ArticleRheumatology and therapy2020
Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment.
Article in Rheumatology and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cardiovascular risk in axial spondyloarthritis-a systematic review.Clinical rheumatology · 2023Pooled it
- Development and validation of a risk stratification model for cardiovascular disease in patients with radiographic axial spondyloarthritis (r-axSpA): a retrospective study.Frontiers in medicine · 2026Article
- Review
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Pain catastrophizing negatively impacts drug retention rate in patients with Psoriatic Arthritis and axial Spondyloarthritis: results from a 2-years perspective multicenter GIRRCS (Gruppo Italiano di Ricerca in Reumatologia Clinica) study.Arthritis research & therapy · 2024Observational
- Entry point of machine learning in axial spondyloarthritis.RMD open · 2024Review
- Increased Prevalence of Headaches and Migraine in Patients with Psoriatic Arthritis and Axial Spondyloarthritis: Insights from an Italian Cohort Study.Biomedicines · 2024Article
- ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI.Biomedicines · 2023Article
- Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques.BMC medical informatics and decision making · 2023Review
- The comparison of cardiovascular disease risk prediction scores and evaluation of subclinical atherosclerosis in rheumatoid arthritis: a cross-sectional study.Clinical rheumatology · 2022Article
- Persistence of C-reactive protein increased levels and high disease activity are predictors of cardiovascular disease in patients with axial spondyloarthritis.Scientific reports · 2022Article
- Your neighborhood matters: A machine-learning approach to the geospatial and social determinants of health in 9-1-1 activated chest pain.Research in nursing & health · 2022Article
- Therapeutic Targets for Ankylosing Spondylitis - Recent Insights and Future Prospects.Open access rheumatology : research and reviews · 2022Review
- High inflammatory burden predicts cardiovascular events in patients with axial spondyloarthritis: a long-term follow-up study.Therapeutic advances in musculoskeletal disease · 2022Article
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Cardiovascular risk estimation in young patients with ankylosing spondylitis: A new model based on a prospective study in Constanta County, Romania.Experimental and therapeutic medicine · 2021Article
- Potential relation of cardiovascular risk factors to disease activity in patients with axial spondyloarthritis.Therapeutic advances in musculoskeletal disease · 2021Article
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22 authors.
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Abstract
introductionThe performance of seven cardiovascular (CV) risk algorithms is evaluated in a multicentric cohort of ankylosing spondylitis (AS) patients. Performance and calibration of traditional CV predictors have been compared with the novel paradigm of machine learning (ML).
methodsA retrospective analysis of prospectively collected data from an AS cohort has been performed. The primary outcome was the first CV event. The discriminatory ability of the algorithms was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), which is like the concordance-statistic (c-statistic). Three ML techniques were considered to calculate the CV risk: support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN).
resultsOf 133 AS patients enrolled, 18 had a CV event. c-statistic scores of 0.71, 0.61, 0.66, 0.68, 0.66, 0.72, and 0.67 were found, respectively, for SCORE, CUORE, FRS, QRISK2, QRISK3, RRS, and ASSIGN. AUC values for the ML algorithms were: 0.70 for SVM, 0.73 for RF, and 0.64 for KNN. Feature analysis showed that C-reactive protein (CRP) has the highest importance, while SBP and hypertension treatment have lower importance.
conclusionsAll of the evaluated CV risk algorithms exhibit a poor discriminative ability, except for RRS and SCORE, which showed a fair performance. For the first time, we demonstrated that AS patients do not show the traditional ones used by CV scores and that the most important variable is CRP. The present study contributes to a deeper understanding of CV risk in AS, allowing the development of innovative CV risk patient-specific models.
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