1
|
Thesen T, Marrero WJ, Konopasky AJ, Duncan MS, Blackmon KE. Towards precision well-being in medical education. MEDICAL TEACHER 2024:1-5. [PMID: 38808734 DOI: 10.1080/0142159x.2024.2357279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/15/2024] [Indexed: 05/30/2024]
Abstract
Medical trainee well-being is often met with generalized solutions that overlook substantial individual variations in mental health predisposition and stress reactivity. Precision medicine leverages individual environmental, genetic, and lifestyle factors to tailor preventive and therapeutic interventions. In addition, an exclusive focus on clinical mental illness tends to disregard the importance of supporting the positive aspects of medical trainee well-being. We introduce a novel precision well-being framework for medical education that is built on a comprehensive and individualized view of mental health, combining measures from mental health and positive psychology in a unified, data-driven framework. Unsupervised machine learning techniques commonly used in precision medicine were applied to uncover patterns within multidimensional mental health data of medical students. Using data from 3,632 US medical students, clusters were formulated based on recognized metrics for depression, anxiety, and flourishing. The analysis identified three distinct clusters. Membership in the 'Healthy Flourishers' well-being phenotype was associated with no signs of anxiety or depression while simultaneously reporting high levels of flourishing. Students in the 'Getting By' cluster reported mild anxiety and depression and diminished flourishing. Membership in the 'At-Risk' cluster was associated with high anxiety and depression, languishing, and increased suicidality. Nearly half (49%) of the medical students surveyed were classified as 'Healthy Flourishers', whereas 36% were grouped into the 'Getting-By' cluster and 15% were identified as 'At-Risk'. Findings show that a substantial portion of medical students report diminished well-being during their studies, with a significant number struggling with mental health challenges. This novel precision well-being framework represents an integrated empirical model that classifies individual medical students into distinct and meaningful well-being phenotypes based on their holistic mental health. This approach has direct applicability to student support and can be used to evaluate the effectiveness of personalized intervention strategies stratified by cluster membership.
Collapse
Affiliation(s)
- Thomas Thesen
- Department of Medical Education, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
- Department of Computer Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Wesley J Marrero
- Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
| | - Abigail J Konopasky
- Department of Medical Education, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Matthew S Duncan
- Department of Medical Education, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
- Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA
| | - Karen E Blackmon
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, Florida, USA
| |
Collapse
|
2
|
Zaretsky TG, Jagodnik KM, Barsic R, Antonio JH, Bonanno PA, MacLeod C, Pierce C, Carney H, Morrison MT, Saylor C, Danias G, Lepow L, Yehuda R. The Psychedelic Future of Post-Traumatic Stress Disorder Treatment. Curr Neuropharmacol 2024; 22:636-735. [PMID: 38284341 PMCID: PMC10845102 DOI: 10.2174/1570159x22666231027111147] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 01/30/2024] Open
Abstract
Post-traumatic stress disorder (PTSD) is a mental health condition that can occur following exposure to a traumatic experience. An estimated 12 million U.S. adults are presently affected by this disorder. Current treatments include psychological therapies (e.g., exposure-based interventions) and pharmacological treatments (e.g., selective serotonin reuptake inhibitors (SSRIs)). However, a significant proportion of patients receiving standard-of-care therapies for PTSD remain symptomatic, and new approaches for this and other trauma-related mental health conditions are greatly needed. Psychedelic compounds that alter cognition, perception, and mood are currently being examined for their efficacy in treating PTSD despite their current status as Drug Enforcement Administration (DEA)- scheduled substances. Initial clinical trials have demonstrated the potential value of psychedelicassisted therapy to treat PTSD and other psychiatric disorders. In this comprehensive review, we summarize the state of the science of PTSD clinical care, including current treatments and their shortcomings. We review clinical studies of psychedelic interventions to treat PTSD, trauma-related disorders, and common comorbidities. The classic psychedelics psilocybin, lysergic acid diethylamide (LSD), and N,N-dimethyltryptamine (DMT) and DMT-containing ayahuasca, as well as the entactogen 3,4-methylenedioxymethamphetamine (MDMA) and the dissociative anesthetic ketamine, are reviewed. For each drug, we present the history of use, psychological and somatic effects, pharmacology, and safety profile. The rationale and proposed mechanisms for use in treating PTSD and traumarelated disorders are discussed. This review concludes with an in-depth consideration of future directions for the psychiatric applications of psychedelics to maximize therapeutic benefit and minimize risk in individuals and communities impacted by trauma-related conditions.
