PMID- 32305218
OWN - NLM
STAT- MEDLINE
DCOM- 20210218
LR  - 20210924
IS  - 1873-2402 (Electronic)
IS  - 0006-3223 (Linking)
VI  - 88
IP  - 4
DP  - 2020 Aug 15
TI  - Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk 
      Syndromes: A Meta-analytic View on the State of the Art.
PG  - 349-360
LID - S0006-3223(20)30098-6 [pii]
LID - 10.1016/j.biopsych.2020.02.009 [doi]
AB  - BACKGROUND: The clinical high risk (CHR) paradigm has facilitated research into 
      the underpinnings of help-seeking individuals at risk for developing psychosis, 
      aiming at predicting and possibly preventing transition to the overt disorder. 
      Statistical methods such as machine learning and Cox regression have provided the 
      methodological basis for this research by enabling the construction of diagnostic 
      models (i.e., distinguishing CHR individuals from healthy individuals) and 
      prognostic models (i.e., predicting a future outcome) based on different data 
      modalities, including clinical, neurocognitive, and neurobiological data. 
      However, their translation to clinical practice is still hindered by the high 
      heterogeneity of both CHR populations and methodologies applied. METHODS: We 
      systematically reviewed the literature on diagnostic and prognostic models built 
      on Cox regression and machine learning. Furthermore, we conducted a meta-analysis 
      on prediction performances investigating heterogeneity of methodological 
      approaches and data modality. RESULTS: A total of 44 articles were included, 
      covering 3707 individuals for prognostic studies and 1052 individuals for 
      diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR 
      patients could be classified against healthy control subjects with 78% 
      sensitivity and 77% specificity. Across prognostic models, sensitivity reached 
      67% and specificity reached 78%. Machine learning models outperformed those 
      applying Cox regression by 10% sensitivity. There was a publication bias for 
      prognostic studies yet no other moderator effects. CONCLUSIONS: Our results may 
      be driven by substantial clinical and methodological heterogeneity currently 
      affecting several aspects of the CHR field and limiting the clinical 
      implementability of the proposed models. We discuss conceptual and methodological 
      harmonization strategies to facilitate more reliable and generalizable models for 
      future clinical practice.
CI  - Copyright (c) 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All 
      rights reserved.
FAU - Sanfelici, Rachele
AU  - Sanfelici R
AD  - Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, 
      Germany; Max Planck School of Cognition, Leipzig, Germany.
FAU - Dwyer, Dominic B
AU  - Dwyer DB
AD  - Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, 
      Germany.
FAU - Antonucci, Linda A
AU  - Antonucci LA
AD  - Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, 
      Germany; Department of Education, Psychology, and Communication, University of 
      Bari "Aldo Moro," Bari, Italy.
FAU - Koutsouleris, Nikolaos
AU  - Koutsouleris N
AD  - Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, 
      Germany; Max Planck Institute of Psychiatry Munich, Munich, Germany. Electronic 
      address: nikolaos.koutsouleris@med.uni-muenchen.de.
LA  - eng
PT  - Journal Article
PT  - Meta-Analysis
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Systematic Review
DEP - 20200220
PL  - United States
TA  - Biol Psychiatry
JT  - Biological psychiatry
JID - 0213264
SB  - IM
CIN - Biol Psychiatry. 2021 Sep 15;90(6):e37-e38. PMID: 34001369
CIN - Biol Psychiatry. 2021 Sep 15;90(6):e33-e35. PMID: 34001370
MH  - Humans
MH  - Machine Learning
MH  - Prognosis
MH  - *Psychotic Disorders/diagnosis
MH  - Risk Factors
MH  - Syndrome
OTO - NOTNLM
OT  - Biomarkers
OT  - Clinical psychobiology
OT  - Machine learning
OT  - Predictive psychiatry
OT  - Psychosis
OT  - Translational medicine
EDAT- 2020/04/20 06:00
MHDA- 2021/02/20 06:00
CRDT- 2020/04/20 06:00
PHST- 2019/09/30 00:00 [received]
PHST- 2020/01/25 00:00 [revised]
PHST- 2020/02/06 00:00 [accepted]
PHST- 2020/04/20 06:00 [pubmed]
PHST- 2021/02/20 06:00 [medline]
PHST- 2020/04/20 06:00 [entrez]
AID - S0006-3223(20)30098-6 [pii]
AID - 10.1016/j.biopsych.2020.02.009 [doi]
PST - ppublish
SO  - Biol Psychiatry. 2020 Aug 15;88(4):349-360. doi: 10.1016/j.biopsych.2020.02.009. 
      Epub 2020 Feb 20.