827953127faf0756d8ad5cf4a3b4c7cd pmc_9917876.pdf 05ca15ab03339a2b32b442a4147142baef824a62 pmc_9917876.pdf 9a556454997a4c1363a6bd07079e70434a157c0c440e0a0368e6a0d9c12721f3 pmc_9917876.pdf Title: Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis—Multivariable Prediction Model Based on Machine Learning Subject: The aim was to identify patient- and disease-related characteristics predicting moderate-to-high disease activity in recent-onset psoriatic arthritis (PsA). We performed a multicenter observational prospective study (2-year follow-up, regular annual visits) in patients aged 18 years who fulfilled the CASPAR criteria and had less than 2 years since the onset of symptoms. The moderate-to-high activity of PsA was defined as DAPSA > 14. 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. The sample comprised 158 patients. At the first follow-up visit, 20.8% of the patients who attended the clinic had a moderate-to-severe disease. This percentage rose to 21.2% on the second visit. The variables predicting moderate-high activity were the PsAID score, tender joint count, level of physical activity, and sex. The mean values of the measures of validity of the machine learning algorithms were all high, especially sensitivity (98%; 95% CI: 86.89–100.00). PsAID was the most important variable in the prediction algorithms, reinforcing the convenience of its inclusion in daily clinical practice. Strategies that focus on the needs of women with PsA should be considered. Keywords: arthritis; psoriatic; disease activity; DAPSA; predictive model; machine learning; PsAID Author: Rubén Queiro, Daniel Seoane-Mato, Ana Laiz, Eva Galindez Agirregoikoa, Carlos Montilla, Hye S. Park, Jose A. Pinto Tasende, Juan J. Bethencourt Baute, Beatriz Joven Ibáñez, Elide Toniolo, Julio Ramírez, Nuria Montero, Cristina Pruenza García-Hinojosa, Ana Serrano García and on behalf of Proyecto REAPSER Study Group Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Wed Jan 25 12:52:18 2023 CET ModDate: Wed Jan 25 13:11:53 2023 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 12 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 308858 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- IOJCNK+VnURWPalladioL-Bold Type 1 Custom yes yes no 10 0 ACKGZT+VnURWPalladioL Type 1 Custom yes yes no 15 0 NIZCHE+URWPalladioL-Bold Type 1 Custom yes yes no 19 0 FAJVFC+URWPalladioL-Roma Type 1 Custom yes yes no 24 0 HHEDBV+URWPalladioL-Ital Type 1 Custom yes yes no 28 0 MQYGQX+CMSY10 Type 1 Builtin yes yes no 32 0 XPUSBJ+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 50 0 BSDWLC+TimesNewRomanPSMT Type 1C WinAnsi yes yes no 53 0 QIELJB+CMMI10 Type 1 Builtin yes yes no 81 0 ZIPEAN+EURM10 Type 1 Builtin yes yes no 85 0 QLBIEB+URWPalladioL-BoldItal Type 1 Custom yes yes no 92 0 WABLSD+SFRM1000 Type 1 Custom yes yes no 102 0 BOLMEN+SFRM0900 Type 1 Custom yes yes no 113 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2023-07-26 02:31:09 CEST RepresentationInformation: pmc_9917876.pdf ReportingModule: PDF-hul, Rel. 1.12.1 (2019-04-17) LastModified: 2023-07-20 02:26:13 CEST Size: 308858 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 253 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@44c8afef Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Children: Item: Title: Variables and Measurement Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Item: Title: Sample Size Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Statistical Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Children: Item: Title: Imputation of Missing Data: Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Generation of the Dataset Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Bivariate Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Multivariate Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Children: Item: Title: Bivariate Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Multivariate Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Appendix A Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Info: Title: Moderate-High Disease Activity in Patients with Recent-Onset Psoriatic Arthritis Multivariable Prediction Model Based on Machine Learning Author: Rubén Queiro, Daniel Seoane-Mato, Ana Laiz, Eva Galindez Agirregoikoa, Carlos Montilla, Hye S. Park, Jose A. Pinto Tasende, Juan J. Bethencourt Baute, Beatriz Joven Ibáñez, Elide Toniolo, Julio Ramírez, Nuria Montero, Cristina Pruenza García-Hinojosa, Ana Serrano García and on behalf of Proyecto REAPSER Study Group Subject: The aim was to identify patient- and disease-related characteristics predicting moderate-to-high disease activity in recent-onset psoriatic arthritis (PsA). We performed a multicenter observational prospective study (2-year follow-up, regular annual visits) in patients aged 18 years who fulfilled the CASPAR criteria and had less than 2 years since the onset of symptoms. The moderate-to-high activity of PsA was defined as DAPSA > 14. 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. The sample comprised 158 patients. At the first follow-up visit, 20.8% of the patients who attended the clinic had a moderate-to-severe disease. This percentage rose to 21.2% on the second visit. The variables predicting moderate-high activity were the PsAID score, tender joint count, level of physical activity, and sex. The mean values of the measures of validity of the machine learning algorithms were all high, especially sensitivity (98%; 95% CI: 86.89 100.00). PsAID was the most important variable in the prediction algorithms, reinforcing the convenience of its inclusion in daily clinical practice. Strategies that focus on the needs of women with PsA should be considered. 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