Literature DB >> 32914178

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.

Gonzalo Salazar de Pablo1,2,3, Erich Studerus4, Julio Vaquerizo-Serrano1,2,3, Jessica Irving1, Ana Catalan1,5,6,7, Dominic Oliver1, Helen Baldwin1, Andrea Danese3,8,9, Seena Fazel10, Ewout W Steyerberg11,12, Daniel Stahl13, Paolo Fusar-Poli1,14,15,16.   

Abstract

BACKGROUND: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.
METHODS: PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.
FINDINGS: Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.
INTERPRETATION: To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.
© The Author(s) 2020. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

Entities:  

Keywords:  evidence; implementation; individualized; prediction; prevention; prognosis; risk; validation

Mesh:

Year:  2021        PMID: 32914178      PMCID: PMC7965077          DOI: 10.1093/schbul/sbaa120

Source DB:  PubMed          Journal:  Schizophr Bull        ISSN: 0586-7614            Impact factor:   9.306


  120 in total

1.  Development and external validation of a prediction rule for an unfavorable course of late-life depression: A multicenter cohort study.

Authors:  O R Maarsingh; M W Heymans; P F Verhaak; B W J H Penninx; H C Comijs
Journal:  J Affect Disord       Date:  2018-04-06       Impact factor: 4.839

2.  External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination.

Authors:  George C M Siontis; Ioanna Tzoulaki; Peter J Castaldi; John P A Ioannidis
Journal:  J Clin Epidemiol       Date:  2014-10-23       Impact factor: 6.437

3.  Obama's Precision Medicine Initiative.

Authors:  Sharon F Terry
Journal:  Genet Test Mol Biomarkers       Date:  2015-03

4.  GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

Authors:  Malgorzata Maciukiewicz; Victoria S Marshe; Anne-Christin Hauschild; Jane A Foster; Susan Rotzinger; James L Kennedy; Sidney H Kennedy; Daniel J Müller; Joseph Geraci
Journal:  J Psychiatr Res       Date:  2018-02-02       Impact factor: 4.791

5.  An Individualized Risk Calculator for Research in Prodromal Psychosis.

Authors:  Tyrone D Cannon; Changhong Yu; Jean Addington; Carrie E Bearden; Kristin S Cadenhead; Barbara A Cornblatt; Robert Heinssen; Clark D Jeffries; Daniel H Mathalon; Thomas H McGlashan; Diana O Perkins; Larry J Seidman; Ming T Tsuang; Elaine F Walker; Scott W Woods; Michael W Kattan
Journal:  Am J Psychiatry       Date:  2016-07-01       Impact factor: 18.112

6.  Prevention of Psychosis: Advances in Detection, Prognosis, and Intervention.

Authors:  Paolo Fusar-Poli; Gonzalo Salazar de Pablo; Christoph U Correll; Andreas Meyer-Lindenberg; Mark J Millan; Stefan Borgwardt; Silvana Galderisi; Andreas Bechdolf; Andrea Pfennig; Lars Vedel Kessing; Therese van Amelsvoort; Dorien H Nieman; Katharina Domschke; Marie-Odile Krebs; Nikolaos Koutsouleris; Philip McGuire; Kim Q Do; Celso Arango
Journal:  JAMA Psychiatry       Date:  2020-07-01       Impact factor: 21.596

7.  Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance.

Authors:  Turker Tekin Erguzel; Serhat Ozekes; Selahattin Gultekin; Nevzat Tarhan; Gokben Hizli Sayar; Ali Bayram
Journal:  Psychiatry Investig       Date:  2015-01-12       Impact factor: 2.505

8.  Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.

Authors:  I R Galatzer-Levy; S Ma; A Statnikov; R Yehuda; A Y Shalev
Journal:  Transl Psychiatry       Date:  2017-03-21       Impact factor: 6.222

9.  Predicting one-year outcome in first episode psychosis using machine learning.

Authors:  Samuel P Leighton; Rajeev Krishnadas; Kelly Chung; Alison Blair; Susie Brown; Suzy Clark; Kathryn Sowerbutts; Matthias Schwannauer; Jonathan Cavanagh; Andrew I Gumley
Journal:  PLoS One       Date:  2019-03-07       Impact factor: 3.240

10.  Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust.

Authors:  Paolo Fusar-Poli; Nomi Werbeloff; Grazia Rutigliano; Dominic Oliver; Cathy Davies; Daniel Stahl; Philip McGuire; David Osborn
Journal:  Schizophr Bull       Date:  2019-04-25       Impact factor: 9.306

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  27 in total

1.  Affective symptom dimensions in early-onset psychosis over time: a principal component factor analysis of the Young Mania Rating Scale and the Hamilton Depression Rating Scale.

Authors:  Marta Rapado-Castro; Carmen Moreno; Gonzalo Salazar de Pablo; Dolores Moreno; Ana Gonzalez-Pinto; Beatriz Paya; Josefina Castro-Fonieles; Inmaculada Baeza; Montserrat Graell; Celso Arango
Journal:  Eur Child Adolesc Psychiatry       Date:  2021-05-30       Impact factor: 4.785

2.  Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort.

