biostudies-literatureUnknown9(2)Venerito V<h4>Objectives</h4>To comprehensively compare the disease burden among patients with RA, PsA and AS using Patient-Reported Outcome Measurement Information System (PROMIS) scores and to identify distinct patient clusters based on comorbidity profiles and PROMIS outcomes.<h4>Methods</h4>Data from the global COVID-19 Vaccination in Autoimmune Diseases (COVAD) 2 e-survey were analysed. Patients with RA, PsA or AS undergoing treatment with DMARDs were included. PROMIS scores (global physical health, global mental health, fatigue 4a and physical function short form 10a), comorbidities and other variables were compared among the three groups, stratified by disease activity status. Unsupervised hierarchical clustering with eXtreme Gradient Boosting feature importance analysis was performed to idenRheumatology advances in practicerkaf031https://www.ebi.ac.uk/biostudies/studies/S-EPMC12007600biostudies-literatureDisease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset.PMC12007600Goswami RPR NPatel AAkarawatcharangura Goo PMadu NHDistler OCabriza KSalinas RGVelikova TCampagne JFonseca JESainaghi PPDel Papa NJain AGromova MAChinchay LCalapaqui WSierra-Zorita RChinoy HOrsolini GFuentes-Silva YSebastiani MPatro PSPalalane EVenerito VZuleta WGRArbelaez ADarooka NDanielli MGSchreiber KOlaosebikan HHarifi GPande ARShinjo SKFazal ZZParisi SMilchert MCansu DUBecky AKuwana MEl Bouchti ILilleker JBYerima ABaez JTAjmani SCavagna LCajas JChowdhury ACLandon-Cardinal ODay JYaadav PSalim BBernaola JPKimura NTomaras SSebastiani GDBommakanti KTPhatak SYoshida ASilva JAPIdania EMMathew AJIma-Edomwonyi UNagy-Vincze MShumnalieva RPandya SCShenoy PArredondo GAndreoli LKaneko YUgarte-Gil MFGupta VLimaye VOrdonez ABFornaro MMaurier FShaharir SSManesh MMHudson MSaha SSato SAgarwal VAlonzo HGutierrez CETFathi HMGrignaschi SSo HChandwar KFabiola ALa Torre IGPilania RKTan CLGhodke AFusaro ETehozol EAZMohammed RHAGiannini MColunga-Pedraza IJGriger ZBohdana DSharma AEdgar Gracia-Ramos ALlerena GARPimentel VVaidya BLini DYıldırım RCaballero-Uribe CVKoussougbo ODGupta LSerrano JRAharonov ONunez LSGovoni MLangguth DHalabi HChatterjee TKatchamart WPastelin CBSAirenakho EKavadichanda CGAranega RDe Angelis RMakol AGaur PSSen PFranceschini FGiollo ABocci EBQuartuccio LCachafeiro-Vilar ADel Vescovo SChavez NJagtap KMerayo-Chalico JMamadapur MTraboco LSNakashima RBaba ZGheita TAMeyer AWibowo SAKParodis INune AHo EJGZiade NHidalgo ERSambataro GColaman NTan ALGono TCristian VMGasparyan AYProft FNHolzer MTKnitza JOkwara CCIannone FHoff LSBautista GGNeedham MSaenz RJoshi MObiols AQHasan ATMTKardes SShukla AZimba OLosanto JBarman BLoarce-Martos JDedeke ICOVAD StudyAlunno AArrieta DDey DHmamouchi IAsranna APrieto-Gonzalez SQuezada IPauling JDWincup CSrivastav NSaavedra MAJavaid MChen YMEl Kibbi LSingh YPRanjan RfalseDisease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset.<h4>Objectives</h4>To comprehensively compare the disease burden among patients with RA, PsA and AS using Patient-Reported Outcome Measurement Information System (PROMIS) scores and to identify distinct patient clusters based on comorbidity profiles and PROMIS outcomes.<h4>Methods</h4>Data from the global COVID-19 Vaccination in Autoimmune Diseases (COVAD) 2 e-survey were analysed. Patients with RA, PsA or AS undergoing treatment with DMARDs were included. PROMIS scores (global physical health, global mental health, fatigue 4a and physical function short form 10a), comorbidities and other variables were compared among the three groups, stratified by disease activity status. Unsupervised hierarchical clustering with eXtreme Gradient Boosting feature importance analysis was performed to iden2025-01-01T00:00:00Z20252025-07-03T03:04:32.824Z2025-07-03T03:04:32.824ZS-EPMC120076004025663310.1093/rap/rkaf031