Literature DB >> 27337225

Risk Prediction Models in Psychiatry: Toward a New Frontier for the Prevention of Mental Illnesses.

Francesco Bernardini1, Luigi Attademo1, Sean D Cleary2, Charles Luther3, Ruth S Shim4,5, Roberto Quartesan1,6, Michael T Compton7,4,5.   

Abstract

OBJECTIVE: We conducted a systematic, qualitative review of risk prediction models designed and tested for depression, bipolar disorder, generalized anxiety disorder, posttraumatic stress disorder, and psychotic disorders. Our aim was to understand the current state of research on risk prediction models for these 5 disorders and thus future directions as our field moves toward embracing prediction and prevention. DATA SOURCES: Systematic searches of the entire MEDLINE electronic database were conducted independently by 2 of the authors (from 1960 through 2013) in July 2014 using defined search criteria. Search terms included risk prediction, predictive model, or prediction model combined with depression, bipolar, manic depressive, generalized anxiety, posttraumatic, PTSD, schizophrenia, or psychosis. STUDY SELECTION: We identified 268 articles based on the search terms and 3 criteria: published in English, provided empirical data (as opposed to review articles), and presented results pertaining to developing or validating a risk prediction model in which the outcome was the diagnosis of 1 of the 5 aforementioned mental illnesses. We selected 43 original research reports as a final set of articles to be qualitatively reviewed. DATA EXTRACTION: The 2 independent reviewers abstracted 3 types of data (sample characteristics, variables included in the model, and reported model statistics) and reached consensus regarding any discrepant abstracted information.
RESULTS: Twelve reports described models developed for prediction of major depressive disorder, 1 for bipolar disorder, 2 for generalized anxiety disorder, 4 for posttraumatic stress disorder, and 24 for psychotic disorders. Most studies reported on sensitivity, specificity, positive predictive value, negative predictive value, and area under the (receiver operating characteristic) curve.
CONCLUSIONS: Recent studies demonstrate the feasibility of developing risk prediction models for psychiatric disorders (especially psychotic disorders). The field must now advance by (1) conducting more large-scale, longitudinal studies pertaining to depression, bipolar disorder, anxiety disorders, and other psychiatric illnesses; (2) replicating and carrying out external validations of proposed models; (3) further testing potential selective and indicated preventive interventions; and (4) evaluating effectiveness of such interventions in the context of risk stratification using risk prediction models. © Copyright 2017 Physicians Postgraduate Press, Inc.

Entities:  

Mesh:

Year:  2017        PMID: 27337225     DOI: 10.4088/JCP.15r10003

Source DB:  PubMed          Journal:  J Clin Psychiatry        ISSN: 0160-6689            Impact factor:   4.384


  25 in total

1.  Ketamine Alleviates Fear Generalization Through GluN2B-BDNF Signaling in Mice.

Authors:  Muhammad Asim; Bo Hao; Yu-Han Yang; Bu-Fang Fan; Li Xue; Yan-Wei Shi; Xiao-Guang Wang; Hu Zhao
Journal:  Neurosci Bull       Date:  2019-08-23       Impact factor: 5.203

2.  Diagnostic Categories: Provisional, Not Otherwise Classified, or Place-holder?

Authors:  William T Carpenter; Darrel Regier
Journal:  Schizophr Bull       Date:  2016-08-27       Impact factor: 9.306

3.  Clinical High Risk Controversies and Challenge for the Experts.

Authors:  William T Carpenter
Journal:  Schizophr Bull       Date:  2018-02-15       Impact factor: 9.306

4.  Predicting posttraumatic stress disorder following a natural disaster.

Authors:  Anthony J Rosellini; Francisca Dussaillant; José R Zubizarreta; Ronald C Kessler; Sherri Rose
Journal:  J Psychiatr Res       Date:  2017-09-08       Impact factor: 4.791

5.  Mapping infant neurodevelopmental precursors of mental disorders: How synthetic cohorts & computational approaches can be used to enhance prediction of early childhood psychopathology.

Authors:  Joan Luby; Norrina Allen; Ryne Estabrook; Daniel S Pine; Cynthia Rogers; Sheila Krogh-Jespersen; Elizabeth S Norton; Lauren Wakschlag
Journal:  Behav Res Ther       Date:  2019-09-26

6.  Prophylactic Ketamine Attenuates Learned Fear.

Authors:  Josephine C McGowan; Christina T LaGamma; Sean C Lim; Melina Tsitsiklis; Yuval Neria; Rebecca A Brachman; Christine A Denny
Journal:  Neuropsychopharmacology       Date:  2017-01-27       Impact factor: 7.853

7.  Prediction of Schizophrenia Diagnosis by Integration of Genetically Correlated Conditions and Traits.

Authors:  Jingchun Chen; Jian-Shing Wu; Travis Mize; Dandan Shui; Xiangning Chen
Journal:  J Neuroimmune Pharmacol       Date:  2018-10-01       Impact factor: 4.147

8.  A Risk Calculator to Predict the Individual Risk of Conversion From Subthreshold Bipolar Symptoms to Bipolar Disorder I or II in Youth.

Authors:  Boris Birmaher; John A Merranko; Tina R Goldstein; Mary Kay Gill; Benjamin I Goldstein; Heather Hower; Shirley Yen; Danella Hafeman; Michael Strober; Rasim S Diler; David Axelson; Neal D Ryan; Martin B Keller
Journal:  J Am Acad Child Adolesc Psychiatry       Date:  2018-08-07       Impact factor: 8.829

9.  Predicting Personalized Risk of Mood Recurrences in Youths and Young Adults With Bipolar Spectrum Disorder.

Authors:  Boris Birmaher; John A Merranko; Mary Kay Gill; Danella Hafeman; Tina Goldstein; Benjamin Goldstein; Heather Hower; Michael Strober; David Axelson; Neal Ryan; Shirley Yen; Rasim Diler; Satish Iyengar; Michael W Kattan; Lauren Weinstock; Martin Keller
Journal:  J Am Acad Child Adolesc Psychiatry       Date:  2020-01-21       Impact factor: 8.829

10.  Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

Authors:  Anthony J Rosellini; Siyu Liu; Grace N Anderson; Sophia Sbi; Esther S Tung; Evdokia Knyazhanskaya
Journal:  J Psychiatr Res       Date:  2019-12-06       Impact factor: 4.791

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