A Framework for Advancing Precision Medicine in Clinical Trials for Mental Disorders

Lenze, Eric J.; Rodebaugh, Thomas L.; Nicol, Ginger E.

doi:10.1001/jamapsychiatry.2020.0114

Cited by 43 publications

(24 citation statements)

References 10 publications

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“…Similarly, such models may be useful to determine who would benefit from treatments that affect serotonergic tone. Ultimately, by measuring a more specific, biologically based phenotype, there is greatly improved opportunity to improve sensitivity of measurement and specificity of treatment [25].…”

Section: Discussionmentioning

confidence: 99%

“…Such measures should strive to be easy to collect, have increased sensitivity to facilitate frequent and accurate assessment, and should be validated in relationship to narrower biological phenotypes and treatment targets than traditional endpoints offer. This will lead to improved dynamic treatment research and clinical decision making [24] based on modulation of underlying neurobiological deficits [25].…”

Section: Introductionmentioning

confidence: 99%

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Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric In-Patients

Galatzer‐Levy

Abbas

Ries

et al. 2020

Preprint

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Background: Multiple symptoms of suicide risk are assessed based on visual and auditory information including flattened affect, reduced movement, and slowed speech. Objective quantification of such symptomatology from novel data sources can increase the sensitivity, scalability, and timeliness of suicide risk assessment. Methods: In the current study we utilized video to quantify facial, vocal, and movement markers associated with mood, emotion, and motor functioning from a structured clinical conversation in 20 patients admitted to a psychiatric hospital following a suicide risk attempt. Measures were calculated using open source deep learning algorithms for processing facial expressivity, head movement, and vocal characteristics. Derived digital measures of flattened affect, reduced movement, and slowed speech were compared to suicide severity using the Beck Suicide Scale (BSS), controlling for age and gender using multiple linear regression. Results: Suicide severity was associated with multiple visual and auditory markers including speech prevalence (β = -0.68; p = .017, r2 = .40), overall expressivity (β = -0.46; p = 0.10, r2 = .27), and head movement measured as head pitch variability (β = -1.24; p = .006, r2 = .48) and head yaw variability (β = -0.54; p = .055, r2 = .32). Conclusions: Digital measurements of facial affect, movement, and speech prevalence demonstrated strong effect sizes and significant linear associations with severity of suicidal ideation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric In-Patients

Galatzer‐Levy

Abbas

Ries

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors did find that the simulated PTR produced better effects. Lenze et al (2020) address the problems of RCTs from a somewhat different perspective than I have presented here and suggest a potential solution that they call precision clinical trials (PCTs). The authors propose that the problem with most existing RCTs is that they measure only the fixed baseline characteristics that are not usually sensitive to detecting treatment responders.…”

Section: Solutions To the Problem Of The Primacy Of Rctsmentioning

confidence: 98%

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Bickman

2020

Adm Policy Ment Health

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This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on the potential contributions of artificial intelligence and precision mental health to improving mental health services. Toward that end, I draw upon my own research, which has changed over the last half century, to highlight the need to transform the way we conduct mental health services research. I identify exemplars from the emerging literature on artificial intelligence and precision approaches to treatment in which there is an attempt to personalize or fit the treatment to the client in order to produce more effective interventions. Keywords Mental health services • Artificial intelligence • Machine learning • Precision mental health • Randomized clinical trials (RCTs) • Precision medicine "AI will bring many wonders. It may also destabilize everything from nuclear détente to human friendships. We need to think much harder about how to adapt... it is changing human knowledge, perception and reality-and, in so doing, changing the course of human history. We seek to understand it and its consequences and encourage others across the disciplines to do the same."-Kissinger et al. 2019, pp. 24-26 "AI is one of the most profound things we're working on as humanity. It's more profound than fire or electricity."-Alphabet CEO Sundar Pichai, in Thompson and Bodoni 2020. This article is part of a special issue, Festschrift for Leonard Bickman: The Future of Children's Mental Health Services.

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“…On the basis of this finding, Everaert and Joormann (2019) provided an example of a testable prediction about which symptoms to target with which strategy based on their network analysis: "If patients display symptoms related to negative expectations about the future (e.g., pessimism, fear of the worst happening), then treatments may adopt a symptom-focused strategy and increase positive reappraisal use (e.g., through cognitive restructuring) to obtain relief in these specific symptoms" (p. 10). As such, data-derived hypotheses generated via network analysis of symptommechanism interactions can function as an intermediate low-burden step that can guide the development of precision clinical trials, which require focalized targets, adaptive approaches, and precise measurement of mechanisms and outcomes (Lenze et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Psychological Networks can Identify Potential Pathways to Specific Intervention Targets for Anxiety in Response to Coronavirus Disease 2019 (COVID-19)

Papini¹,

Dainer-Best²,

Rubin³

et al. 2020

Preprint

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Addressing mental health challenges related to the COVID-19 outbreak can be facilitated through research that characterizes the needs of subpopulations and identifies specific pathways to targeted intervention. Toward this aim, we examined the impact of the COVID-19 outbreak on anxiety symptoms among college students (N = 487) and explored the relative impact of coping strategies using a psychological network approach, which models complex interactions to identify potential pathways to symptom-level intervention. Although students showed several significant fluctuations in pre- to post-outbreak anxiety symptom levels measured with the GAD-7, anxiety network connectivity was not significantly different across timepoints. Consistent with hypotheses, the post-outbreak symptom+coping network revealed that increased use of the adaptive coping strategies of acceptance, behavioral activation, and values-based action was associated with lower levels of fear, restlessness, and trouble relaxing. The symptom+coping network also revealed that increased use of the maladaptive strategies of excessive cleaning, reassurance seeking, and excessive checking was associated with higher levels of irritability and fear. Surprisingly, the use of reappraisal and avoidance, two strategies with putatively opposing adaptive value, highly overlapped and showed positive associations with fear and irritability. These symptom+coping associations can guide the assessment and treatment of anxiety in the face of COVID-19.

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A Framework for Advancing Precision Medicine in Clinical Trials for Mental Disorders

Cited by 43 publications

References 10 publications

Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric In-Patients

Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric In-Patients

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health

Psychological Networks can Identify Potential Pathways to Specific Intervention Targets for Anxiety in Response to Coronavirus Disease 2019 (COVID-19)

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