Multivariable index for assessing the activity and predicting all-cause mortality in antineutrophil cytoplasmic antibody-associated vasculitis.

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ID: 17573
2019
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Abstract
So far, there has been no tool to estimate activity at diagnosis and predict all-cause mortality in patients with ANCA-associated vasculitis (AAV). Hence, we determined the initial predictors of them in patients with AAV.We retrospectively reviewed the medical records of 182 patients with AAV. Severe AAV was defined as Birmingham Vasculitis Activity Score (BVAS) ≥ 16. The cutoffs were extrapolated by the receiver operator characteristic (ROC) curve. The odds ratio (OR) and the relative risk (RR) were assessed using the multivariable logistic regression analysis and the chi-square test, respectively.In the comparison analysis, patients with severe AAV exhibited the higher neutrophil and platelet counts, creatinine, erythrocyte sedimentation rate and C-reactive protein, and the lower lymphocyte count, hemoglobin, and serum albumin than those without. In the multivariable logistic regression analysis, creatinine ≥ 0.9 mg/dL (OR 2.264), lymphocyte count ≤ 1430.0/mm (OR 1.856), and hemoglobin ≤ 10.8 g/dL (OR 2.085) were associated with severe AAV. We developed a new equation of a multivariable index for AAV (MVIA) = 0.6 × (Lymphocyte count ≤ 1430.0/mm ) + 0.7 × (Hemoglobin ≤ 10.8 g/dL) + 0.8 × (Creatinine ≥ 0.9 mg/dL). The optimal cutoff of MVIA for severe AAV was set as 1.35. Severe AAV was identified more frequently in patients with MVIA at diagnosis ≥1.35 than those without (RR 4.432). Patients with MVIA at diagnosis ≥1.35 exhibited the lower cumulative patient survival rate than those without.Multivariable index for AAV could assess the cross-sectional activity and predict all-cause mortality in patients with AAV.
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kim2019multivariablejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kim, Minyoung Kevin;Choi, Hyeok;Kim, Jae Yeon;Song, Jason Jungsik;Park, Yong-Beom;Lee, Sang-Won;
Journal journal of clinical laboratory analysis
Year 2019
DOI
10.1002/jcla.23022
URL
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