41. Subgrouping and personalized risk evaluation for outcome prediction in ANCA associated vasculitis (AAV)
Creators: name: Wójcik1, Krzysztof
name: Ćmiel2, Adam
name: Satława10, Tadeusz
name: Lichołai1, Sabina
name: Wawrzycka-Adamczyk1, Katarzyna
name: Biedroń1, Grzegorz
name: Masiak3, Anna
name: Zdrojewski3, Zbigniew
name: Storoniak3, Hanna
name: Bułło-Piątecka3, Barbara
name: Dębska-Ślizień3, Alicja
name: Radosław Jeleniewicz4
name: Majdan4, Maria
name: Jakuszko5, Katarzyna
name: Augustyniak-Bartosik5, Hanna
name: Krajewska5, Magdalena
name: Brzosko6, Iwona
name: Brzosko6, Marek
name: Kur-Zalewska7, Joanna
name: Tłustochowicz7, Witold
name: Madej5, Marta
name: Hawrot-Kawecka8, Anna
name: Kucharz8, Eugeniusz
name: Piotr Głuszko9,
name: Małgorzata Wisłowska9
name: Miłkowska-Dymanowska11, Joanna
name: Lewandowska-Polak11, Anna
name: Zalewska12, Joanna
name: Makowska11, Joanna
name: Gubała10, Tomasz
name: Malawski10, Maciej
name: Musiał1, Jacek
Publisher: name: Zenodo
Year: 2022
Subjects: Vasculitis
ANCA
Abstracts
Giant Cell Arthritis
MPA
GPA
EGPA
Takayasu
IgA vasculitis

description: <strong>Objectives: </strong>In the most severe cases AAV can lead to end stage kidney disease or death. Since etiology and detailed pathogenesis of AAV is not known, the prediction of disease outcome is challenging. Early identification of patients who are likely to develop disease exacerbation is crucial, as timely intensification of treatment may reverse the course of the disorder, preventing severe organ dysfunction (e.g. end stage kidney disease) and death. Thus, there is an unmet need for tools able to identify patients with the high risk of organ dysfunction and death. <strong>Methods:</strong> We present here two approaches (sub phenotyping of patients and individual patient computed risk evaluation) to identify subjects with high risk of chronic replacement therapy (CRRT) and death based on retrospective data from Polish national AAV registry (POLVAS). The parameters used were: demographic data and laboratory parameters, specific organ involvement, ANCA specificity and time between selected stages of the disease. First approach is based on latent class analysis (LCA) followed by logistic regression to subcategorize patients (GPA, n = 417, MPA, n = 106) and to identify sub phenotypes with the highest risk of CRRT and death. Second approach is based on two machine learning (ML) classifiers, which by analyzing clinical information allow assigning computed risk for CRRT and death in an individual patient (GPA, n = 565, MPA, n = 135). We have evaluated several different approaches to build the ML models (including logistic regression, support vector machines, random forests), and obtained the best results for the gradient boosting algorithm implementation called LightGBM. It works as a sequential ensemble of so-called weak learners (decision trees) finally combined in a one prediction model. <strong>Results:</strong> LCA used on our AAV cohort identified four sub phenotypes, including three previously proposed and revealing a fourth clinically relevant sub phenotype. Logistic regression analysis revealed significant differences in the risk of CRRT and death between these subgroups. Using ML approach, we obtained a prediction model performance (ROC AUC of 0.85 for CRRT and 0.82 for death) which we consider clinically relevant. <strong>Conclusions:</strong> We consider results obtained encouraging. They may offer a new insight into the course of the vasculitis based on data available at diagnosis and create solid foundations for better capture of temporal relations between clinical events and towards potential clinical decision support system. <strong>Disclosures:</strong> None