Dataset Information

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

ABSTRACT:

Background

Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers.

Methods

Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as determined by the Hamilton Rating Scale for Depression (HRSD), MDD patients were sorted into three groups: mild to moderate depression (HRSD 14-19), severe depression (HRSD 20-23), and very severe depression (HRSD ? 24). We collected functional magnetic resonance imaging (fMRI) data during both resting-state and an emotional-face matching task. Patients in each of the three severity groups were compared against controls in separate analyses, using either the resting-state or task-based fMRI data. We use each of these six datasets with linear support vector machine (SVM) binary classifiers for identifying individuals as patients or controls.

Results

The resting-state fMRI data showed statistically significant classification accuracy only for the very severe depression group (accuracy 66%, p = 0.012 corrected), while mild to moderate (accuracy 58%, p = 1.0 corrected) and severe depression (accuracy 52%, p = 1.0 corrected) were only at chance. With task-based fMRI data, the automated classifier performed at chance in all three severity groups.

Conclusions

Binary linear SVM classifiers achieved significant classification of very severe depression with resting-state fMRI, but the contribution of brain measurements may have limited potential in differentiating patients with less severe depression from healthy controls.

SUBMITTER: Ramasubbu R

PROVIDER: S-EPMC4983635 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

Ramasubbu Rajamannar R Brown Matthew R G MR Cortese Filmeno F Gaxiola Ismael I Goodyear Bradley B Greenshaw Andrew J AJ Dursun Serdar M SM Greiner Russell R

NeuroImage. Clinical 20160727

<h4>Background</h4>Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-Control diagnostic classifiers.<h4>Methods</h4>Forty-five medication-free patients with DSM-IV defined MDD and 19 healthy controls participated in the study. Based on depression severity as det ...[more]

PMID: 27551669

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Project description:Background: Major Depressive Disorder (MDD) is a moderately heritable disorder with a high lifetime prevalence. At present, laboratory blood tests to support MDD diagnosis are not available. Methods: We used a classifier approach on blood gene expression profiles of a unique set of non-medicated subjects (MDD patients and controls) to select genes of which expression is predictive for disease status. To reveal blood gene expression changes related to MDD disease, we applied a powerful ex vivo stimulus to the blood, i.e. incubation with lipopolysaccharide (LPS; 10 ng/ml blood). Results: Based on LPS-stimulated blood gene expression using whole-genome microarrays in 42 subjects (primary cohort; 21 MDD patients (mean age 42.3 years), 21 healthy controls (mean age 41.9 years)), we identified a set of genes (CAPRIN1, CLEC4A, KRT23, MLC1, PLSCR1, PROK2, ZBTB16) that serves as a molecular signature of MDD. These findings were validated for the primary cohort using an independent quantitative PCR method (P = 0.007). The difference between depressive patients and controls was confirmed (P = 0.019) in a replication cohort of 13 patients with MDD (mean age 42.8 years) and 14 controls (mean age 45.6 years). The MDD-signature score comprised of expression levels of 7 genes could discriminate depressive patients from controls with sensitivity of 76.9% and specificity of 71.8%. Conclusions: We show for the first time that molecular analysis of stimulated blood cells can be used as an endophenotype for MDD diagnosis, which is a milestone in establishing biomarkers for neuropsychiatric disorders with moderate heritability in general. Our results may provide a new entry point for following and predicting treatment outcome, as well as prediction of severity and recurrence of MDD. In total, 33 MDD patients and 34 healthy controls were analyzed using basal gene expression in whole blood, and gene expression from whole blood that was stimulated with LPS for 5-6 h, using microarrays. Patients were arbitrarily selected from all patients to serve as primary cohort (nMDD = 21 (MDD01-MDD21); nControls = 21 (Con01-Con21)), or replication cohort (nMDD = 12 (MDD22-MDD35); nControls = 13 (Con22-Con37)) using microarrays. This submission does not include Samples CON21_LPS or CON30_LPS.

Project description:BackgroundDespite the availability of pharmacologic and nonpharmacologic treatment options, depression continues to be one of the leading causes of disability worldwide. This study evaluated whether depression symptom severity, as measured by PHQ-9 score, of patients diagnosed with MDD is associated with short-term risk of a hospital encounter (ER visit or inpatient stay).MethodsAdults with ≥1 PHQ-9 assessment in an outpatient setting (index date) and ≥ 1 MDD diagnosis within 6 months prior were included from the de-identified Optum Electronic Health Record database (April 2016-June 2019). Patients were categorized by depression symptom severity based on PHQ-9 scores obtained by natural language processing. Crude rates, adjusted absolute risks, and adjusted relative risks of all-cause and MDD-related hospital encounters within 30 days following assessment of depression severity were determined.ResultsThe study population consisted of 280,145 patients with MDD and ≥ 1 PHQ-9 assessment in an outpatient setting. Based on PHQ-9 scores, 26.9% of patients were categorized as having none/minimal depression symptom severity, 16.4% as mild, 24.7% as moderate, 19.6% as moderately severe, and 12.5% as severe. Among patients with none/minimal, mild, moderate, moderately severe, and severe depression, the adjusted absolute short-term risks of an initial all-cause hospital encounter were 4.1, 4.4, 4.8, 5.6, and 6.5%, respectively; MDD-related hospital encounter adjusted absolute risks were 0.8, 1.0, 1.3, 1.6, and 2.1%, respectively. Compared to patients with none/minimal depression symptom severity, the adjusted relative risks of an all-cause hospital encounter were 1.60 (95% CI 1.50-1.70) for those with severe, 1.36 (1.29-1.44) for those with moderately severe, 1.18 (1.12-1.25) for those with moderate, and 1.07 (1.00-1.13) for those with mild depression symptom severity.ConclusionsThese study findings indicate that depression symptom severity is a key driver of short-term risk of hospital encounters, emphasizing the need for timely interventions that can ameliorate depression symptom severity.

Dataset Information

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

Background

Methods

Results

Conclusions

Publications

Accuracy of automated classification of major depressive disorder as a function of symptom severity.

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