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Machine learning to personalize antidepressant treatment

Award Number: R01MH134831
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 07/01/2024
PERIOD OF PERFORMANCE END DATE: 04/30/2027

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Machine learning to personalize antidepressant treatment - Half of people starting antidepressant medication experience no significant improvement with the first medication prescribed, leading either to discouragement and dropout or months of trial-and-error. More accurately matching individuals with specific medications could significantly increase treatment success. While selecting antidepressant medication is one of the most common clinical decisions in mental health and general medical care, actionable evidence to guide that decision is remarkably scarce. Matching patients with specific medications remains no better than chance. We identify three key gaps in previous research to personalize antidepressant selection. First, previous research has not clearly distinguished predicting overall prognosis or nonspecific response from predicting specific response to specific medications. Second, sample sizes have typically been inadequate to accurately detect differential treatment response. Third, previous research has focused on response to a single episode of treatment, and that is too “noisy” a clinical phenotype to support identification of useful endophenotypes. We propose to address these limitations using data resources, informatics tools, and analytic methods developed by the NIMH-funded Mental Health Research Network (MHRN). Records databases include comprehensive and harmonized data concerning sociodemographic characteristics, past mental health diagnoses and treatments, comorbid mental health or substance use disorders, concomitant treatments, dose and duration of antidepressant exposure, and structured assessment of depression symptoms at baseline and follow-up. We will assemble a database including over 500,000 antidepressant treatment episodes for over 200,000 patients treated since 2010 in order to: 1) Rigorously test core assumptions of previous research and treatment guidelines - Heterogeneity-of- treatment-effects analyses will evaluate two common beliefs regarding antidepressant response: a. Favorable or unfavorable prior response to a single medication predicts subsequent response b. Prior response to a medication in a specific class predicts subsequent response to that class 2) Identify and validate decision rules to inform selection of antidepressant treatment based on patterns of prior treatment response – Analyses will use machine learning methods (dynamic weighted ordinary least squares regression) to identify and validate practical decision rules for clinical practice. 3) Identify valid treatment response phenotypes to enable future research regarding genomic- or biomarker-guided personalization – Primary analyses will use k-means clustering to identify patterns of response across medications and medication classes. Additional analyses will explore clinical characteristics that distinguish response phenotypes as well as order effects. Findings will inform a next generation of research regarding genetic- and biomarker-guided antidepressant treatment.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $765,573 )
2026	2026	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Mental Health Research Grants	000	3	5/1/2026	NON-COMPETING CONTINUATION	$765,573
														Subtotal = $765,573

Issue Date FY: 2025 ( Subtotal = $773,886 )
2025	2025	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Mental Health Research Grants	000	2	9/2/2025	NON-COMPETING CONTINUATION	$773,886
														Subtotal = $773,886

Issue Date FY: 2024 ( Subtotal = $818,413 )
2024	2024	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Mental Health Research Grants	000	1	6/27/2024	NEW	$818,413
														Subtotal = $818,413

Grand Total All Awards = $2,357,872

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Machine learning to personalize antidepressant treatment

Award Number: R01MH134831

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 07/01/2024

PERIOD OF PERFORMANCE END DATE: 04/30/2027

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