Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning

Επιστημονική δημοσίευση - Άρθρο Περιοδικού uoadl:3031572 56 Αναγνώσεις

Μονάδα:
Ερευνητικό υλικό ΕΚΠΑ
Τίτλος:
Prediction of Early Symptom Remission in Two Independent Samples of
First-Episode Psychosis Patients Using Machine Learning
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
Background: Validated clinical prediction models of short-term remission
in psychosis are lacking. Our aim was to develop a clinical prediction
model aimed at predicting 4-6-week remission following a first episode
of psychosis.Method: Baseline clinical data from the Athens First
Episode Research Study was used to develop a Support Vector Machine
prediction model of 4-week symptom remission in first-episode psychosis
patients using repeated nested cross-validation. This model was further
tested to predict 6-week remission in a sample of two independent,
consecutive Danish first-episode cohorts.Results: Of the 179
participants in Athens, 120 were male with an average age of 25.8 years
and average duration of untreated psychosis of 32.8 weeks. 62.9% were
antipsychotic-naive. Fifty-seven percent attained remission after 4
weeks. In the Danish cohort, 31% attained remission. Eleven clinical
scale items were selected in the Athens 4-week remission cohort. These
included the Duration of Untreated Psychosis, Personal and Social
Performance Scale, Global Assessment of Functioning and eight items from
the Positive and Negative Syndrome Scale. This model significantly
predicted 4-week remission status (area under the receiver operator
characteristic curve (ROC-AUC) = 71.45, P <.0001). It also predicted
6-week remission status in the Danish cohort (ROC-AUC = 67.74, P
<.0001), demonstrating reliability.Conclusions: Using items from common
and validated clinical scales, our model significantly predicted early
remission in patients with firstepisode psychosis. Although replicated
in an independent cohort, forward testing between machine learning
models and clinicians’ assessment should be undertaken to evaluate the
possible utility as a routine clinical tool.
Έτος δημοσίευσης:
2022
Συγγραφείς:
Soldatos, Rigas F.
Cearns, Micah
Nielsen, Mette O.
Kollias,
Costas
Xenaki, Lida-Alkisti
Stefanatou, Pentagiotissa
Ralli,
Irene
Dimitrakopoulos, Stefanos
Hatzimanolis, Alex and
Kosteletos, Ioannis
Vlachos, Ilias I.
Selakovic, Mirjana and
Foteli, Stefania
Nianiakas, Nikolaos
Mantonakis, Leonidas and
Triantafyllou, Theoni F.
Ntigridaki, Aggeliki
Ermiliou, Vanessa
and Voulgaraki, Marina
Psarra, Evaggelia
Sorensen, Mikkel E. and
Bojesen, Kirsten B.
Tangmose, Karen
Sigvard, Anne M. and
Ambrosen, Karen S.
Meritt, Toni
Syeda, Warda
Glenthoj, Birte
Y.
Koutsouleris, Nikolaos
Pantelis, Christos
Ebdrup, Bjorn
H.
Stefanis, Nikos
Περιοδικό:
Schizophrenia Bulletin
Εκδότης:
Oxford University Press
Τόμος:
48
Αριθμός / τεύχος:
1
Σελίδες:
122-133
Λέξεις-κλειδιά:
first-episode; psychosis; psychosis; schizophrenia; remission;
prediction; psychopathology; machine learning
Επίσημο URL (Εκδότης):
DOI:
10.1093/schbul/sbab107
Το ψηφιακό υλικό του τεκμηρίου δεν είναι διαθέσιμο.