Collapse
Affiliation(s)
- Tamar Glatman Zaretsky
- James J. Peters Veterans Affairs Medical Center, New York, NY, USA
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kathleen M. Jagodnik
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Barsic
- James J. Peters Veterans Affairs Medical Center, New York, NY, USA
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Josimar Hernandez Antonio
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Philip A. Bonanno
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carolyn MacLeod
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charlotte Pierce
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hunter Carney
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Morgan T. Morrison
- James J. Peters Veterans Affairs Medical Center, New York, NY, USA
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charles Saylor
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - George Danias
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren Lepow
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rachel Yehuda
- James J. Peters Veterans Affairs Medical Center, New York, NY, USA
- The Center for Psychedelic Psychotherapy and Trauma Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
3
|
Gauld C, Masri Y, Fourneret P. Clinical intuition in psychology through the prism of personalized psychiatry. Front Psychol 2023; 14:1111250. [PMID: 37077841 PMCID: PMC10108676 DOI: 10.3389/fpsyg.2023.1111250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Affiliation(s)
- Christophe Gauld
- Service de Psychiatrie de l'Enfant et de l'Adolescent, Université de Lyon 1, Lyon, France
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS & Université Claude Bernard Lyon 1, Lyon, France
- *Correspondence: Christophe Gauld
| | - Yassmine Masri
- Service de Psychiatrie de l'Enfant et de l'Adolescent, Université de Lyon 1, Lyon, France
| | - Pierre Fourneret
- Service de Psychiatrie de l'Enfant et de l'Adolescent, Université de Lyon 1, Lyon, France
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS & Université Claude Bernard Lyon 1, Lyon, France
| |
Collapse
|
4
|
Johnson D, Letchumanan V, Thum CC, Thurairajasingam S, Lee LH. A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression. Nutrients 2023; 15:nu15061382. [PMID: 36986112 PMCID: PMC10053794 DOI: 10.3390/nu15061382] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/16/2023] Open
Abstract
Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents a key domain of healthcare, which currently has limited, adverse-effect prone treatment options, and probiotics may hold the potential to be a novel, customizable treatment for depression. Clinical depression is a common, potentially debilitating condition that may be amenable to a precision psychiatry-based approach utilizing probiotics. Although our understanding has not yet reached a sufficient level, this could be a therapeutic approach that can be tailored for specific individuals with their own unique set of characteristics and health issues. Scientifically, the use of probiotics as a treatment for depression has a valid basis rooted in the microbiota-gut-brain axis (MGBA) mechanisms, which play a role in the pathophysiology of depression. In theory, probiotics appear to be ideal as adjunct therapeutics for major depressive disorder (MDD) and as stand-alone therapeutics for mild MDD and may potentially revolutionize the treatment of depressive disorders. Although there is a wide range of probiotics and an almost limitless range of therapeutic combinations, this review aims to narrow the focus to the most widely commercialized and studied strains, namely Lactobacillus and Bifidobacterium, and to bring together the arguments for their usage in patients with major depressive disorder (MDD). Clinicians, scientists, and industrialists are critical stakeholders in exploring this groundbreaking concept.
Collapse
Affiliation(s)
- Dinyadarshini Johnson
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
| | - Vengadesh Letchumanan
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Pathogen Resistome Virulome and Diagnostic Research Group (PathRiD), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
| | - Chern Choong Thum
- Department of Psychiatry, Hospital Sultan Abdul Aziz Shah, Persiaran Mardi-UPM, Serdang 43400, Malaysia
| | - Sivakumar Thurairajasingam
- Clinical School Johor Bahru, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Johor Bahru 80100, Malaysia
- Correspondence: (S.T.); or (L.-H.L.)
| | - Learn-Han Lee
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Pathogen Resistome Virulome and Diagnostic Research Group (PathRiD), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Correspondence: (S.T.); or (L.-H.L.)
