Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Observational Study
. 2022 Jun 24;24(1):153.
doi: 10.1186/s13075-022-02838-2.

Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning

Rubén Queiro  1 , Daniel Seoane-Mato  2 , Ana Laiz  3 , Eva Galíndez Agirregoikoa  4 , Carlos Montilla  5 , Hye-Sang Park  3 , Jose A Pinto-Tasende  6 , Juan J Bethencourt Baute  7 , Beatriz Joven Ibáñez  8 , Elide Toniolo  9 , Julio Ramírez  10 , Ana Serrano García  11 ; Proyecto REAPSER Study Group
Collaborators, Affiliations
Observational Study

Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning

Rubén Queiro et al. Arthritis Res Ther. .

Abstract

Background: Very few data are available on predictors of minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis (PsA). Such data are crucial, since the therapeutic measures used to change the adverse course of PsA are more likely to succeed if we intervene early. In the present study, we used predictive models based on machine learning to detect variables associated with achieving MDA in patients with recent-onset PsA.

Methods: We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset contained data for the independent variables from the baseline visit and from follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a random forest-type machine learning algorithm to analyze the association between the outcome measure and the variables selected in the bivariate analysis. In order to understand how the model uses the variables to make its predictions, we applied the SHAP technique. We used a confusion matrix to visualize the performance of the model.

Results: The sample comprised 158 patients. 55.5% and 58.3% of the patients had MDA at the first and second follow-up visit, respectively. In our model, the variables with the greatest predictive ability were global pain, impact of the disease (PsAID), patient global assessment of disease, and physical function (HAQ-Disability Index). The percentage of hits in the confusion matrix was 85.94%.

Conclusions: A key objective in the management of PsA should be control of pain, which is not always associated with inflammatory burden, and the establishment of measures to better control the various domains of PsA.

Keywords: Machine learning; Minimal disease activity; Predictive model; Recent-onset psoriatic arthritis.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests related to this work.

Figures

Fig. 1
Fig. 1
Random forest–type machine learning algorithm. SHAP summary graph
See this image and copyright information in PMC

References

    1. Romero Pérez A, Queiro R, Seoane-Mato D, Graell E, Chamizo E, Chaves Chaparro L, et al. Higher prevalence of psoriatic arthritis in the adult population in Spain? A population-based cross-sectional study. PLoS One. 2020;15:e0234556. doi: 10.1371/journal.pone.0234556. - DOI - PMC - PubMed
    1. Ritchlin CT, Colbert RA, Gladman DD. Psoriatic arthritis. N Engl J Med. 2017;376:957–970. doi: 10.1056/NEJMra1505557. - DOI - PubMed
    1. Smolen JS, Schöls M, Braun J, Dougados M, FitzGerald O, Gladman DD, et al. Treating axial spondyloarthritis and peripheral spondyloarthritis, especially psoriatic arthritis, to target: 2017 update of recommendations by an international task force. Ann Rheum Dis. 2018;77:3–17. doi: 10.1136/annrheumdis-2017-211734. - DOI - PMC - PubMed
    1. Gossec L, McGonagle D, Korotaeva T, Lubrano E, de Miguel E, Østergaard M, et al. Minimal disease activity as a treatment target in psoriatic arthritis: a review of the literature. J Rheumatol. 2018;45:6–13. doi: 10.3899/jrheum.170449. - DOI - PubMed
    1. Lorenzo A, Pardo E, Charca L, Pino M, Queiro R. Enthesitis and joint erosions are disease traits associated with cardiovascular risk in psoriatic arthritis. Clin Rheumatol. 2020;39:2973–2979. doi: 10.1007/s10067-020-05088-2. - DOI - PubMed

Publication types