Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F24%3A10481929" target="_blank" >RIV/00064165:_____/24:10481929 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/24:10481929
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=8pJglPi5KP" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=8pJglPi5KP</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1136/rmdopen-2023-003962" target="_blank" >10.1136/rmdopen-2023-003962</a>
Alternative languages
Result language
angličtina
Original language name
Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis
Original language description
Objective: ANCA-associated vasculitis (AAV) is a relapsing-remitting disease, resulting in incremental tissue injury. The gold-standard relapse definition (Birmingham Vasculitis Activity Score, BVAS>0) is often missing or inaccurate in registry settings, leading to errors in ascertainment of this key outcome. We sought to create a computable phenotype (CP) to automate retrospective identification of relapse using real-world data in the research setting. Methods: We studied 536 patients with AAV and >6 months follow-up recruited to the Rare Kidney Disease registry (a national longitudinal, multicentre cohort study). We followed five steps: (1) independent encounter adjudication using primary medical records to assign the ground truth, (2) selection of data elements (DEs), (3) CP development using multilevel regression modelling, (4) internal validation and (5) development of additional models to handle missingness. Cut-points were determined by maximising the F1-score. We developed a web application for CP implementation, which outputs an individualised probability of relapse. Results: Development and validation datasets comprised 1209 and 377 encounters, respectively. After classifying encounters with diagnostic histopathology as relapse, we identified five key DEs; DE1: change in ANCA level, DE2: suggestive blood/urine tests, DE3: suggestive imaging, DE4: immunosuppression status, DE5: immunosuppression change. F1-score, sensitivity and specificity were 0.85 (95% CI 0.77 to 0.92), 0.89 (95% CI 0.80 to 0.99) and 0.96 (95% CI 0.93 to 0.99), respectively. Where DE5 was missing, DE2 plus either DE1/DE3 were required to match the accuracy of BVAS. Conclusions: This CP accurately quantifies the individualised probability of relapse in AAV retrospectively, using objective, readily accessible registry data. This framework could be leveraged for other outcomes and relapsing diseases.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30226 - Rheumatology
Result continuities
Project
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
RMD Open
ISSN
2056-5933
e-ISSN
2056-5933
Volume of the periodical
10
Issue of the periodical within the volume
2
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
e003962
UT code for WoS article
001222043300004
EID of the result in the Scopus database
2-s2.0-85191922157