadalafil in connective tissue disease-associated pulmonary arterial hypertension (CTD-PAH): subgroup analysis from the AMBITION trial. Ann Rheum Dis 2017;76:1219-27.

16. Channick R, Chin KM, McLaughlin VV *et al.* Macitentan in pulmonary arterial hypertension associated with connective tissue disease (CTD-PAH): real-world evidence from the combined OPUS/OrPHEUS dataset. Cardiol Ther 2024;13:315-39.

17. Erdogan M, Esatoglu SN, Kilickiran Avci B, Hatemi G. Treatment of pulmonary arterial hypertension in patients with connective tissue diseases: a systematic review and meta-analysis. Intern Emerg Med 2024;19:731-43.

18. Gaine S, Escribano-Subias P, Muller A *et al.* Selexipag in patients with pulmonary arterial hypertension associated with connective tissue disease (PAH-CTD): real-world experience from EXPOSURE. Pulm Circ 2024;14:e12403.

19. Humbert M, Kovacs G, Hoeper MM, ESC/ERS Scientific Document Group *et al.* 2022 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension. Eur Heart J 2022; 43:3618-731.

20. Dardi F, Boucly A, Benza R *et al.* Risk stratification and treatment goals in pulmonary arterial hypertension. Eur Respir J 2024; 64:2401323.

21. Hoeper MM, Pausch C, Olsson KM *et al.* COMPERA 2.0: a refined four-stratum risk assessment model for pulmonary arterial hypertension. Eur Respir J 2022;60:2102311.

22. Ahmed A, Ahmed S, Kempe D, Rådegran G. Evaluation of the European Society of Cardiology/European Respiratory Society derived three- and four-strata risk stratification models in pulmonary arterial hypertension: introducing an internet-based risk stratification calculator. Eur Heart J Open 2023;3:oad012.

23. Kylhammar D, Hjalmarsson C, Hesselstrand R *et al.* Predicting mortality during long-term follow-up in pulmonary arterial hypertension. ERJ Open Res 2021;7:00837-2020.

24. Benza RL, Kanwar MK, Raina A *et al.* Development and validation of an abridged version of the REVEAL 2.0 risk score calculator, REVEAL Lite 2, for use in patients with pulmonary arterial hypertension. Chest 2021;159:337-46.

25. Dardi F, Manes A, Guarino D *et al.* A pragmatic approach to risk assessment in pulmonary arterial hypertension using the 2015 European Society of Cardiology/European Respiratory Society guidelines. Open Heart 2021;8:e001725.

26. Benza RL, Gomberg-Maitland M, Elliott CG *et al.* Predicting survival in patients with pulmonary arterial hypertension: the REVEAL risk score calculator 2.0 and comparison with ESC/ERS-based risk assessment strategies. Chest 2019;156:323-37.

27. Kylhammar D, Kjellström B, Hjalmarsson C *et al.* A comprehensive risk stratification at early follow-up determines prognosis in pulmonary arterial hypertension. Eur Heart J 2018; 39:4175-81.

28. Hoeper MM, Kramer T, Pan Z *et al.* Mortality in pulmonary arterial hypertension: prediction by the 2015 European pulmonary hypertension guidelines risk stratification model. Eur Respir J 2017; 50:1700740.

29. Boucly A, Weatherald J, Savale L *et al.* Risk assessment, prognosis and guideline implementation in pulmonary arterial hypertension. Eur Respir J 2017;50:1700889.

30. Meier FMP, Frommer KW, Dinser R, EUSTAR Co-authors *et al.* Update on the profile of the EUSTAR cohort: an analysis of the EULAR scleroderma trials and research group database. Ann Rheum Dis 2012;71:1355-60.

31. van den Hoogen F, Khanna D, Fransen J *et al.* 2013 classification criteria for systemic sclerosis: an American college of rheumatology/European league against rheumatism collaborative initiative. Ann Rheum Dis 2013;72:1747-55.

