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Pigoni A, Delvecchio G, Turtulici N, Madonna D, Pietrini P, Cecchetti L, Brambilla P. Machine learning and the prediction of suicide in psychiatric populations: a systematic review. Transl Psychiatry 2024; 14:140. [PMID: 38461283 PMCID: PMC10925059 DOI: 10.1038/s41398-024-02852-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.
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Affiliation(s)
- Alessandro Pigoni
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Domenico Madonna
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Pietro Pietrini
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Luca Cecchetti
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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Biagianti B, Lisi I, Di Liberto A, Turtulici N, Foti G, Zito S, Ginex V, Fornoni C, Gallo F, Cantù F, Tombola V, Di Fede V, Rossetti MG, Colombo E, Stocchetti N, Zanier ER, Bellani M, Bressi C, Brambilla P. Feasibility and preliminary efficacy of brief tele-psychotherapy for COVID-19 patients and their first-degree relatives. J Affect Disord 2023; 330:300-308. [PMID: 36934855 PMCID: PMC10022466 DOI: 10.1016/j.jad.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 03/01/2023] [Accepted: 03/11/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND The SARS-CoV-2 pandemic compromised the mental health of COVID-19 patients and their family members. Due to social distancing and lockdown measures, a remote, tele-psychotherapy program for former or current COVID-19 patients and their relatives was implemented. OBJECTIVE The primary goal of this project was to evaluate intervention feasibility. The secondary aim was to assess the impact of the intervention by means of pre-post psychological changes. METHODS After a phone-based eligibility screening and remote neuropsychological testing, participants completed online self-reports assessing baseline COVID-related psychopathology. Next, participants attended eight tele-psychotherapy sessions. After treatment, the online self-reports were completed again. RESULTS Of 104 enrolled participants, 88 completed the intervention (84.6 % completion rate). Significant pre-post improvements were observed for generalized anxiety (d = 0.38), depression (d = 0.37), insomnia (d = 0.43), post-traumatic psychopathology (d = 0.54), and general malaise (d = 0.31). Baseline cluster analysis revealed a subgroup of 41 subjects (47.6 %) with no psychopathology, and a second subgroup of 45 subject (52.3 %) with moderate severity. Thirty-three percent of the second group reached full symptom remission, while 66 % remained symptomatic after treatment. CONCLUSIONS Remote brief tele-psychotherapy for COVID-19 patients and their first-degree relatives is feasible and preliminary efficacious at reducing COVID-related psychopathology in a subgroup of patients. Further research is needed to investigate distinct profiles of treatment response.
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Affiliation(s)
- Bruno Biagianti
- Department of Psychology, University of Milan Bicocca, 20126, Milano, Italy
| | - Ilaria Lisi
- Laboratory of Acute Brain Injury and Therapeutic Strategies, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milano, Italy
| | - Asia Di Liberto
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Nunzio Turtulici
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Giuseppe Foti
- Department of Psychology, University of Milan Bicocca, 20126, Milano, Italy
| | - Silvana Zito
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Valeria Ginex
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Chiara Fornoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Francesca Gallo
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Filippo Cantù
- Department of Psychology, University of Milan Bicocca, 20126, Milano, Italy
| | - Valentina Tombola
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Viviana Di Fede
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Maria Gloria Rossetti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Elisa Colombo
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Nino Stocchetti
- Department of Anaesthesia and Critical Care, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Elisa R Zanier
- Laboratory of Acute Brain Injury and Therapeutic Strategies, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milano, Italy
| | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, 37134 Verona, Italy
| | - Cinzia Bressi
- Department of Psychology, University of Milan Bicocca, 20126, Milano, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Paolo Brambilla
- Department of Psychology, University of Milan Bicocca, 20126, Milano, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy.
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3
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Enrico P, Delvecchio G, Turtulici N, Aronica R, Pigoni A, Squarcina L, Villa FM, Perlini C, Rossetti MG, Bellani M, Lasalvia A, Bonetto C, Scocco P, D'Agostino A, Torresani S, Imbesi M, Bellini F, Veronese A, Bocchio-Chiavetto L, Gennarelli M, Balestrieri M, Colombo GI, Finardi A, Ruggeri M, Furlan R, Brambilla P. Identification of an inflammation-associated psychosis onset subgroup by applying unsupervised machine learning to whole-blood expression levels of immune gene transcripts. Journal of Affective Disorders Reports 2023. [DOI: 10.1016/j.jadr.2023.100540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
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4
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Enrico P, Delvecchio G, Turtulici N, Aronica R, Pigoni A, Squarcina L, Villa FM, Perlini C, Rossetti MG, Bellani M, Lasalvia A, Bonetto C, Scocco P, D'Agostino A, Torresani S, Imbesi M, Bellini F, Veronese A, Bocchio-Chiavetto L, Gennarelli M, Balestrieri M, Colombo GI, Finardi A, Ruggeri M, Furlan R, Brambilla P. A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup. Mol Psychiatry 2023; 28:1190-1200. [PMID: 36604602 DOI: 10.1038/s41380-022-01911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/29/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023]
Abstract
Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.