Authors:  Nikolaos Koutsouleris; Michelle Worthington; Dominic B Dwyer; Lana Kambeitz-Ilankovic; Rachele Sanfelici; Paolo Fusar-Poli; Marlene Rosen; Stephan Ruhrmann; Alan Anticevic; Jean Addington; Diana O Perkins; Carrie E Bearden; Barbara A Cornblatt; Kristin S Cadenhead; Daniel H Mathalon; Thomas McGlashan; Larry Seidman; Ming Tsuang; Elaine F Walker; Scott W Woods; Peter Falkai; Rebekka Lencer; Alessandro Bertolino; Joseph Kambeitz; Frauke Schultze-Lutter; Eva Meisenzahl; Raimo K R Salokangas; Jarmo Hietala; Paolo Brambilla; Rachel Upthegrove; Stefan Borgwardt; Stephen Wood; Raquel E Gur; Philip McGuire; Tyrone D Cannon
Journal:  Biol Psychiatry       Date:  2021-07-06       Impact factor: 13.382

3.  Evaluating the Effectiveness of Personalized Medicine With Software.

Authors:  Adam Kapelner; Justin Bleich; Alina Levine; Zachary D Cohen; Robert J DeRubeis; Richard Berk
Journal:  Front Big Data       Date:  2021-05-18

4.  Individualized Prediction of Prodromal Symptom Remission for Youth at Clinical High Risk for Psychosis.

Authors:  Michelle A Worthington; Jean Addington; Carrie E Bearden; Kristin S Cadenhead; Barbara A Cornblatt; Matcheri Keshavan; Daniel H Mathalon; Thomas H McGlashan; Diana O Perkins; William S Stone; Ming T Tsuang; Elaine F Walker; Scott W Woods; Tyrone D Cannon
Journal:  Schizophr Bull       Date:  2022-03-01       Impact factor: 7.348

5.  Preventive psychiatry: a blueprint for improving the mental health of young people.

Authors:  Paolo Fusar-Poli; Christoph U Correll; Celso Arango; Michael Berk; Vikram Patel; John P A Ioannidis
Journal:  World Psychiatry       Date:  2021-06       Impact factor: 79.683

6.  A qualitative study on identity in individuals at clinical high risk for psychosis: " … Why does it have to be one thing?".

Authors:  Cansu Sarac; Joseph S DeLuca; Zarina R Bilgrami; Shaynna N Herrera; Jonathan J Myers; Matthew F Dobbs; Shalaila S Haas; Therese L Todd; Agrima Srivastava; Rachel Jespersen; Riaz B Shaik; Yulia Landa; Larry Davidson; Anthony J Pavlo; Cheryl M Corcoran
Journal:  Psychiatr Rehabil J       Date:  2021-06-17

7.  New Electronic Health Records Screening Tools to Improve Detection of Emerging Psychosis.

Authors:  Paolo Fusar-Poli
Journal:  Front Psychiatry       Date:  2021-07-14       Impact factor: 4.157

8.  Neurocognitive Functioning in Individuals at Clinical High Risk for Psychosis: A Systematic Review and Meta-analysis.

Authors:  Ana Catalan; Gonzalo Salazar de Pablo; Claudia Aymerich; Stefano Damiani; Veronica Sordi; Joaquim Radua; Dominic Oliver; Philip McGuire; Anthony J Giuliano; William S Stone; Paolo Fusar-Poli
Journal:  JAMA Psychiatry       Date:  2021-06-16       Impact factor: 25.911

9.  Longitudinal outcome of attenuated positive symptoms, negative symptoms, functioning and remission in people at clinical high risk for psychosis: a meta-analysis.

Authors:  Gonzalo Salazar de Pablo; Filippo Besana; Vincenzo Arienti; Ana Catalan; Julio Vaquerizo-Serrano; Anna Cabras; Joana Pereira; Livia Soardo; Francesco Coronelli; Simi Kaur; Josette da Silva; Dominic Oliver; Natalia Petros; Carmen Moreno; Ana Gonzalez-Pinto; Covadonga M Díaz-Caneja; Jae Il Shin; Pierluigi Politi; Marco Solmi; Renato Borgatti; Martina Maria Mensi; Celso Arango; Christoph U Correll; Philip McGuire; Paolo Fusar-Poli
Journal:  EClinicalMedicine       Date:  2021-06-16

10.  Developing and Validating an Individualized Clinical Prediction Model to Forecast Psychotic Recurrence in Acute and Transient Psychotic Disorders: Electronic Health Record Cohort Study.

Authors:  Stefano Damiani; Grazia Rutigliano; Teresa Fazia; Sergio Merlino; Carlo Berzuini; Luisa Bernardinelli; Pierluigi Politi; Paolo Fusar-Poli
Journal:  Schizophr Bull       Date:  2021-10-21       Impact factor: 7.348

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