2
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Lieslehto J, Tiihonen J, Lähteenvuo M, Leucht S, Correll CU, Mittendorfer-Rutz E, Tanskanen A, Taipale H. Development and Validation of a Machine Learning-Based Model of Mortality Risk in First-Episode Psychosis. JAMA Netw Open 2024; 7:e240640. [PMID: 38497965 PMCID: PMC10949098 DOI: 10.1001/jamanetworkopen.2024.0640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/07/2024] [Indexed: 03/19/2024] Open
Abstract
Importance There is an absence of mortality risk assessment tools in first-episode psychosis (FEP) that could enable personalized interventions. Objective To examine the feasibility of machine learning (ML) in discerning mortality risk in FEP and to assess whether such risk predictions can inform pharmacotherapy choices. Design, Setting, and Participants In this prognostic study, Swedish nationwide cohort data (from July 1, 2006, to December 31, 2021) were harnessed for model development and validation. Finnish cohort data (from January 1, 1998, to December 31, 2017) were used for external validation. Data analyses were completed between December 2022 and December 2023. Main Outcomes and Measures Fifty-one nationwide register variables, encompassing demographics and clinical and work-related histories, were subjected to ML to predict future mortality risk. The ML model's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The comparative effectiveness of pharmacotherapies in patients was assessed and was stratified by the ML model to those with predicted high mortality risk (vs low risk), using the between-individual hazard ratio (HR). The 5 most important variables were then identified and a model was retrained using these variables in the discovery sample. Results This study included 24 052 Swedish participants (20 000 in the discovery sample and 4052 in the validation sample) and 1490 Finnish participants (in the validation sample). Swedish participants had a mean (SD) age of 29.1 (8.1) years, 62.1% were men, and 418 died with 2 years. Finnish participants had a mean (SD) age of 29.7 (8.0) years, 61.7% were men, and 31 died within 2 years. The discovery sample achieved an AUROC of 0.71 (95% CI, 0.68-0.74) for 2-year mortality prediction. Using the 5 most important variables (ie, the top 10% [substance use comorbidities, first hospitalization duration due to FEP, male sex, prior somatic hospitalizations, and age]), the final model resulted in an AUROC of 0.70 (95% CI, 0.63-0.76) in the Swedish sample and 0.67 (95% CI, 0.56-0.78) in the Finnish sample. Individuals with predicted high mortality risk had an elevated 15-year risk in the Swedish sample (HR, 3.77 [95% CI, 2.92-4.88]) and an elevated 20-year risk in the Finnish sample (HR, 3.72 [95% CI, 2.67-5.18]). For those with predicted high mortality risk, long-acting injectable antipsychotics (HR, 0.45 [95% CI, 0.23-0.88]) and mood stabilizers (HR, 0.64 [95% CI, 0.46-0.90]) were associated with decreased mortality risk. Conversely, for those predicted to survive, only oral aripiprazole (HR, 0.38 [95% CI, 0.20-0.69]) and risperidone (HR, 0.38 [95% CI, 0.18-0.82]) were associated with decreased mortality risk. Conclusions and Relevance In this prognostic study, an ML-based model was developed and validated to predict mortality risk in FEP. These findings may help to develop personalized interventions to mitigate mortality risk in FEP.
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Affiliation(s)
- Johannes Lieslehto
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jari Tiihonen
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio
- Center for Psychiatry Research, Stockholm City Council, Stockholm, Sweden
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Markku Lähteenvuo
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio
| | - Stefan Leucht
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christoph U. Correll
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, New York
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
- Department of Child and Adolescent Psychiatry, Charité–Universitätsmedizin Berlin, Berlin, Germany
| | - Ellenor Mittendorfer-Rutz
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Antti Tanskanen
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Psychiatry Research, Stockholm City Council, Stockholm, Sweden
| | - Heidi Taipale
- Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio
- Division of Insurance Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Psychiatry Research, Stockholm City Council, Stockholm, Sweden
- School of Pharmacy, University of Eastern Finland, Kuopio
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Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87:103705. [PMID: 37506575 DOI: 10.1016/j.ajp.2023.103705] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
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Affiliation(s)
- Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Beijing 100191, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Qun-Xi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - San-Wang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yong-Bo Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Xiao-Xing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Tang-Sheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Kai Yuan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Ying Han
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China.
