Dataset Information

Psoriatic Arthritis: Development, Detection and Prevention: A Scoping Review.

ABSTRACT: Psoriatic arthritis is a heterogenous chronic inflammatory disease that develops over time in some patients with psoriasis. The course of the disease is variable, with a broad clinical spectrum. The management of PsA has changed tremendously over the last decade, thanks to earlier diagnosis, a multidisciplinary approach and progress in pharmacological therapies. Therefore, screening for risk factors and the early signs of arthritis is highly important and recommended. Currently, research is focused on finding soluble biomarkers and developing imaging techniques that can improve the prediction of psoriatic arthritis. Among imaging modalities, ultrasonography seems to be the most accurate in detecting subclinical inflammation. Early intervention is based on the assumption that it is possible to prevent or delay psoriatic arthritis if systemic treatment for psoriasis can be administered early enough. This review article provides an overview of the current perspectives and evidence regarding the diagnosis, management and prevention of psoriatic arthritis.

SUBMITTER: Kimak A

PROVIDER: S-EPMC10253876 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Psoriatic Arthritis: Development, Detection and Prevention: A Scoping Review.

Kimak Agnieszka A Robak Ewa E Makowska Joanna J Woźniacka Anna A

Journal of clinical medicine 20230605 11

Psoriatic arthritis is a heterogenous chronic inflammatory disease that develops over time in some patients with psoriasis. The course of the disease is variable, with a broad clinical spectrum. The management of PsA has changed tremendously over the last decade, thanks to earlier diagnosis, a multidisciplinary approach and progress in pharmacological therapies. Therefore, screening for risk factors and the early signs of arthritis is highly important and recommended. Currently, research is focu ...[more]

PMID: 37298045

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Project description:BackgroundArtificial intelligence (AI) has many applications in numerous medical fields, including dermatology. Although the majority of AI studies in dermatology focus on skin cancer, there is growing interest in the applicability of AI models in inflammatory diseases, such as psoriasis. Psoriatic disease is a chronic, inflammatory, immune-mediated systemic condition with multiple comorbidities and a significant impact on patients' quality of life. Advanced treatments, including biologics and small molecules, have transformed the management of psoriatic disease. Nevertheless, there are still considerable unmet needs. Globally, delays in the diagnosis of the disease and its severity are common due to poor access to health care systems. Moreover, despite the abundance of treatments, we are unable to predict which is the right medication for the right patient, especially in resource-limited settings. AI could be an additional tool to address those needs. In this way, we can improve rates of diagnosis, accurately assess severity, and predict outcomes of treatment.ObjectiveThis study aims to provide an up-to-date literature review on the use of AI in psoriatic disease, including diagnostics and clinical management as well as addressing the limitations in applicability.MethodsWe searched the databases MEDLINE, PubMed, and Embase using the keywords "AI AND psoriasis OR psoriatic arthritis OR psoriatic disease," "machine learning AND psoriasis OR psoriatic arthritis OR psoriatic disease," and "prognostic model AND psoriasis OR psoriatic arthritis OR psoriatic disease" until June 1, 2023. Reference lists of relevant papers were also cross-examined for other papers not detected in the initial search.ResultsOur literature search yielded 38 relevant papers. AI has been identified as a key component in digital health technologies. Within this field, there is the potential to apply specific techniques such as machine learning and deep learning to address several aspects of managing psoriatic disease. This includes diagnosis, particularly useful for remote teledermatology via photographs taken by patients as well as monitoring and estimating severity. Similarly, AI can be used to synthesize the vast data sets already in place through patient registries which can help identify appropriate biologic treatments for future cohorts and those individuals most likely to develop complications.ConclusionsThere are multiple advantageous uses for AI and digital health technologies in psoriatic disease. With wider implementation of AI, we need to be mindful of potential limitations, such as validation and standardization or generalizability of results in specific populations, such as patients with darker skin phototypes.

Project description:ObjectiveTo systematically review the accuracy and characteristics of different questionnaire-based PsA screening tools.MethodsA systematic review of MEDLINE, Excerpta Medical Database, Cochrane Central Register of Controlled Trials and Web of Science was conducted to identify studies that evaluated the accuracy of self-administered PsA screening tools for patients with psoriasis. A bivariate meta-analysis was used to pool screening tool-specific accuracy estimates (sensitivity and specificity). Heterogeneity of the diagnostic odds ratio was evaluated through meta-regression. All full-text records were assessed for risk of bias with the QUADAS 2 tool.ResultsA total of 2280 references were identified and 130 records were assessed for full-text review, of which 42 were included for synthesis. Of these, 27 were included in quantitative syntheses. Of the records, 37% had an overall low risk of bias. Fourteen different screening tools and 104 separate accuracy estimates were identified. Pooled sensitivity and specificity estimates were calculated for the Psoriatic Arthritis Screening and Evaluation (cut-off = 44), Psoriatic Arthritis Screening and Evaluation (47), Toronto Psoriatic Arthritis Screening (8), Psoriasis Epidemiology Screening Tool (3) and Early Psoriatic Arthritis Screening Questionnaire (3). The Early Psoriatic Arthritis Screening Questionnaire reported the highest sensitivity and specificity (0.85 each). The I2 for the diagnostic odds ratios varied between 76 and 90.1%. Meta-regressions were conducted, in which the age, risk of bias for patient selection and the screening tool accounted for some of the observed heterogeneity.ConclusionsQuestionnaire-based tools have moderate accuracy to identify PsA among psoriasis patients. The Early Psoriatic Arthritis Screening Questionnaire appears to have slightly better accuracy compared with the Toronto Psoriatic Arthritis Screening, Psoriasis Epidemiology Screening Tool and Psoriatic Arthritis Screening and Evaluation. An economic evaluation could model the uncertainty and estimate the cost-effectiveness of PsA screening programs that use different tools.

Dataset Information

Psoriatic Arthritis: Development, Detection and Prevention: A Scoping Review.

Publications

Psoriatic Arthritis: Development, Detection and Prevention: A Scoping Review.

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