Evidence map›Paper›PMID 42503589›Full record

ArticleClinical rheumatology2026

Association of the first-year cumulative vascular damage index with relapse risk in granulomatosis with polyangiitis: a retrospective cohort analysis.

Oh Chan Kwon, Jang Woo Ha, Yong-Beom Park, Sang-Won Lee

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Article in Clinical rheumatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Oh Chan KwonDivision of Rheumatology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.ORCID http://orcid.org/0000-0001-7962-3697
Jang Woo HaDivision of Rheumatology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi-do, Republic of Korea.ORCID http://orcid.org/0000-0002-3307-5215
Yong-Beom ParkDivision of Rheumatology, Department of Internal Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.ORCID http://orcid.org/0000-0003-4695-8620
Sang-Won LeeDivision of Rheumatology, Department of Internal Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea. sangwonlee@yuhs.ac.ORCID http://orcid.org/0000-0002-8038-3341

Funding

Eisai Korea 4-2024-0700Samil Pharmaceutical Co., Ltd. 4-2025-1087Samil Pharmaceutical Co., Ltd. LFN-26-I01Samsung Bioepis Co., Ltd. 4-2025-1066Yuhan 4-2025-0044
6 · The paper itself

Abstract

objectivesTo evaluate whether the first-year cumulative vascular damage index (VDI) could serve as an independent predictor of relapse in patients with granulomatosis with polyangiitis (GPA).

methodsWe retrospectively analysed 39 patients with GPA enrolled in a single-centre cohort. The first-year cumulative VDI was calculated as the area under the curve (AUC) of VDI scores during the first year using the trapezoidal rule. The earliest VDI, defined as the first VDI recorded ≥ 3 months after AAV diagnosis or AAV symptom onset, was also assessed as a contextual reference. Relapse was defined as the recurrence or new onset of vasculitic manifestations after ≥ 3 months of remission. The predictive accuracy for relapse was assessed using receiver operating characteristic (ROC) curves. Multivariable Cox proportional hazard models were performed to assess whether the first-year cumulative VDI is independently associated with the risk of relapse.

resultsDuring a median follow-up of 40.6 (13.9-64.7) months, relapse occurred in 18 (46.2%) patients. The first-year cumulative VDI (AUC = 0.817) showed a significant predictive value for relapse, with a numerically higher accuracy than the earliest VDI (AUC = 0.734), although the difference was not statistically significant (p = 0.272). In the multivariable Cox model, the first-year cumulative VDI was significantly associated with relapse risk (adjusted hazard ratio [HR] = 6.536, 95% confidence interval [CI] = 1.746-24.460, p = 0.005), whereas the earliest VDI was not (adjusted HR = 1.358, 95% CI = 0.976-1.889, p = 0.070).

conclusionThe first-year cumulative VDI independently predicts relapse in GPA, and outperformed early single time-point damage assessment. Our findings suggest that early cumulative damage evaluation may improve relapse risk stratification. Key Points • We assessed whether VDI can predict relapse in patients with GPA. • The first-year cumulative VDI was found to independently predict relapse in GPA. • Early cumulative damage evaluation may improve relapse risk stratification.

Indexed as

Granulomatosis with PolyangiitisAdultAgedFemaleHumansMaleMiddle AgedProportional Hazards ModelsRecurrenceRetrospective StudiesRisk FactorsROC CurveSeverity of Illness IndexGranulomatosis with polyangiitisRelapseRiskVascular damage index

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.