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Affiliation(s)
- Paolo Enrico
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Rosario Aronica
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.,Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Filippo M Villa
- Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy
| | - Cinzia Perlini
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy.,USD Clinical Psychology, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Maria G Rossetti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.,UOC of Psychiatry, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Antonio Lasalvia
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Chiara Bonetto
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Paolo Scocco
- Department of Mental Health, AULSS 6 Euganea, Padua, Italy
| | - Armando D'Agostino
- Department of Health Sciences, San Paolo University Hospital, University of Milan, Milano, Milan, Italy
| | - Stefano Torresani
- Department of Psychiatry, ULSS, Bolzano Suedtiroler Sanitaetbetrieb- Azienda Sanitaria dell'Alto Adige, Bolzano, Italy
| | | | | | | | - Luisella Bocchio-Chiavetto
- Faculty of Psychology, eCampus University, Novedrate, Como, Italy.,Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Massimo Gennarelli
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio, Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | | | - Gualtiero I Colombo
- Centro Cardiologico Monzino IRCCS, Immunology and Functional Genomics Unit, Milan, Italy
| | - Annamaria Finardi
- Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - Mirella Ruggeri
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.,UOC of Psychiatry, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Roberto Furlan
- Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy. .,Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
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5
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Gargano G, Caletti E, Perlini C, Turtulici N, Bellani M, Bonivento C, Garzitto M, Siri FM, Longo C, Bonetto C, Cristofalo D, Scocco P, Semrov E, Preti A, Lazzarotto L, Gardellin F, Lasalvia A, Ruggeri M, Marini A, Brambilla P. Language production impairments in patients with a first episode of psychosis. PLoS One 2022; 17:e0272873. [PMID: 35951619 PMCID: PMC9371299 DOI: 10.1371/journal.pone.0272873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 07/27/2022] [Indexed: 11/18/2022] Open
Abstract
Language production has often been described as impaired in psychiatric diseases such as in psychosis. Nevertheless, little is known about the characteristics of linguistic difficulties and their relation with other cognitive domains in patients with a first episode of psychosis (FEP), either affective or non-affective. To deepen our comprehension of linguistic profile in FEP, 133 patients with FEP (95 non-affective, FEP-NA; 38 affective, FEP-A) and 133 healthy controls (HC) were assessed with a narrative discourse task. Speech samples were systematically analyzed with a well-established multilevel procedure investigating both micro- (lexicon, morphology, syntax) and macro-linguistic (discourse coherence, pragmatics) levels of linguistic processing. Executive functioning and IQ were also evaluated. Both linguistic and neuropsychological measures were secondarily implemented with a machine learning approach in order to explore their predictive accuracy in classifying participants as FEP or HC. Compared to HC, FEP patients showed language production difficulty at both micro- and macro-linguistic levels. As for the former, FEP produced shorter and simpler sentences and fewer words per minute, along with a reduced number of lexical fillers, compared to HC. At the macro-linguistic level, FEP performance was impaired in local coherence, which was paired with a higher percentage of utterances with semantic errors. Linguistic measures were not correlated with any neuropsychological variables. No significant differences emerged between FEP-NA and FEP-A (p≥0.02, after Bonferroni correction). Machine learning analysis showed an accuracy of group prediction of 76.36% using language features only, with semantic variables being the most impactful. Such a percentage was enhanced when paired with clinical and neuropsychological variables. Results confirm the presence of language production deficits already at the first episode of the illness, being such impairment not related to other cognitive domains. The high accuracy obtained by the linguistic set of features in classifying groups support the use of machine learning methods in neuroscience investigations.
