Person-centered diagnostics and prediction for child dysregulatory psychopathology using novel phenotypes

Active Cooperative Agreement Awarded Last action

Award ID
UF1MH135970
Recipient
Awarding agency
Obligated
$9,762,321

Award facts

Assistance listing
93.242 Mental Health Research Grants
Completion date 50% complete
Place of performance
Portland, OR 97239, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
This U01 proposal is submitted in response to the nimh impact RFA (RFA-MH-23-105). IT will extend research efforts to both define novel, deployable behavioral phenotypes (highlighting cognitive and emotional transdiagnostic measures) and develop and test diagnostic and prognostic prediction models, using advanced analytics, within the clinic setting. IT does so for children aged 7-17, presenting with common forms of psychopathology, characterized by dysregulation of attention, behavior, and/or emotion. Aim 1 uses established longitudinal research cohorts to further refine computational phenotypes for cognition (executive functions, alertness/arousal) and low-cost trait ratings relevant to emotional regulation and valence, and in combination with key environmental variables, tests their cross-sectional and longitudinal predictive utility using machine learning models. Aim 2 uses large electronic medical record (EMR) data to refine sophisticated neural network models to enhance mental health diagnostics and outcome prediction in children. Aims 3 and 4 entail new data collection of several thousand patients in four pediatric and psychiatry clinic sites around the country, and then combines and extends findings from aims 1 and 2 to test their diagnostic and prognostic effectiveness in these diverse patient populations. The study is significant in its potential to open the way for clinical care to benefit from years of scientific progress in phenotype refinement that are low cost and deployable. IT is further significant in its potential to harvest from existing EMR data far more clinically useful prediction algorithms than are currently available. The inclusion of sequential bayesian logic for aiding clinicians in deciding which cases require additional assessment and which do not will be transformative in opening a path for significant savings in cost and clinician time by improving efficiency of care. The ability to better predict critical outcomes, such as worsening of mental health symptoms, suicidality, or increased resource utilization is urgent and will be addressed in our work. This project is innovative in combining large machine learning models from EMR data with similar models using novel research phenotypes and will be the first prospective test of these models in patients recruited from active clinics in multiple locations to evaluate generalizability. Further significance and innovation are added by careful attention to the role of environmental adversity and extensive plans to minimize or overcome the asynchronous benefit of such efforts to historically under-served and under-represented populations. The project directly, significantly, and with innovation addresses the goals and purpose of the impact RFA by aiming to demonstrate how novel behavioral phenotypes can enhance clinical care even as maximum value is extracted from EMR data already in hand.

Subawards

Subrecipient Amount Awarded
Syracuse University Person-centered diagnostics and prediction for child dysregulatory psychopathology using novel phenotypes $339,079
Trustees Of Indiana University $1,047,498
Seattle Children's Hospital $605,535
The Research Foundation For The State University Of New York $300,337

Quick answers

Who received award UF1MH135970?

UF1MH135970 was awarded to Oregon Health & Science University by National Institute of Mental Health (National Institutes of Health) on May 15, 2024.

How much is award UF1MH135970 worth?

$9,762,321 has been obligated on UF1MH135970.

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