ArticleBrain and behavior2021
How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting?
Article in Brain and behavior, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Brain structural and functional aberrant of hippocampal subregions was associated with somatic depression.BMC psychiatry · 2025Article
- A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach.JMIR formative research · 2023Article
- How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting?Brain and behavior · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo create and validate a model to predict depression symptom severity among patients with treatment-resistant depression (TRD) using commonly recorded variables within medical claims databases.
methodsAdults with TRD (here defined as > 2 antidepressant treatments in an episode, suggestive of nonresponse) and ≥ 1 Patient Health Questionnaire (PHQ)-9 record on or after the index TRD date were identified (2013-2018) in Decision Resource Group's Real World Data Repository, which links an electronic health record database to a medical claims database. A total of 116 clinical/demographic variables were utilized as predictors of the study outcome of depression symptom severity, which was measured by PHQ-9 total score category (score: 0-9 = none to mild, 10-14 = moderate, 15-27 = moderately severe to severe). A random forest approach was applied to develop and validate the predictive model.
resultsAmong 5,356 PHQ-9 scores in the study population, the mean (standard deviation) PHQ-9 score was 10.1 (7.2). The model yielded an accuracy of 62.7%. For each predicted depression symptom severity category, the mean observed scores (8.0, 12.2, and 16.2) fell within the appropriate range.
conclusionsWhile there is room for improvement in its accuracy, the use of a machine learning tool that predicts depression symptom severity of patients with TRD can potentially have wide population-level applications. Healthcare systems and payers can build upon this groundwork and use the variables identified and the predictive modeling approach to create an algorithm specific to their population.
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