32. Goh NSL, Desai SR, Veeraraghavan S *et al.* Interstitial lung disease in systemic sclerosis: a simple staging system. Am J Respir Crit Care Med 2008;177:1248-54.

33. Chung L, Domsic RT, Lingala B *et al.* Survival and predictors of mortality in systemic sclerosis-associated pulmonary arterial hypertension: outcomes from the pulmonary hypertension assessment and recognition of outcomes in scleroderma registry. Arthritis Care Res (Hoboken) 2014;66:489-95.

34. Lefèvre G, Dauchet L, Hachulla E *et al.* Survival and prognostic factors in systemic sclerosis-associated pulmonary hypertension: a systematic review and meta-analysis. Arthritis Rheum 2013; 65:2412-23.

35. Elhai M, Meune C, Boubaya M, EUSTAR group *et al.* Mapping and predicting mortality from systemic sclerosis. Ann Rheum Dis 2017;76:1897-905.

36. Pokeerbux MR, Giovannelli J, Dauchet L *et al.* Survival and prognosis factors in systemic sclerosis: data of a French multicenter cohort, systematic review, and meta-analysis of the literature. Arthritis Res Ther 2019;21:86.

37. Meunier P, Dequidt L, Barnetche T, FHU ACRONIM *et al.* Increased risk of mortality in systemic sclerosis-associated digital ulcers: a systematic review and meta-analysis. J Eur Acad Dermatol Venereol 2019;33:405-9.

38. Morrisroe K, Stevens W, Huq M, Australian Scleroderma Interest Group (ASIG) *et al.* Survival and quality of life in incident systemic sclerosis-related pulmonary arterial hypertension. Arthritis Res Ther 2017;19:122.

39. Peacock AJ, Ling Y, Johnson MK *et al.* Idiopathic pulmonary arterial hypertension and co-existing lung disease: is this a new phenotype? Pulm Circ 2020;10:2045894020914851.
40. Tennøe AH, Murbræch K, Andreassen JC et al. Left ventricular diastolic dysfunction predicts mortality in patients with systemic sclerosis. J Am Coll Cardiol 2018;72:1804–13.

41. Boucly A, Weatherald J, Savale L et al. External validation of a refined four-stratum risk assessment score from the French pulmonary hypertension registry. Eur Respir J 2022;59:2102419.

42. Hjalmarsson C, Kjellström B, Jansson K et al. Early risk prediction in idiopathic versus connective tissue disease-associated pulmonary arterial hypertension: call for a refined assessment. ERJ Open Res 2021;7:00854–2020.

43. Weatherald J, Boucly A, Launay D et al. Haemodynamics and serial risk assessment in systemic sclerosis associated pulmonary arterial hypertension. Eur Respir J 2018; 52:1800678.

44. Mercurio V, Diab N, Peloquin G et al. Risk assessment in scleroderma patients with newly diagnosed pulmonary arterial

hypertension: application of the ESC/ERS risk prediction model. Eur Respir J 2018;52:1800497.

45. Chin KM, Gaine SP, Gerges C et al. Treatment algorithm for pulmonary arterial hypertension. Eur Respir J 2024;64:2401325.

46. Clements PJ, Tan M, McLaughlin VV, Pulmonary Arterial Hypertension Quality Enhancement Research Initiative (PAH-QuERI) Investigators et al. The pulmonary arterial hypertension quality enhancement research initiative: comparison of patients with idiopathic PAH to patients with systemic sclerosis-associated PAH. Ann Rheum Dis 2012;71:249–52.

47. Hoeper MM, Dwivedi K, Pausch C et al. Phenotyping of idiopathic pulmonary arterial hypertension: a registry analysis. Lancet Respir Med 2022;10:937–48.

48. Valentin S, Maurac A, Sitbon O et al. Outcomes of patients with decreased arterial oxyhaemoglobin saturation on pulmonary arterial hypertension drugs. Eur Respir J 2021;58:2004066.