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IRIS
Objectives: 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. Methods: 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 identify patient subgroups based on comorbidity profiles and PROMIS outcomes. Results: The study included 2561 patients (1907 RA, 311 PsA, 343 AS). After adjusting for demographic factors, no significant differences in PROMIS scores were observed among the three groups, regardless of disease activity status. Clustering analysis identified four distinct patient groups: low burden, comorbid PsA/AS, low burden with depression and high-burden RA. Feature importance analysis revealed PROMIS global physical health as the strongest determinant of cluster assignment, followed by depression and diagnosis. The comorbid PsA/AS and high-burden RA clusters showed a higher prevalence of comorbidities (56.47% and 69.7%, respectively) and depression (41.18% and 41.67%, respectively), along with poorer PROMIS outcomes. Conclusion: Disease burden in inflammatory arthritis is determined by a complex interplay of factors, with physical health status and depression playing crucial roles. The identification of distinct patient clusters suggests the need for a paradigm shift towards more integrated care approaches that equally emphasize physical and mental health, regardless of the underlying diagnosis.
Objectives: 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. Methods: 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 identify patient subgroups based on comorbidity profiles and PROMIS outcomes. Results: The study included 2561 patients (1907 RA, 311 PsA, 343 AS). After adjusting for demographic factors, no significant differences in PROMIS scores were observed among the three groups, regardless of disease activity status. Clustering analysis identified four distinct patient groups: low burden, comorbid PsA/AS, low burden with depression and high-burden RA. Feature importance analysis revealed PROMIS global physical health as the strongest determinant of cluster assignment, followed by depression and diagnosis. The comorbid PsA/AS and high-burden RA clusters showed a higher prevalence of comorbidities (56.47% and 69.7%, respectively) and depression (41.18% and 41.67%, respectively), along with poorer PROMIS outcomes. Conclusion: Disease burden in inflammatory arthritis is determined by a complex interplay of factors, with physical health status and depression playing crucial roles. The identification of distinct patient clusters suggests the need for a paradigm shift towards more integrated care approaches that equally emphasize physical and mental health, regardless of the underlying diagnosis.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/3021833
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.