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5
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Allesøe RL, Lundgaard AT, Hernández Medina R, Aguayo-Orozco A, Johansen J, Nissen JN, Brorsson C, Mazzoni G, Niu L, Biel JH, Brasas V, Webel H, Benros ME, Pedersen AG, Chmura PJ, Jacobsen UP, Mari A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Hong MG, Musholt PB, De Masi F, Vogt J, Pedersen HK, Gudmundsdottir V, Jones A, Kennedy G, Bell J, Thomas EL, Frost G, Thomsen H, Hansen E, Hansen TH, Vestergaard H, Muilwijk M, Blom MT, 't Hart LM, Pattou F, Raverdy V, Brage S, Kokkola T, Heggie A, McEvoy D, Mourby M, Kaye J, Hattersley A, McDonald T, Ridderstråle M, Walker M, Forgie I, Giordano GN, Pavo I, Ruetten H, Pedersen O, Hansen T, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, McCarthy MI, Pearson E, Banasik K, Rasmussen S, Brunak S, Thomas CE, Haussler R, Beulens J, Rutters F, Nijpels G, van Oort S, Groeneveld L, Elders P, Giorgino T, Rodriquez M, Nice R, Perry M, Bianzano S, Graefe-Mody U, Hennige A, Grempler R, Baum P, Stærfeldt HH, Shah N, Teare H, Ehrhardt B, Tillner J, Dings C, Lehr T, Scherer N, Sihinevich I, Cabrelli L, Loftus H, Bizzotto R, Tura A, Dekkers K, van Leeuwen N, Groop L, Slieker R, Ramisch A, Jennison C, McVittie I, Frau F, Steckel-Hamann B, Adragni K, Thomas M, Pasdar NA, Fitipaldi H, Kurbasic A, Mutie P, Pomares-Millan H, Bonnefond A, Canouil M, Caiazzo R, Verkindt H, Holl R, Kuulasmaa T, Deshmukh H, Cederberg H, Laakso M, Vangipurapu J, Dale M, Thorand B, Nicolay C, Fritsche A, Hill A, Hudson M, Thorne C, Allin K, Arumugam M, Jonsson A, Engelbrechtsen L, Forman A, Dutta A, Sondertoft N, Fan Y, Gough S, Robertson N, McRobert N, Wesolowska-Andersen A, Brown A, Davtian D, Dawed A, Donnelly L, Palmer C, White M, Ferrer J, Whitcher B, Artati A, Prehn C, Adam J, Grallert H, Gupta R, Sackett PW, Nilsson B, Tsirigos K, Eriksen R, Jablonka B, Uhlen M, Gassenhuber J, Baltauss T, de Preville N, Klintenberg M, Abdalla M. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models. Nat Biotechnol 2023; 41:399-408. [PMID: 36593394 PMCID: PMC10017515 DOI: 10.1038/s41587-022-01520-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/20/2022] [Indexed: 01/03/2023]
Abstract
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Agnete Troen Lundgaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ricardo Hernández Medina
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alejandro Aguayo-Orozco
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Joachim Johansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jakob Nybo Nissen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Gianluca Mazzoni
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jorge Hernansanz Biel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Valentas Brasas
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark.,Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anders Gorm Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Piotr Jaroslaw Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ulrik Plesner Jacobsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Andrea Mari
- C.N.R. Institute of Neuroscience, Padova, Italy
| | - Robert Koivula
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ana Vinuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.,Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | | | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.,Chair of Food Chemistry and Molecular and Sensory Science, Technical University of Munich, Freising, Germany
| | - Mark Haid
- Metabolomics and Proteomics Core, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mun-Gwan Hong
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Petra B Musholt
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Federico De Masi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Josef Vogt
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helle Krogh Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Valborg Gudmundsdottir
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Angus Jones
- University of Exeter Medical School, Exeter, UK
| | - Gwen Kennedy
- The Immunoassay Biomarker Core Laboratory, School of Medicine, University of Dundee, Dundee, UK
| | - Jimmy Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - Gary Frost
- Section for Nutrition Research, Faculty of Medicine, Imperial College London, London, UK
| | - Henrik Thomsen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Elizaveta Hansen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Tue Haldor Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mirthe Muilwijk
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Department of Biomedical Data Science, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Francois Pattou
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Violeta Raverdy
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Alison Heggie
- Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle, UK
| | - Miranda Mourby
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | - Jane Kaye
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | | | | | - Martin Ridderstråle
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Ian Forgie
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, CRC, Lund University, SUS, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations, Vienna, Austria
| | - Hartmut Ruetten
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emmanouil Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Paul W Franks
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Harvard T.H. Chan School of Public Health, Boston, MA, USA.,OCDEM, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.,Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.,Genentech, South San Francisco, CA, USA
| | - Ewan Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
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Allesøe RL, Thompson WK, Bybjerg-Grauholm J, Hougaard DM, Nordentoft M, Werge T, Rasmussen S, Benros ME. Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data. JAMA Psychiatry 2023; 80:146-155. [PMID: 36477816 PMCID: PMC9857190 DOI: 10.1001/jamapsychiatry.2022.4076] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Importance Diagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy. Objective To develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics. Design, Setting, and Participants This study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022. Exposure At least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ. Main Outcomes and Measures The predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance. Results A total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72. Conclusions and Relevance Results of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Wesley K. Thompson
- Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla
| | - Jonas Bybjerg-Grauholm
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - David M. Hougaard
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Institute of Biological Psychiatry, Mental Health Centre Sct Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark,Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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