Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment
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ORIGINAL RESEARCH
Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment Luca Navarini . Francesco Caso . Luisa Costa . Damiano Currado . Liliana Stola . Fabio Perrotta . Lorenzo Delfino . Michela Sperti . Marco A. Deriu . Piero Ruscitti . Viktoriya Pavlych . Addolorata Corrado . Giacomo Di Benedetto . Marco Tasso . Massimo Ciccozzi . Alice Laudisio . Claudio Lunardi . Francesco Paolo Cantatore . Ennio Lubrano . Roberto Giacomelli . Raffaele Scarpa . Antonella Afeltra Received: July 23, 2020 / Accepted: August 28, 2020 The Author(s) 2020
ABSTRACT Introduction: 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). Methods: A retrospective analysis of prospectively collected data from an AS cohort has been Luca Navarini and Francesco Caso contributed equally to this paper. L. Navarini (&) D. Currado L. Stola A. Afeltra Unit of Allergology, Immunology, Rheumatology, Department of Medicine, Universita` Campus BioMedico di Roma, Rome, Italy e-mail: [email protected] F. Caso (&) L. Costa M. Tasso R. Giacomelli Rheumatology Unit, Department of Clinical Medicine and Surgery, School of Medicine, University Federico II of Naples, Naples, Italy e-mail: [email protected]; [email protected] F. Perrotta E. Lubrano Academic Rheumatology Unit, Dipartimento di Medicina e Scienze della Salute ‘‘Vincenzo Tiberio’’, Universita` degli Studi del Molise, Campobasso, Italy L. Delfino C. Lunardi Department of Medicine, University of Verona, Verona, Italy
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 concordancestatistic (c-statistic). Three ML techniques were considered to calculate the CV risk: support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN). Results: 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, M. Sperti M. A. Deriu PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy P. Ruscitti V. Pavlych R. Giacomelli Rheumatology Unit, Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Aquila, Italy A. Corrado F. P. Cantatore Rheumatology Clinic, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy G. Di Benedetto 7HC, srl. Via Giovanni Paisiello 55 CAP 00198, Rome, Italy G. Di Benedetto Biomedical Research and Innovation Institute of Ca´diz (INiBICA), Research Unit, Puerta del Mar University Hospital, University of Ca´diz, Cadiz, Spain
Rheumatol Ther
QRISK3, RRS, and ASSIGN. AUC values for the ML algorith
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