The revolution of personalized psychiatry: will technology make it happen sooner?

Perna, Giampaolo; Grassi, Massimiliano; Caldirola, Daniela; Nemeroff, Charles B.

doi:10.1017/s0033291717002859

Cited by 83 publications

(76 citation statements)

References 66 publications

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“…137,138 Consistent with this view, recent approaches have pointed to the development of composite prediction models that integrate multiple potential predictors through new technological acquisitions and computational resources, such as ma-chine learning and related techniques. 139,140 Some promising attempts were performed, showing that this paradigm may offer relevant advances toward personalized treatments. A deep learning prediction approach, resulting from the integrated analysis of genetic (i.e., single nucleotide polymorphisms), sociodemographic, and clinical factors in a large sample of patients with MDD, seems to be suitable to distinguish responders from non-responders to SSRI treatment.…”

Section: Discussionmentioning

confidence: 99%

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Perna

Alciati²,

Daccò

et al. 2020

Psychiatry Investig

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Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.

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Section: Discussionmentioning

confidence: 99%

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Perna

Alciati²,

Daccò

et al. 2020

Psychiatry Investig

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“…Their future goals include developing deep-learning methods aimed at uncovering latent patterns found in brain images and networks. Perna et al (2018), in an editorial, have also emphasized machine learning in the development of new predictive algorithms derived from data collected from electronic medical records and real-time data collected from smart phones and other technologies, noting that such approaches have resulted in the prediction of remission, relapse, and suicidal ideation. However, clinical applications have been rare.…”

Section: Megastudies: Clarity or Confusion?mentioning

confidence: 99%

Neural circuitry and precision medicines for mental disorders: are they compatible?

Dean

2018

Psychol. Med.

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Given the failure of psychiatry to develop clinically useful biomarkers for psychiatric disorders, and the concomitant failure to develop significant advances in diagnosis and treatment, the National Institute of Mental Health (NIMH) in 2010 launched the Research Domain Criteria (RDoC), a framework for research based on the assumption that mental disorders are disorders of identifiable brain neural circuits, with neural circuitry at the center of units of analysis ranging from genes, molecules, and cells to behavior, self-reports, and paradigms. These were to be integrated with five validated dimensional psychological constructs such as negative and positive valence systems. Four years later, the NIMH stated that the ultimate goal of RDoC is precision medicine for psychiatry, with the assumption that precision medications will normalize dysfunctional neural circuits. How this could be accomplished is not obvious, given that neural circuits are widely distributed, have unclear boundaries, and exhibit a significant degree of neuroplasticity, with multiple circuits present in any given disorder. Moreover, the early focus on neural circuitry has been criticized for its reductionism and neglect of the more recent RDoC emphasis on the integration and equivalence of biological and psychological phenomena. Yet this seems inconsistent with the priorities of the NIMH director, an advocate of the central role of neural circuitry and projects such as the Brain Initiative and the Human Connectome Project. Will such projects, at a cost of at least $10 billion, lead to precision medications for mental disorders, or further diminish funding for clinical care and research?

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“…Health is defined in relation to personal goals. It requires a personalized psychiatry which takes into account patients' characteristics, needs and desires in their care process (Perna et al, 2018). These developments create a paradigm shift from illness to wellbeing.…”

Section: Introductionmentioning

confidence: 99%

Implementing Experience Sampling Technology for Functional Analysis in Family Medicine – A Design Thinking Approach

et al. 2019

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Background: A paradigm shift in health care from illness to wellbeing requires new assessment technologies and intervention strategies. Self-monitoring tools based on the Experience Sampling Method (ESM) might provide a solution. They enable patients to monitor both vulnerability and resilience in daily life. Although ESM solutions are extensively used in research, a translation from science into daily clinical practice is needed. Objective: To investigate the redesign process of an existing platform for ESM data collection for detailed functional analysis and disease management used by psychological assistants to the general practitioner (PAGPs) in family medicine. Methods: The experience-sampling platform was reconceptualized according to the design thinking framework in three phases. PAGPs were closely involved in co-creation sessions. In the 'understand' phase, knowledge about end-users' characteristics and current eHealth use was collected (nominal group technique-2 sessions with N = 15). In the 'explore' phase, the key needs concerning the platform content and functionalities were evaluated and prioritized (empathy mapping-1 session with N = 5, moderated user testing-1 session with N = 4). In the 'materialize' phase, the adjusted version of the platform was tested in daily clinical practice (4 months with N = 4). The whole process was extensively logged, analyzed using content analysis, and discussed with an interprofessional project group. Results: In the 'understand' phase, PAGPs emphasized the variability in symptoms reported by patients. Therefore, moment-to-moment assessment of mood and behavior in a daily life context could be valuable. In the 'explore' phase, (motivational) functionalities, technological performance and instructions turned out to be important user requirements and could be improved. In the 'materialize' phase, PAGPs encountered barriers to implement the experience-sampling platform. They were insufficiently facilitated by the regional primary care group and general practitioners.

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The revolution of personalized psychiatry: will technology make it happen sooner?

Cited by 83 publications

References 66 publications

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Neural circuitry and precision medicines for mental disorders: are they compatible?

Implementing Experience Sampling Technology for Functional Analysis in Family Medicine – A Design Thinking Approach

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