MétaCan
Menu
← Retour à la cohorte
Enregistrement W4411398180 · doi:10.1016/j.ard.2025.05.423

POS0026 Risk of pneumonia-related hospitalisation in patients with giant cell arteritis and small vessel vasculitis: a nationwide population-based cohort study

2025· article· en· W4411398180 sur OpenAlexaboutno aff
Mads Engell Refstrup Sørensen, René Cordtz, K.S. Duch, Maria K. Stilling‐Vinther, L. Dreyer, Katharine A. Kirk, Mette Holland‐Fischer, Salome Kristensen

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueVasculitis and related conditions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineGiant cell arteritisVasculitisCohortPneumoniaArteritisCohort studySystemic vasculitisPopulationInternal medicinePediatricsDiseaseEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Background: Patients with giant cell arteritis (GCA) and small vessel vasculitis (SVV) are treated with potent immunosuppressant drugs putting them at higher risk of infections and hospitalization compared to the general population [1, 2]. Much of existing literature focuses on overall infection risk, however, pneumonias are often reported as the most common infection [1, 2]. Thus, further describing and exploring the risk of pneumonia-related hospitalizations in patients with GCA and SVV are of importance. Objectives: This study aimed to assess the risk of hospitalization with pneumonia (HP) including Pneumocystis jirovecii pneumonia (PJP) among patients with vasculitis. Methods: This study was conducted as a nationwide population-based matched cohort study. It included patients diagnosed with GCA and SVV between 2000 to 2021 using the Danish National Patient Registry. Patients with GCA were identified using a validated algorithm [3], and patients with SVV were identified by using a modified version of two validated algorithms [4, 5] creating two separate cohorts (GCA and SVV). Each patient from the two cohorts was matched with 10 controls based on sex and year of birth. Using the pseudo-observation method, we calculated the cumulative incidence proportion (CIP), risk difference (RD) and relative risk (RR) for HP at one-, two- and five-years follow-up. The Aalen-Johansen estimator was used to plot the cumulative incidence of HP at 5 years of follow-up. Furthermore, we carried out two separate nested case-control analyses using a conditional logistic regression for the multivariable analyses, including all comorbidities and treatments as exposure variables. This was performed exclusively for patients diagnosed after December 31, 2009. In these nested case-control analyses, each patient with GCA or SVV hospitalized with pneumonia was matched on sex, year of birth, and time of diagnosis with two patients with GCA or SVV not being hospitalized with pneumonia. Results: The CIP of HP for 9307 patients with GCA was 6.0% (5.5;6.5), 9.4% (8.8;10.0), and 17.6% (16.8;18.4) after one, two, and five years of follow-up, respectively. The RD compared to matched controls was 3.0% (2.6;3.5), 3.8% (3.2;4.4), and 5.3% (4.5;6.1) after one, two, and five years of follow-up, respectively. Meanwhile, the RR decreased from 2.2 (2.0;2.4) after one year, 1.8 (1.7;1.9) after two years, and 1.5 (1.4;1.6) after five years of follow-up. For the 2401 patients with SVV, the CIP rose from 12.6% (11.2;13.9), to 17.9% (16.3;19.5), and 28.3% (26.4;30.3) during the follow-up period. The RD compared to the matched controls increased from 10.1% (8.8;11.5), 14.8% (12.3;15.4) and 20.4% (17.9;21.8) after one, two and five years of follow-up. Similar to patients with GCA, the RR declined from 7.2 (6.2;8.4), to 5.6 (4.9;6.3), and 3.9 (3.6;4.3). Figure 1 displays the five-year unadjusted CIP. The nested case-control analyses are presented in Table 1. In patients with GCA, all included variables were associated with HP. This included comorbidities, methotrexate treatment, and a cumulative prednisolone intake of more than 1000mg in the six months prior to hospitalization. For patients with SVV, chronic kidney disease, chronic lung disease, previous pneumonia, and obesity were associated with HP. Additionally, mycophenolate and rituximab were also associated with HP. Pneumonia in patients with GCA mainly involved common non-opportunistic bacterial pathogens, similar to the matched controls. In contrast, patients with SVV were more likely to develop opportunistic infections, such as Pseudomonas (3.2% vs 1.3% compared to matched controls) and PJP (6.0% vs 0.0% compared to matched controls). Conclusion: Patients with GCA and SVV demonstrated an increased risk of HP compared to their matched controls. The risk was highest during the early stages of the diseases, likely due to combination of intensive immunosuppressive treatments and the inflammatory burden early on. The greatest risk was observed in patients with SVV, where opportunistic infections were also demonstrated. Patient with recent immunosuppressive therapy and comorbidities were at an increased risk of HP. These findings highlight the need for preventative initiatives, particularly for patients receiving high-dose immunosuppressive therapy. REFERENCES: [1] Faurschou M. et al. Long-term risk and outcome of infection-related hospitalization in granulomatosis with polyangiitis: a nationwide population-based cohort study. Scandinavian Journal of Rheumatology. 2018, Vol. 47, pp. 475-80. [2] Wu J. et al. Incidence of infections associated with oral glucocorticoid dose in people diagnosed with polymyalgia rheumatica or giant cell arteritis: a cohort study in England. Canadian Medical Association Journal. Jun 2019, Vol. 191, 25, pp. 680-8. [3] Hjort PE. et al. Positive Predictive Value of the Giant Cell Arteritis Diagnosis in the Danish National Patient Registry: A Validation Study. Clinical Epidemiology. 2020, pp. 731-36. [4] Nelveg-Kristensen KE. et al. Increasing incidence and improved survival in ANCA-associated vasculitis: A Danish nationwide study. Nephrology Dialysis Transplantation. January 2022, Vol. 37, 1, pp. 63-71. [5] Sreih AG. et al. Development and validation of case-finding algorithms for the identification of patients with anti-neutrophil cytoplasmic antibody-associated vasculitis in large healthcare administrative databases. Pharmacoepidemiology and Drug Safety. 2016, Vol. 25, pp. 1368-74. Acknowledgements: NIL . Disclosure of Interests: Mads Engell Refstrup Sørensen: None declared, René Lindholm Cordtz RC is employed by Novo Nordisk outside of the present study and is a former employee of IQVIA, Kirsten S. Duch: None declared, Maria Kristina Stilling-Vinther: None declared, Lene Dreyer LD has received research grant (paid to her institution) from BMS and AbbVie outside the current manuscript. She is member of the steering committee of the Danish Rheumatology Quality Registry (DANBIO, DRQ), which receives public funding from the hospital owners and funding from pharmaceutical companies, Karina Frahm Kirk: None declared, Mette Holland-Fischer: None declared, Salome Kristensen: None declared. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,224
Écart entre enseignants0,219 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAnnals of the Rheumatic Diseases→Même sujetVasculitis and related conditions→Travaux en français237 207→