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POS1074 MINIMAL DISEASE ACTIVITY (MDA) IN PATIENTS WITH RECENT-ONSET PSORIATIC ARTHRITIS. PREDICTIVE MODEL BASED ON MACHINE LEARNING
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  1. R. Queiró Silva1,
  2. D. Seoane-Mato2,
  3. A. Laiz3,
  4. E. Galindez4,
  5. C. A. Montilla-Morales5,
  6. H. S. Park3,
  7. J. A. Pinto Tasende6,
  8. J. J. Bethencourt Baute7,
  9. B. Joven-Ibáñez8,
  10. E. Toniolo9,
  11. J. Ramirez10,
  12. A. Serrano García11
  13. on behalf of Proyecto REAPSER Study Group
  1. 1Universidad de Oviedo, Rheumatology Service & the Principality of Asturias Institute for Health Research (ISPA). Faculty of Medicine, Oviedo, Spain
  2. 2Spanish Society of Rheumatology, Research Unit, Madrid, Spain
  3. 3Hospital Universitari de la Santa Creu i Sant Pau, Rheumatology and Autoimmune Disease Department, Barcelona, Spain
  4. 4Hospital Universitario Basurto, Rheumatology Service, Bilbao, Spain
  5. 5Hospital Universitario de Salamanca, Rheumatology Service, Salamanca, Spain
  6. 6Complexo Hospitalario Universitario de A Coruña, Rheumatology Service-INIBIC, A Coruña, Spain
  7. 7Hospital Universitario de Canarias, Rheumatology Service, Sta. Cruz de Tenerife, Spain
  8. 8Hospital Universitario 12 de Octubre, Rheumatology Service, Madrid, Spain
  9. 9Hospital Universitari Son Llàtzer, Rheumatology Service, Palma de Mallorca, Spain
  10. 10Hospital Clínic Barcelona, Arthritis Unit. Rheumatology Department, Barcelona, Spain
  11. 11Universidad Autónoma de Madrid, Knowledge Engineering Institute, Madrid, Spain

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.

Objectives To detect patient and disease 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), promoted by the Spanish Society of Rheumatology. Patients aged ≥18 years who fulfilled the CASPAR criteria, with less than 2 years since the onset of symptoms, were included. The intention at the baseline visit was to reflect the patient’s situation before disease progress was modified by the treatments prescribed by the rheumatologist.

All patients gave their informed consent. The study was approved by the Clinical Research Ethics Committee of the Principality of Asturias.

MDA was defined as fulfillment of at least 5 of the following: ≤1 tender joint; ≤1 swollen joint; PASI ≤1 or BSA ≤3%; score on the visual analog scale (VAS) for pain provided by the patient ≤1.5; overall score for disease activity provided by the patient ≤2; HAQ score ≤0.5; ≤1 painful enthesis [1].

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. This approach assigns a SHAP value to each value of each variable according to the extent to which it affects the prediction of the model (the higher the absolute SHAP value, the greater the influence of this data item on prediction) and to how it affects the prediction (if the SHAP value is positive, the data item positively affects the prediction, that is, it confers a higher value on the prediction). The SHAP summary graphs order the predictors by their importance in the predictions of the model. This importance is calculated with the mean of the SHAP values assigned to each data item of a variable; mean values <0.01 indicate the low importance of the variable in the model. We used a confusion matrix to visualize the performance of the model. This matrix shows the real class of the data items, together with the predicted class, and records the number of hits and misses.

Results The sample comprised 158 patients. 14.6% were lost to follow-up. 55.5% and 58.3% of the patients had MDA at the first and second follow-up visit, respectively. The importance of the variables in the model according to the mean of the SHAP values is shown in Table 1. 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 SHAP values for each value of each variable are shown in Figure 1. The percentage of hits in the confusion matrix was 85.94%.

Table 1.

Variables in the predictions of the random forest for MDA according to the SHAP method.

Conclusion 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.

References [1]Coates LC, Fransen J, Helliwell PS. Defining minimal disease activity in psoriatic arthritis: a proposed objective target for treatment. Ann Rheum Dis. 2010;69:48-53.

Acknowledgements The authors would like to acknowledge José Luis Fernández Sueiro for the conception of the study; José Miguel Carrasco for his contribution to the design of the study; Nuria Montero and Cristina Oliva for her contribution to data monitoring; Ana González Marcos and Cristina Pruenza for her contribution to data analysis; and Thomas O´Boyle for the translation of the manuscript.

Disclosure of Interests None declared

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