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

Abstract

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA 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.ResultsOf 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.ConclusionOur 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.

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

“…Van Leeuwen and colleagues [ 24 ] successfully created a prediction model able to identify nonprogressors. First, they defined progression as worsening in one or more organ systems and/or start of immunosuppressive therapy or death between two visits.…”

Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records

“…For example, AI has been shown to aid the diagnosis of fibrotic lung diseases, tuberculosis, and diabetes in research studies [ 18 , 19 , 20 ]. AI also had been shown to track disease progression in diseases such as systemic sclerosis [ 21 ], osteoarthritis [ 22 ], and mild cognitive impairment [ 23 ], and predict disease complications in diseases such as diabetes [ 24 ], Crohn’s disease [ 25 ], and atrial fibrillation [ 26 ]. However, the adoption of AI in healthcare and medicine is slower than in other fields [ 27 ].…”