Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

Lezcano-Valverde, José M.; Salazar, Fernando; León, Leticia; Toledano, Esther; Jover, Juan A.; Soudah, Eduardo; González‐Álvaro, Isidoro; Abásolo, Lydia; Rodríguez‐Rodríguez, Luis

doi:10.1038/s41598-017-10558-w

Cited by 58 publications

(36 citation statements)

References 69 publications

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“…Disease progression and outcome was a focus for 27 studies. Other considered issues were disease severity [72][73][74][75][76][77][78] in psoriasis, RA, IBD and coeliac disease; treatment response [79][80][81][82][83][84][85][86][87] in IBD, RA and primary biliary cirrhosis (PBC); and survival prediction [88][89][90] in PBC, RA and SLE. Other models focused on improved image segmentation to aid prognoses [91][92][93][94][95][96] for IBD and MS. Disease progression and outcome was the second-most prevalent area for model development.…”

Section: Disease Progression and Outcomementioning

confidence: 99%

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

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Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

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Section: Disease Progression and Outcomementioning

confidence: 99%

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

et al. 2020

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“…Moreover, KM curves showed a significant association of the baseline reactivity levels of MAT2β-AAb with the time of KOA appearance. This statistical approach has been widely used in cancer biomarkers30–32 and it has been recently introduced in the rheumatology field 33 34. Interestingly, our results showed that higher baseline reactivity levels of this AAb result in a sooner development of radiographic KOA.…”

Section: Discussionmentioning

confidence: 61%

Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative

et al. 2019

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ObjectiveTo find autoantibodies (AAbs) in serum that could be useful to predict incidence of radiographic knee osteoarthritis (KOA).DesignA Nucleic-acid Programmable Protein Arrays (NAPPA) platform was used to screen AAbs against 2125 human proteins in sera at baseline from participants free of radiographic KOA belonging to the incidence and non-exposed subcohorts of the Osteoarthritis Initiative (OAI) who developed or not, radiographic KOA during a follow-up period of 96 months. NAPPA-ELISA were performed to analyse reactivity against methionine adenosyltransferase two beta (MAT2β) and verify the results in 327 participants from the same subcohorts. The association of MAT2β-AAb levels with KOA incidence was assessed by combining several robust biostatistics analysis (logistic regression, Receiver Operating Characteristic and Kaplan-Meier curves). The proposed prognostic model was replicated in samples from the progression subcohort of the OAI.ResultsIn the screening phase, six AAbs were found significantly different at baseline in samples from incident compared with non-incident participants. In the verification phase, high levels of MAT2β-AAb were significantly associated with the future incidence of KOA and with an earlier development of the disease. The incorporation of this AAb in a clinical model for the prognosis of incident radiographic KOA significantly improved the identification/classification of patients who will develop the disorder. The usefulness of the model to predict radiographic KOA was confirmed on a different OAI subcohort.ConclusionsThe measurement of AAbs against MAT2β in serum might be highly useful to improve the prediction of OA development, and also to estimate the time to incidence.

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“…144 Further research is needed in this field, and we can expect that future multivariate studies will also include radiologic variables to establish models that can predict the long-term outcome of inflammatory rheumatic diseases. 145…”

Section: Future Perspectivesmentioning

confidence: 99%

Quantitative Imaging in Inflammatory Arthritis: Between Tradition and Innovation

Giraudo

Kainberger

Boesen

et al. 2020

Semin Musculoskelet Radiol

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Radiologic imaging is crucial for diagnosing and monitoring rheumatic inflammatory diseases. Particularly the emerging approach of precision medicine has increased the interest in quantitative imaging. Extensive research has shown that ultrasound allows a quantification of direct signs such as bone erosions and synovial thickness. Dual-energy X-ray absorptiometry and high-resolution peripheral quantitative computed tomography (CT) contribute to the quantitative assessment of secondary signs such as osteoporosis or lean mass loss. Magnetic resonance imaging (MRI), using different techniques and sequences, permits in-depth evaluations. For instance, the perfusion of the inflamed synovium can be quantified by dynamic contrast-enhanced imaging or diffusion-weighted imaging, and cartilage injury can be assessed by mapping (T1ρ, T2). Furthermore, the increased metabolic activity characterizing the inflammatory response can be reliably assessed by hybrid imaging (positron emission tomography [PET]/CT, PET/MRI). Finally, advances in intelligent systems are pushing forward quantitative imaging. Complex mathematical algorithms of lesions' segmentation and advanced pattern recognition are showing promising results.

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Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach

Cited by 58 publications

References 69 publications

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative

Quantitative Imaging in Inflammatory Arthritis: Between Tradition and Innovation

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