Predictors of flares in recent-onset psoriatic arthritis. Results of a multivariable model based on machine learning

Author:

Queiro Rubén1,Seoane-Mato Daniel2,Agirregoikoa Eva Galindez3,Cañete Juan D.4,Gratacós Jordi5,Juanola Xavier6,Fiter Jordi7,Marcos Ana González8,Laiz Ana9

Affiliation:

1. Universidad de Oviedo

2. Spanish Society of Rheumatology

3. Hospital Universitario Basurto

4. Hospital Clinic & IDIBAPS

5. Hospital Universitario Parc Taulí

6. Hospital Universitari Bellvitge

7. Hospital Universitario Son Espases

8. Universidad Autónoma de Madrid

9. Hospital Universitari de la Santa Creu i Sant Pau

Abstract

Abstract Background Predicting the occurrence of a flare using tools and information that are readily available in daily clinical practice would provide added value in disease management. Scarcely any studies address this issue. The aim was to identify patient- and disease-related characteristics predicting flares in 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, fullfilling the CASPAR criteria and less than 2 years since the onset of symptoms. Flares were defined as inflammatory episodes affecting the axial skeleton and/or peripheral joints (joints, digits or entheses), diagnosed by a rheumatologist. 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 logistic regression model and random forest–type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. A k-fold cross-validation with k = 5 was performed. Results At the first follow-up visit, 37.6% of the patients who attended the clinic had experienced flares since the baseline visit. Of those who attended the second visit, 27.4% had experienced flares since the first visit. The number of observations for the multivariate analysis was 295.The variables predicting flares between visits were PsAID, number of digits with onychopathy, age-adjusted Charlson comorbidity index and level of physical activity. The mean values of the measures of validity of the machine learning algorithms were all high, especially sensitivity (95.71%. 95% CI: 79.84–100.00). Conclusions These findings provide guidance not only on general measures (regular physical activity), but also on therapy (drugs addressing nail disease).

Publisher

Research Square Platform LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3