Evidence
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
JAMA Netw Open. 2023 Sep 5;6(9):e2336094. doi: 10.1001/jamanetworkopen.2023.36094.
ABSTRACT
IMPORTANCE: Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging.
OBJECTIVE: To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications.
DESIGN, SETTING, AND PARTICIPANTS: This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022.
EXPOSURES: In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment.
MAIN OUTCOMES AND MEASURES: The model’s performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity.
RESULTS: The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model’s specificity in predicting SSRIs treatment response.
CONCLUSIONS AND RELEVANCE: In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.
PMID:37768659 | DOI:10.1001/jamanetworkopen.2023.36094
![Google](https://www.google.com/images/branding/googlelogo/2x/googlelogo_light_color_92x30dp.png)
![Google Keep](https://www.gstatic.com/images/branding/product/1x/keep_48dp.png)
![Share on Linkedin](https://psychiatryai.com/wp-content/uploads/2023/10/linkedin-logo-png-2048-1.png)
Estimated reading time: 7 minute(s)
Latest: Psychiatryai.com #RAISR4D Evidence
![](/wp-content/uploads/2024/04/bd462cc11bcf0bd0d0d6f1d0f8b7cd04-modified-1.png)
Cool Evidence: Engaging Young People and Students in Real-World Evidence
![](/wp-content/uploads/2024/04/bd462cc11bcf0bd0d0d6f1d0f8b7cd04-modified-1.png)
Real-Time Evidence Search [Psychiatry]
![](/wp-content/uploads/2024/04/pubmed.png)
AI Research
![](/wp-content/uploads/2024/05/Il5nR_nf_400x400-modified-1.png)
Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
🌐 90 Days
Evidence Blueprint
Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)
☊ AI-Driven Related Evidence Nodes
(recent articles with at least 5 words in title)
More Evidence
![](https://psychiatryai.com/wp-content/uploads/2023/04/psychiatryai_com.webp)