POS0026 Risk of pneumonia-related hospitalisation in patients with giant cell arteritis and small vessel vasculitis: a nationwide population-based cohort study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".