| |
Collapse
|
5
|
Watts D, Mamak M, Moulden H, Upfold C, de Azevedo Cardoso T, Kapczinski F, Chaimowitz G. The HARM models: Predicting longitudinal physical aggression in patients with schizophrenia at an individual level. J Psychiatr Res 2023; 161:91-98. [PMID: 36917868 DOI: 10.1016/j.jpsychires.2023.02.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/14/2023] [Accepted: 02/23/2023] [Indexed: 03/16/2023]
Abstract
The prediction and prevention of aggression in individuals with schizophrenia remains a top priority within forensic psychiatric settings. While risk assessment methods are well rooted in forensic psychiatry, there are no available tools to predict longitudinal physical aggression in patients with schizophrenia within forensic settings at an individual level. In the present study, we used evidence-based risk and protective factors, as well as variables related to course of treatment assessed at baseline, to predict prospective incidents of physical aggression (4-month, 12-month, and 18-month follow-up) among 151 patients with schizophrenia within the forensic mental healthcare system. Across our HARM models, the balanced accuracy (sensitivity + specificity/2) of predicting physical aggressive incidents in patients with schizophrenia ranged from 59.73 to 87.33% at 4-month follow-up, 68.31-80.10% at 12-month follow-up, and 46.22-81.63% at 18-month follow-up, respectively. Additionally, we developed separate models, using clinician rated clinical judgement of short term and immediate violent risk, as a measure of comparison. Several modifiable evidence-based predictors of prospective physical aggression in schizophrenia were identified, including impulse control, substance abuse, impulsivity, treatment non-adherence, mood and psychotic symptoms, substance abuse, and poor family support. To the best of our knowledge, our HARM models are the first to predict longitudinal physical aggression at an individual level in patients with schizophrenia in forensic settings. However, it is important to caution that since these machine learning models were developed in the context of forensic settings, they may not be generalisable to individuals with schizophrenia more broadly. Moreover, a low base rate of physical aggression was observed in the testing set (6.0-11.6% across timepoints). As such, larger cohorts will be required to determine the replicability of these findings.
Collapse
Affiliation(s)
- Devon Watts
- Neuroscience Graduate Program, McMaster University, Hamilton, Canada.
| | - Mini Mamak
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
| | - Heather Moulden
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
| | - Casey Upfold
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
| | | | - Flavio Kapczinski
- Neuroscience Graduate Program, McMaster University, Hamilton, Canada; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, Brazil.
| | - Gary Chaimowitz
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
| |
Collapse
|
6
|
de Angel V, Adeleye F, Zhang Y, Cummins N, Munir S, Lewis S, Laporta Puyal E, Matcham F, Sun S, Folarin AA, Ranjan Y, Conde P, Rashid Z, Dobson R, Hotopf M. The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement. JMIR Ment Health 2023; 10:e42866. [PMID: 36692937 PMCID: PMC9906314 DOI: 10.2196/42866] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/10/2022] [Accepted: 11/26/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. OBJECTIVE A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. METHODS A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. RESULTS The overall retention rate was 60%. Higher-intensity treatment (χ21=4.6; P=.03) and higher baseline anxiety (t56.28=-2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4=-0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. CONCLUSIONS Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term.
Collapse
Affiliation(s)
- Valeria de Angel
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Fadekemi Adeleye
- Department of Psychology, King's College London, London, United Kingdom
| | - Yuezhou Zhang
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Nicholas Cummins
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sara Munir
- Lewisham Talking Therapies, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Serena Lewis
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - Estela Laporta Puyal
- Biomedical Signal Interpretation and Computational Simulation Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- Centro de Investigación Biomédica en Red of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Faith Matcham
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- School of Psychology, University of Sussex, Brighton, United Kingdom
| | - Shaoxiong Sun
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Amos A Folarin
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Institute of Health Informatics, University College London, London, United Kingdom
- Health Data Research UK London, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at University College London Hospitals, University College London Hospitals NHS Foundation Trust, London, United Kingdom
| | - Yatharth Ranjan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Pauline Conde
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Zulqarnain Rashid
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Richard Dobson
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Matthew Hotopf
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| |
Collapse
|
7
|
