Evidence map›Paper›PMID 33561292›Full record

ArticleSchizophrenia bulletin2021

Classification of Psychoses Based on Immunological Features: A Machine Learning Study in a Large Cohort of First-Episode and Chronic Patients.

Paolo Enrico, Giuseppe Delvecchio, Nunzio Turtulici, Alessandro Pigoni, Filippo Maria Villa, Cinzia Perlini, Maria Gloria Rossetti, Marcella Bellani, Antonio Lasalvia, Chiara Bonetto and 14 more

Open access · greenAbstract read
In one paragraph

Article in Schizophrenia bulletin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
0.7field-weighted citation impact, top 36% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors at 11 institutions in 1 country.

Paolo EnricoDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
Giuseppe DelvecchioDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.ORCID 0000-0003-4750-1980
Nunzio TurtuliciDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
Alessandro PigoniMoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy.
Filippo Maria VillaScientific Institute, IRCCS E. Medea, Bosisio Parini, Lecco, Italy.
Cinzia PerliniDepartment of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy.
Maria Gloria RossettiDepartment of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.
Marcella BellaniDepartment of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.
Antonio LasalviaDepartment of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.
Chiara BonettoDepartment of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.
Paolo ScoccoDepartment of Mental Health, AULSS 6 Euganea, Padua, Italy.
Armando D'AgostinoDepartment of Health Sciences, San Paolo University Hospital, University of Milan, Milan, Italy.ORCID 0000-0002-2126-799X
Stefano TorresaniDepartment of Psychiatry, ULSS, Bolzano Suedtiroler Sanitaetbetrieb- Azienda Sanitaria dell'Alto Adige, Bolzano, Italy.
Massimiliano ImbesiDepartment of Psychiatry, AUSL Emilia, Piacenza, Italy.
Francesca BelliniDepartment of Psychiatry, AUSL Romagna, Rimini, Italy.
Angela VeroneseDepartment of Mental Health, AULSS 6 Euganea, Padua, Italy.
Luisella Bocchio-ChiavettoGenetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Massimo GennarelliGenetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.
Matteo BalestrieriUnit of Psychiatry, Department of Medicine (DAME), University of Udine, Udine, Italy.
Gualtiero I ColomboCentro Cardiologico Monzino IRCCS, Immunology and Functional Genomics Unit, Milan, Italy.
Annamaria FinardiClinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy.
Mirella RuggeriDepartment of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.
Roberto FurlanClinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy.
Paolo BrambillaDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
University of Verona · ITUniversity of Milan · ITIRCCS Ospedale San Raffaele · ITAzienda Unità Sanitaria Locale Piacenza · ITCentro Cardiologico Monzino · ITCentro San Giovanni di Dio Fatebenefratelli · ITIMT School for Advanced Studies Lucca · ITIRCCS Eugenio Medea · ITOspedale di Bolzano · ITUniversità degli Studi eCampus · ITUniversity of Udine · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For several years, the role of immune system in the pathophysiology of psychosis has been well-recognized, showing differences from the onset to chronic phases. Our study aims to implement a biomarker-based classification model suitable for the clinical management of psychotic patients. A machine learning algorithm was used to classify a cohort of 362 subjects, including 160 first-episode psychosis patients (FEP), 70 patients affected by chronic psychiatric disorders (schizophrenia, bipolar disorder, and major depressive disorder) with psychosis (CRO) and 132 health controls (HC), based on mRNA transcript levels of 56 immune genes. Models distinguished between FEP, CRO, and HC and between the subgroup of drug-free FEP and HC with a mean accuracy of 80.8% and 90.4%, respectively. Interestingly, by using the feature importance method, we identified some immune gene transcripts that contribute most to the classification accuracy, possibly giving new insights on the immunopathogenesis of psychosis. Therefore, our results suggest that our classification model has a high translational potential, which may pave the way for a personalized management of psychosis.

Indexed as

AdultChronic DiseaseCohort StudiesFemaleHumansMachine LearningMaleMiddle AgedPsychotic Disordersimmune biomarkersimmunitymachine learningpersonalized medicinepsychosistranscriptomics

Identifiers

PMID33561292
PMCPMC8266656
OpenAlexW3127467784

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.