Computational strategies to tailor existing interventions for first major depressive episodes to inform and test personalized interventions

Active Project Grant Awarded Last action

Award ID
R01MH132973
Recipient
Awarding agency
Obligated
$6,302,646

Award facts

Assistance listing
93.242 Mental Health Research Grants
Completion date 67% complete
Place of performance
Larkfield-Wikiup, CA 95403, USA

Award details come from USAspending.gov.

Description

Abstract
Major depressive disorder (MDD) is a chronic, recurrent illness impacting 20.6% of the U.S. Population, causing significant disability and an economic impact of $326.2 billion annually. One of the largest risk factors for depression chronicity and disability is inadequate antidepressant response, defined as less than a 50% improvement in depressive symptoms after starting antidepressant treatment. Antidepressants are recommended as a first-line depression treatment and taken by 70% of patients with depression. Inadequate antidepressant response is experienced by 50-60% of patients starting an antidepressant and is responsible for 47% of the economic impact and disability caused by MDD. As such, identifying risk for inadequate antidepressant response early, during a patient's first clinical presentation for a depressive episode, would be an innovative, urgently needed first step towards preventing recurrent depressive episodes, reducing depression chronicity and disability, and improving MDD outcomes. This step aligns with the national institute of mental health (nimh) strategic plan objective 3.2 to "develop strategies for tailoring existing interventions (antidepressants) to optimize (depressive episode) outcomes." while previous studies identified separate predictors of antidepressant response, no study to date has focused on integrating known and novel predictors of inadequate antidepressant response during a patient's first depressive episode. This knowledge GAP exists as no large studies in diverse populations have integrated comprehensive clinical, demographic, genetic, and behavioral information in one model to predict inadequate antidepressant response prior to first antidepressant treatment. Such information is crucial to improve patient care, reduce depressive disorder chronicity and disability, and tailor existing patient interventions to optimize MDD outcomes. Utilizing electronic health record data from three large, integrated healthcare systems representing over 6.9 million members (kaiser permanente (KP) northern california, KP washington, and healthpartners), we aim to quantify inadequate antidepressant response risk at the time of a patient's first clinical presentation for a depressive episode by integrating clinical, demographic, genetic, and behavioral information in one predictive model. To accomplish this aim, we will use translational machine learning and predictive modeling, internal and external model validation and testing, prospective validation, and existing genome-wide genotypic data. Further, we will examine barriers and facilitators to clinical applications of predictive models for MDD to facilitate clinical translation and implementation of the predictive model, reducing the time between research innovation and clinical application. Our long-term goal is to develop a clinical tool informing decision making and promoting MDD treatment optimization.

Transactions

Action Obligated Date
Amendment $768,153
Amendment $767,255
Amendment $79,852
Amendment $718,665
Amendment $718,665
Amendment $0
Amendment $0
Amendment $0
Amendment $812,514
Amendment $812,514
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Original award $812,514
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Subawards

Subrecipient Amount Awarded
Healthpartners Institute As the Site Pl on this project, Dr. Rossom will oversee all study activities at HealthPartners Institute, including ensuring IRB approval and the execution of… $81,567
Healthpartners Institute Dr. Rossom will oversee all study activities at HealthPartners Institute, including ensuring IRB approval and the execution of all necessary data use… $118,070
Healthpartners Institute As the Site PI on this project, Dr. Rossom will oversee all study activities at HealthPartners Institute, including ensuring IRB approval and the execution of… $89,327
Healthpartners Institute As the Site Pl on this project, Dr. Rossom will oversee all study activities at HealthPartners Institute, including ensuring IRB approval and the execution of… $98,261

Quick answers

Who received award R01MH132973?

R01MH132973 was awarded to Kaiser Foundation Hospitals by National Institute of Mental Health (National Institutes of Health) on July 19, 2023.

How much is award R01MH132973 worth?

$6,302,646 has been obligated on R01MH132973.

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