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Early warning systems in inpatient anorexia nervosa: A validation of the MARSIPAN ‐based modified early warning system
Author(s) -
Ioannidis Konstantinos,
Serfontein Jaco,
Deakin Julia,
Bruneau Melanie,
Ciobanca Anya,
Holt Leah,
Snelson Sarah,
Stochl Jan
Publication year - 2020
Publication title -
european eating disorders review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.511
H-Index - 67
eISSN - 1099-0968
pISSN - 1072-4133
DOI - 10.1002/erv.2753
Subject(s) - mews , early warning score , anorexia nervosa , warning system , receiver operating characteristic , eating disorders , early warning system , population , medicine , anorexia , psychology , psychiatry , computer science , environmental health , pathology , telecommunications
Objective We aimed to evaluate the validity of a MARSIPAN‐guidance‐adapted 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 machine‐learning‐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.