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Affiliation(s)
- Giulia Gargano
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Elisabetta Caletti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Cinzia Perlini
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Verona Hospital Trust–Azienda Ospedaliera Universitaria Integrata Verona–AOUI, Verona, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Marcella Bellani
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Verona Hospital Trust–Azienda Ospedaliera Universitaria Integrata Verona–AOUI, Verona, Italy
| | - Carolina Bonivento
- IRCCS “E.Medea” Polo Friuli Venezia Giulia, San Vito al Tagliamento, PN, Italy
| | - Marco Garzitto
- Department of Languages and Literatures, Communication, Education and Society, University of Udine, Udine, Italy
| | - Francesca Marzia Siri
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Chiara Longo
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Chiara Bonetto
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Doriana Cristofalo
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Paolo Scocco
- Department of Mental Health, Azienda ULSS 16, Padua, Italy
| | | | - Antonio Preti
- Department of Mental Health, Niguarda Ca’ Granda Hospital, Milan, Italy
| | - Lorenza Lazzarotto
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Antonio Lasalvia
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Verona Hospital Trust–Azienda Ospedaliera Universitaria Integrata Verona–AOUI, Verona, Italy
| | - Mirella Ruggeri
- Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Verona Hospital Trust–Azienda Ospedaliera Universitaria Integrata Verona–AOUI, Verona, Italy
| | - Andrea Marini
- Department of Languages and Literatures, Communication, Education and Society, University of Udine, Udine, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda-Ospedale Maggiore Policlinico, Milan, Italy
- * E-mail:
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Madonna D, Enrico P, Ciappolino V, Boscutti A, Colombo E, Turtulici N, Cantù F, Cereda G, Delvecchio G, De Falco S, Chierichetti M, Savioli M, Grasselli G, Brambilla P. Factors Associated With Severity of Delirium Complicating COVID-19 in Intensive Care Units. Front Neurol 2022; 13:774953. [PMID: 35401416 PMCID: PMC8987982 DOI: 10.3389/fneur.2022.774953] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 02/07/2022] [Indexed: 12/12/2022] Open
Abstract
The clinical outcome of the disease provoked by the SARS-CoV-2 infection, COVID-19, is largely due to the development of interstitial pneumonia accompanied by an Acute Respiratory Distress Syndrome (ARDS), often requiring ventilatory support therapy in Intensive Care Units (ICUs). Current epidemiologic evidence is demonstrating that the COVID-19 prognosis is significantly influenced by its acute complications. Among these, delirium figures as one of the most frequent and severe, especially in the emergency setting, where it shows a significantly negative prognostic impact. In this regard, the aim of our study is to identify clinical severity factors of delirium complicating COVID-19 related-ARDS. We performed a comparative and correlation analysis using demographics, comorbidities, multisystemic and delirium severity scores and anti-delirium therapy in two cohorts of ARDS patients with delirium, respectively, due to COVID-19 (n = 40) or other medical conditions (n = 39). Our results indicate that delirium in COVID-19-related ARDS is more severe since its onset despite a relatively less severe systemic condition at the point of ICU admission and required higher dosages of antipsychotic and non-benzodiazepinic sedative therapy respect to non-COVID patients. Finally, the correlation analysis showed a direct association between the male gender and maximum dosage of anti-delirium medications needed within the COVID-19 group, which was taken as a surrogate of delirium severity. Overall, our results seem to indicate that pathogenetic factors specifically associated to severe COVID-19 are responsible for the high severity of delirium, paving the way for future research focused on the mechanisms of the cognitive alterations associated with COVID-19.
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Affiliation(s)
- Domenico Madonna
- Department of Neurosciences and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Enrico
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Valentina Ciappolino
- Department of Neurosciences and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Andrea Boscutti
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Elisa Colombo
- Department of Neurosciences and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Filippo Cantù
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Guido Cereda
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Stefano De Falco
- Department of Anesthesia, Intensive Care and Emergency, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Monica Chierichetti
- Department of Anesthesia, Intensive Care and Emergency, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Monica Savioli
- Department of Anesthesia, Intensive Care and Emergency, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Giacomo Grasselli
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Anesthesia, Intensive Care and Emergency, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda-Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- *Correspondence: Paolo Brambilla
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7
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Enrico P, Delvecchio G, Turtulici N, Pigoni A, Villa FM, Perlini C, Rossetti MG, Bellani M, Lasalvia A, Bonetto C, Scocco P, D’Agostino A, Torresani S, Imbesi M, Bellini F, Veronese A, Bocchio-Chiavetto L, Gennarelli M, Balestrieri M, Colombo GI, Finardi A, Ruggeri M, Furlan R, Brambilla P. Classification of Psychoses Based on Immunological Features: A Machine Learning Study in a Large Cohort of First-Episode and Chronic Patients. Schizophr Bull 2021; 47:1141-1155. [PMID: 33561292 PMCID: PMC8266656 DOI: 10.1093/schbul/sbaa190] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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.
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Affiliation(s)
- Paolo Enrico
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandro Pigoni
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | | | - Cinzia Perlini
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy
| | - Maria Gloria Rossetti
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
- AOUI – Verona Hospital Trust, Verona, Italy
| | - Antonio Lasalvia
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
- AOUI – Verona Hospital Trust, Verona, Italy
| | - Chiara Bonetto
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Paolo Scocco
- Department of Mental Health, AULSS 6 Euganea, Padua, Italy
| | - Armando D’Agostino
- Department of Health Sciences, San Paolo University Hospital, University of Milan, Milan, Italy
| | - Stefano Torresani
- Department of Psychiatry, ULSS, Bolzano Suedtiroler Sanitaetbetrieb- Azienda Sanitaria dell’Alto Adige, Bolzano, Italy
| | | | | | | | - Luisella Bocchio-Chiavetto
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Faculty of Psychology, eCampus University, Novedrate, Como, Italy
| | - Massimo Gennarelli
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Matteo Balestrieri
- Unit of Psychiatry, Department of Medicine (DAME), University of Udine, Udine, Italy
| | - Gualtiero I Colombo
- Centro Cardiologico Monzino IRCCS, Immunology and Functional Genomics Unit, Milan, Italy
| | - Annamaria Finardi
- Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - Mirella Ruggeri
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
- AOUI – Verona Hospital Trust, Verona, Italy
| | - Roberto Furlan
- Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy
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