Prediction of REsponse to Depression Interventions Using Clinical and TD-fNIRS Measurements (PREDICT)

This observational cohort study (n=540) aims to gather unified data from a large cohort of patients with Major Depressive Disorder (MDD) undergoing various treatment (including ketamine & esketamine) options to identify optimal biomarkers for depression treatments at the individual patient level.

This study, sponsored by Kernel, involves enrolling 540 adult participants seeking treatment for depression across several clinics in the United States. The participants will be divided into cohorts based on the treatment they receive: ketamine, SPRAVATO, TMS, or antidepressants. Each cohort will undergo fNIRS (functional Near Infrared Spectroscopy) measurements using the Kernel Flow2 system at pre-treatment, early-treatment, and post-treatment visits.

The primary outcome measures include assessing brain hemodynamic activity, optical properties of the brain, and physiological features with TD-fNIRS. Secondary outcome measures will include various clinical scales such as the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Patient Health Questionnaire (PHQ-9) to evaluate depressive symptoms.

The study aims to better understand individual responses to different depression interventions, ultimately contributing to more tailored and effective treatment approaches for MDD patients. It commenced in August 2023 and is estimated to be completed by December 2024.

Status Enrolling by invitation
Results Published No
Start date 14 August 2023
End date 31 December 2024
Phase Not Applicable
Design Open
Type Observational
Participants 540
Sex All
Age 18- 75
Therapy No

Trial Details

The purpose of the current study is to perform a unified, homogeneous data collection protocol that includes a large cohort of patients undergoing different treatment options for Major Depressive Disorder (MDD) as an avenue for investigating optimal biomarkers for depression treatments on an individual patient level.

Trial Number NCT06002100

Data attribution

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