Classification of Depressive and Schizophrenic Episodes Using Night-Time Motor Activity Signal. Healthcare (Basel) 2022; 10:healthcare10071256. [PMID: 35885784 PMCID: PMC9318635 DOI: 10.3390/healthcare10071256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/01/2022] [Accepted: 07/01/2022] [Indexed: 11/21/2022] Open
Abstract
Major depressive disorder (MDD) is the most recurrent mental illness globally, affecting approximately 5% of adults. Furthermore, according to the National Institute of Mental Health (NIMH) of the U.S., calculating an actual schizophrenia prevalence rate is challenging because of this illness’s underdiagnosis. Still, most current global metrics hover between 0.33% and 0.75%. Machine-learning scientists use data from diverse sources to analyze, classify, or predict to improve the psychiatric attention, diagnosis, and treatment of MDD, schizophrenia, and other psychiatric conditions. Motor activity data are gaining popularity in mental illness diagnosis assistance because they are a cost-effective and noninvasive method. In the knowledge discovery in databases (KDD) framework, a model to classify depressive and schizophrenic patients from healthy controls is constructed using accelerometer data. Taking advantage of the multiple sleep disorders caused by mental disorders, the main objective is to increase the model’s accuracy by employing only data from night-time activity. To compare the classification between the stages of the day and improve the accuracy of the classification, the total activity signal was cut into hourly time lapses and then grouped into subdatasets depending on the phases of the day: morning (06:00–11:59), afternoon (12:00–17:59), evening (18:00–23:59), and night (00:00–05:59). Random forest classifier (RFC) is the algorithm proposed for multiclass classification, and it uses accuracy, recall, precision, the Matthews correlation coefficient, and F1 score to measure its efficiency. The best model was night-featured data and RFC, with 98% accuracy for the classification of three classes. The effectiveness of this experiment leads to less monitoring time for patients, reducing stress and anxiety, producing more efficient models, using wearables, and increasing the amount of data.
Collapse
|
8
|
Bartoli F, Carrà G. Focus on Peripheral Biomarkers of Mental Disorders. Brain Sci 2022; 12:brainsci12060756. [PMID: 35741640 PMCID: PMC9221179 DOI: 10.3390/brainsci12060756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/01/2022] [Accepted: 06/02/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Francesco Bartoli
- Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy;
- Correspondence:
| | - Giuseppe Carrà
- Department of Medicine and Surgery, University of Milano-Bicocca, Via Cadore 48, 20900 Monza, Italy;
- Division of Psychiatry, University College London, 6th Floor, Maple House, 149 Tottenham Court Road, London W1T 7NF, UK
| |
Collapse
|
9
|
Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self. J Pers Med 2022; 12:jpm12030403. [PMID: 35330403 PMCID: PMC8955533 DOI: 10.3390/jpm12030403] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 02/07/2023] Open
Abstract
Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person’s own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.
Collapse
|
10
|
Marzola E, Martini M, Brustolin A, Abbate-Daga G. Inpatients with severe-enduring anorexia nervosa: Understanding the "enduringness" specifier. Eur Psychiatry 2021; 64:e44. [PMID: 34254574 PMCID: PMC8278247 DOI: 10.1192/j.eurpsy.2021.2218] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Background Despite the need for a common definition of severe and enduring anorexia nervosa (SE-AN) with the overarching goal to optimize treatments, this definition still is being debated. Therefore, in this study we conducted an in-depth investigation of the history of AN and its clinical outcomes on inpatients with AN to ascertain the eventual “profiles” for individuals with varying durations of the illness (DOI). Methods We recruited 169 inpatients with AN, grouping them according to DOI: <3 years (short duration, SD-AN); 3–6.99 years (medium duration, MD-AN); and ≥7 years (long duration, LD-AN). We then performed a self-report and interview-based investigation of AN history, clinical data, eating, and general psychopathology, including personality, premorbid traits, stage of change, and quality of life. We measured the clinical outcomes for hospitalization as well. Results The majority of the measures did not differ across groups. Those with LD-AN were older and diagnosed mostly with the binge-purging AN subtype, failed more previous AN-related treatments, reported a lower lifetime body mass index, and trended toward a younger age at onset when compared to the other groups. All patients responded equally well to hospitalization, but patients with SD-AN improved less in drive for thinness and body-related concerns. Conclusions We did not find the “enduringness” of AN to be a specifier of severity. Hospitalization was effective for those with LD-AN and MD-AN, while interventions for the core cognitive aspects of over-evaluation of body shape should be offered to patients with SD-AN.
Collapse
Affiliation(s)
- Enrica Marzola
- Eating Disorders Center, Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
| | - Matteo Martini
- Eating Disorders Center, Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
| | - Annalisa Brustolin
- Eating Disorders Center, Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
| | - Giovanni Abbate-Daga
- Eating Disorders Center, Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
| |
Collapse
|