Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment
| Title: | Cardiovascular Risk Prediction in Ankylosing Spondylitis: From Traditional Scores to Machine Learning Assessment |
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| Authors: | 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 |
| Source: | Rheumatology and Therapy, Vol 7, Iss 4, Pp 867-882 (2020) |
| Publisher Information: | Adis, Springer Healthcare, 2020. |
| Publication Year: | 2020 |
| Collection: | LCC:Diseases of the musculoskeletal system |
| Subject Terms: | Ankylosing spondylitis, Cardiovascular risk, C-reactive protein, Machine learning, Diseases of the musculoskeletal system, RC925-935 |
| More Details: | 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 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). 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, 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. Conclusions 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2198-6576 2198-6584 |
| Relation: | https://doaj.org/toc/2198-6576; https://doaj.org/toc/2198-6584 |
| DOI: | 10.1007/s40744-020-00233-4 |
| Access URL: | https://doaj.org/article/c4d5142579f8425e950c00599ad14534 |
| Accession Number: | edsdoj.4d5142579f8425e950c00599ad14534 |
| Database: | Directory of Open Access Journals |
| ISSN: | 21986576 21986584 |
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| DOI: | 10.1007/s40744-020-00233-4 |
| Published in: | Rheumatology and Therapy |
| Language: | English |