RT Journal Article
T1 Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment.
A1 Navarini, Luca
A1 Caso, Francesco
A1 Costa, Luisa
A1 Currado, Damiano
A1 Stola, Liliana
A1 Perrotta, Fabio
A1 Delfino, Lorenzo
A1 Sperti, Michela
A1 Deriu, Marco A
A1 Ruscitti, Piero
A1 Pavlych, Viktoriya
A1 Corrado, Addolorata
A1 Di Benedetto, Giacomo
A1 Tasso, Marco
A1 Ciccozzi, Massimo
A1 Laudisio, Alice
A1 Lunardi, Claudio
A1 Cantatore, Francesco Paolo
A1 Lubrano, Ennio
A1 Giacomelli, Roberto
A1 Scarpa, Raffaele
A1 Afeltra, Antonella
K1 Ankylosing spondylitis
K1 C-reactive protein
K1 Cardiovascular risk
K1 Machine learning
AB The 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). A 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). Of 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. All 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.
PB Springer
SN 2198-6576
YR 2020
FD 2020-08-28
LK http://hdl.handle.net/10668/16266
UL http://hdl.handle.net/10668/16266
LA en
NO Navarini L, Caso F, Costa L, Currado D, Stola L, Perrotta F, et al. Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment. Rheumatol Ther. 2020 Dec;7(4):867-882
DS RISalud
RD Apr 7, 2025