Literature DB >> 34535800

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

Rigas F Soldatos1,2,3, Micah Cearns2,4, Mette Ø Nielsen5,6, Costas Kollias1, Lida-Alkisti Xenaki1, Pentagiotissa Stefanatou1, Irene Ralli1, Stefanos Dimitrakopoulos1, Alex Hatzimanolis1, Ioannis Kosteletos1, Ilias I Vlachos1, Mirjana Selakovic1, Stefania Foteli1, Nikolaos Nianiakas1, Leonidas Mantonakis1, Theoni F Triantafyllou1, Aggeliki Ntigridaki1, Vanessa Ermiliou1, Marina Voulgaraki1, Evaggelia Psarra1, Mikkel E Sørensen5, Kirsten B Bojesen5, Karen Tangmose5,6, Anne M Sigvard5,6, Karen S Ambrosen5, Toni Meritt2, Warda Syeda2, Birte Y Glenthøj5,6, Nikolaos Koutsouleris3,7, Christos Pantelis2,3, Bjørn H Ebdrup2,5,6, Nikos Stefanis1,3,8.   

Abstract

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-naïve. 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 first-episode 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.
© The Author(s) 2021. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  first-episode/psychosis; machine learning; prediction; psychopathology; psychosis; remission; schizophrenia

Mesh:

Year:  2022        PMID: 34535800      PMCID: PMC8781312          DOI: 10.1093/schbul/sbab107

Source DB:  PubMed          Journal:  Schizophr Bull        ISSN: 0586-7614            Impact factor:   7.348


  72 in total

1.  Duration of untreated psychosis and neurocognitive functioning in first-episode psychosis: a systematic review and meta-analysis.

Authors:  K Allott; D Fraguas; C F Bartholomeusz; C M Díaz-Caneja; C Wannan; E M Parrish; G P Amminger; C Pantelis; C Arango; P D McGorry; M Rapado-Castro
Journal:  Psychol Med       Date:  2017-11-27       Impact factor: 7.723

Review 2.  Duration of untreated psychosis as predictor of long-term outcome in schizophrenia: systematic review and meta-analysis.

Authors:  Matti Penttilä; Erika Jääskeläinen; Noora Hirvonen; Matti Isohanni; Jouko Miettunen
Journal:  Br J Psychiatry       Date:  2014-08-01       Impact factor: 9.319

3.  Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach.

Authors:  Samuel P Leighton; Rachel Upthegrove; Rajeev Krishnadas; Michael E Benros; Matthew R Broome; Georgios V Gkoutos; Peter F Liddle; Swaran P Singh; Linda Everard; Peter B Jones; David Fowler; Vimal Sharma; Nicholas Freemantle; Rune H B Christensen; Nikolai Albert; Merete Nordentoft; Matthias Schwannauer; Jonathan Cavanagh; Andrew I Gumley; Max Birchwood; Pavan K Mallikarjun
Journal:  Lancet Digit Health       Date:  2019-09-12

4.  Identifying schizophrenia subgroups using clustering and supervised learning.

Authors:  Alexandra Talpalaru; Nikhil Bhagwat; Gabriel A Devenyi; Martin Lepage; M Mallar Chakravarty
Journal:  Schizophr Res       Date:  2019-08-24       Impact factor: 4.939

5.  Failures of metacognition and lack of insight in neuropsychiatric disorders.

Authors:  Anthony S David; Nicholas Bedford; Ben Wiffen; James Gilleen
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2012-05-19       Impact factor: 6.237

6.  Rating depressive patients.

Authors:  M Hamilton
Journal:  J Clin Psychiatry       Date:  1980-12       Impact factor: 4.384

Review 7.  Early intervention in psychosis. The critical period hypothesis.

Authors:  M Birchwood; P Todd; C Jackson
Journal:  Br J Psychiatry Suppl       Date:  1998

Review 8.  Current Data on and Clinical Insights into the Treatment of First Episode Nonaffective Psychosis: A Comprehensive Review.

Authors:  Benedicto Crespo-Facorro; Jose Maria Pelayo-Teran; Jacqueline Mayoral-van Son
Journal:  Neurol Ther       Date:  2016-08-23

9.  Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.

Authors:  Richard Dinga; Andre F Marquand; Dick J Veltman; Aartjan T F Beekman; Robert A Schoevers; Albert M van Hemert; Brenda W J H Penninx; Lianne Schmaal
Journal:  Transl Psychiatry       Date:  2018-11-05       Impact factor: 6.222

Review 10.  Early intervention in psychosis: obvious, effective, overdue.

Authors:  Patrick D McGorry
Journal:  J Nerv Ment Dis       Date:  2015-05       Impact factor: 2.254

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