New risk model is able to identify patients with a low risk of progression in systemic sclerosis

Abstract

Objectives: To develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.

Methods: A machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.

Results: Of the 492 SSc patients (follow-up range: 2-10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197-0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.

Conclusion: Our machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as 'low risk'. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

Keywords: autoimmunity; health care; outcome assessment; scleroderma; systemic.

Figure 1

Flow chart description: flow charts of inclusion process. Cut-off time point for inclusion: 1 July 2019. *92 patients had to be excluded due to missing data/incomplete data eventhough they had three or more visits. Of these, the majority did not show progression based on clinical and laboratory assessment, 6 min walking distance and pulmonary function testing. SSc, systemic sclerosis.

Figure 2

Progressors in SSc cohort description: organ progression in SSc cohort, progression was not always limited to one organ domain. Ttwenty-five per cent of the patients showed organ progression on more than one organ domain. SSc, systemic sclerosis.

Figure 3

ROC curve and distribution of probability plot. ROC curve and distribution of probability plot of the validation set in progressors and non-progressors.

Figure 4

Probability risk scores of the progressors stratified for treatment initiation and organ domain. Patients with cardiac progression and treatment (n=3): ¹ trifascicular block with pauses >3 s for which pacemaker implantation, severe tricuspid insufficiency, ² new right bundle branch block, decrease in LVEF <50%, increase dyspnoea,³ clinical cardiac involvement; supraventriculair arrhythmias 2%, diastolic dysfunction grade 1, elevated troponin T and CK, progressive dyspnoea). Patients with cardiac progression without treatment (n=2): ¹ LVEF <54%, ² suptraventricular arrhythmias >2% on 24 hours Holter ECG monitoring. ILD progression with treatment (n=3): ¹ mild fibrotic changes with a decrease in FVC (73% to 58%) and in DLCO (97%–76%), ² increase in fibrotic changes, decline FVC (52%–42%) and decline in DLCO (48%–28%), ³ progressive ILD and decline in FVC and DLCO (n=3). ILD progression without treatment (n=1): ¹ presence of ILD with bronchiectasis, honeycombing and an increase in reticular opacities, no clinical symptoms, with FVC decline (101%–90%). Skin progression with treatment (n=2): ¹ mRSS increase from 10 to 17, ² increase mRSS 10 to 23. Gastrointestinal progression with treatment (n=1): ¹ weight loss >10% in 1 year and Hb decline. One patient developed renal crisis, one patient died due to lung carcinoma (also had supraventriculair extrasystoles >2 s and in increase in fibrotic changes on HRCT). DLCO, diffusing capacity of the lungs for carbon monoxide; FVC, forced vital capacity; HRCT, high-resolution CT; ILD, interstitial lung disease; mRSS, modified Rodnan skin score.

Figure 5

ROC curve and distribution of probabilities plot of the Delphi model. ROC curve and distribution of probabilities plot of the Delphi model stratified for progression.