Early Warning Systems in inpatient Anorexia Nervosa: A validation of the MARSIPAN-based Modified Early Warning System
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11510%2F20%3A10418468" target="_blank" >RIV/00216208:11510/20:10418468 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KoBlQR_ITO" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=KoBlQR_ITO</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/erv.2753" target="_blank" >10.1002/erv.2753</a>
Alternative languages
Result language
angličtina
Original language name
Early Warning Systems in inpatient Anorexia Nervosa: A validation of the MARSIPAN-based Modified Early Warning System
Original language description
Objective: We aimed to evaluate the validity of a MARSIPAN-guidanceadapted Early Warning System (MARSI MEWS) and compare it to the National Early Warning Score (NEWS) and an adapted version of the Physical Risk in Eating Disorders Index (PREDIX), to ascertain whether current practice is comparable to best-practice standards. Methods: We collated 3,937 observations from 36 inpatients from Addenbrookes Hospital over 2017-2018 and used three independent raters to create a "gold standard" of deteriorating cases. We ascertained performance metrics (Receiver Operating Characteristic Area Under the curve) for MARSI MEWS, NEWS and PREDIX; we also tested the proof of concept of a machinelearning-based early-warning-system (ML-EWS) using cross-validation and out-of-sample prediction of cases. Results: The MARSI MEWS system showed higher ROC AUC (0.916) compared to NEWS (0.828) or PREDIX (0.865). ML-EWS (random forest) performed well at independent samples analysis (0.980) and multilevel analysis (0.922). Conclusion: MARSI MEWS seems most suitable for identifying critically deteriorating cases in anorexia nervosa inpatient population. We did not examine community practice in which the PREDIX arguably remains the best to ascertain deteriorating cases. Our results also provide a first proof of concept for the development of artificial-intelligence-based early warning systems in anorexia nervosa. Implications for inpatient clinical practice in eating disorders are discussed.
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
30306 - Sport and fitness sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
European Eating Disorders Review
ISSN
1072-4133
e-ISSN
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Volume of the periodical
28
Issue of the periodical within the volume
5
Country of publishing house
GB - UNITED KINGDOM
Number of pages
8
Pages from-to
551-558
UT code for WoS article
000540259300001
EID of the result in the Scopus database
2-s2.0-85086433185