Unmet Needs in Axial Spondyloarthritis

Lubrano, Ennio; Socio, Antonia De; Perrotta, Fabio Massimo

doi:10.1007/s12016-017-8637-0

Cited by 26 publications

(17 citation statements)

References 74 publications

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“…In recent years, there have been expanded efforts to improve knowledge on axSpA using big data and machine-learning techniques [53]. With the technological advances in imaging modalities and treatment, researchers identified many individuals with nontraditional axSpA phenotypes that were previously not recognized [53,54].…”

Section: Opportunities and Potential Benefits With Machine Learning Imentioning

confidence: 99%

Application of machine learning in the diagnosis of axial spondyloarthritis

Walsh

Rozycki²,

et al. 2019

Current Opinion in Rheumatology

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Purpose of review In this review article, we describe the development and application of machine-learning models in the field of rheumatology to improve the detection and diagnosis rates of underdiagnosed rheumatologic conditions, such as ankylosing spondylitis and axial spondyloarthritis (axSpA). Recent findings In an attempt to aid in the earlier diagnosis of axSpA, we developed machine-learning models to predict a diagnosis of ankylosing spondylitis and axSpA using administrative claims and electronic medical record data. Machine-learning algorithms based on medical claims data predicted the diagnosis of ankylosing spondylitis better than a model developed based on clinical characteristics of ankylosing spondylitis. With additional clinical data, machine-learning algorithms developed using electronic medical records identified patients with axSpA with 82.6–91.8% accuracy. These two algorithms have helped us understand potential opportunities and challenges associated with each data set and with different analytic approaches. Efforts to refine and validate these machine-learning models are ongoing. Summary We discuss the challenges and benefits of machine-learning models in healthcare, along with potential opportunities for its application in the field of rheumatology, particularly in the early diagnosis of axSpA and ankylosing spondylitis.

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Section: Opportunities and Potential Benefits With Machine Learning Imentioning

confidence: 99%

Application of machine learning in the diagnosis of axial spondyloarthritis

Walsh

Rozycki²,

et al. 2019

Current Opinion in Rheumatology

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“…Delays in diagnosis of axSpA also have been attributed to late referral of patients with inflammatory back pain by general practitioners to rheumatologists [ 15 , 16 , 30 , 31 ]. However, the average wait for a rheumatology appointment is 4 months [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Primary care physician perspectives on barriers to diagnosing axial Spondyloarthritis: a qualitative study

et al. 2020

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Background The average delay in diagnosis for patients with axial spondyloarthritis (axSpA) is 7 to 10 years. Factors that contribute to this delay are multifactorial and include the lack of diagnostic criteria (although classification criteria exist) for axSpA and the difficulty in distinguishing inflammatory back pain, a key symptom of axSpA, from other highly prevalent forms of low back pain. We sought to describe reasons for diagnostic delay for axSpA provided by primary care physicians. Methods We conducted a qualitative research study which included 18 US primary care physicians, balanced by gender. Physicians provided informed consent to participate in an in-depth interview (< 60 min), conducted in person (n = 3) or over the phone (n = 15), in 2019. The analysis focuses on thoughts about factors contributing to diagnostic delay in axSpA. Results Physicians noted that the disease characteristics contributing to diagnostic delay include: back pain is common and axSpA is less prevalent, slow progression of axSpA, intermittent nature of axSpA pain, and in the absence of abnormal radiographs of the spine or sacroiliac joints, there is no definitive test for axSpA. Patient characteristics believed to contribute to diagnostic delay included having multiple conditions in need of attention, infrequent interactions with the health care system, and “doctor shopping.” Doctors noted that patients wait until the last moments of the clinical encounter to discuss back pain. Problematic physician characteristics included lack of rapport with patients, lack of setting appropriate expectations, and attribution of back pain to other factors. Structural/system issues included short appointments, lack of continuity of care, insufficient insurance coverage for tests, lack of back pain clinics, and a shortage of rheumatologists. Conclusion Primary care physicians agreed that lengthy axSpA diagnosis delays are challenging to address owing to the multifactorial causes (e.g., disease characteristics, patient characteristics, lack of definitive tests, system factors).

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“…the p40 subunit of IL-12/IL-23, antibodies blocking RORγt etc). However, a high proportion of patients still has an inadequate response, or loses efficacy after time, due to unknown mechanisms [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

MicroRNAs in ankylosing spondylitis: Function, potential and challenges

Motta

Carena

Selmi

et al. 2020

Journal of Translational Autoimmunity

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Epigenetic mechanisms such as DNA methylation, histone modifications and non-coding RNA, are considered the essential connection between a disorder’s onset and the environment, on a permissive genetic background. Among autoimmune and inflammatory-mediated disorders, Ankylosing Spondylitis (AS), a chronic arthritis of the spine, is a very good example for the weight of epigenetics’ contribution. MicroRNAs (miRNAs) are single-stranded nucleotides which regulate gene expression and are involved in pathological and physiological processes. In this manuscript we provide a clarification on the role of microRNAs in AS, with a focus on the mechanisms of pathogenesis. In specific, we have examined the contribution of miRNAs in the processes of inflammation, new bone formation and T-cell function, and the pathways (i.e. Wnt, BMP, TGFβ signalling etc.) they regulate. The utility of miRNAs in better understanding AS pathogenesis is undisputed and their utility as therapeutic opportunity is strongly increasing.

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Unmet Needs in Axial Spondyloarthritis

Cited by 26 publications

References 74 publications

Application of machine learning in the diagnosis of axial spondyloarthritis

Application of machine learning in the diagnosis of axial spondyloarthritis

Primary care physician perspectives on barriers to diagnosing axial Spondyloarthritis: a qualitative study

MicroRNAs in ankylosing spondylitis: Function, potential and challenges

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