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Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.
In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.
A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.
A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.
Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.
Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.
Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.
The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.
Patients with major depressive disorder (MDD) display cognitive deficits in acutely depressed and remitted states. Childhood maltreatment is associated with cognitive dysfunction in adults, but its impact on cognition and treatment related cognitive outcomes in adult MDD has received little consideration. We investigate whether, compared to patients without maltreatment and healthy participants, adult MDD patients with childhood maltreatment display greater cognitive deficits in acute depression, lower treatment-associated cognitive improvements, and lower cognitive performance in remission.
Healthy and acutely depressed MDD participants were enrolled in a multi-center MDD predictive marker discovery trial. MDD participants received 16 weeks of standardized antidepressant treatment. Maltreatment and cognition were assessed with the Childhood Experience of Care and Abuse interview and the CNS Vital Signs battery, respectively. Cognitive scores and change from baseline to week 16 were compared amongst MDD participants with (DM+, n = 93) and without maltreatment (DM−, n = 90), and healthy participants with (HM+, n = 22) and without maltreatment (HM−, n = 80). Separate analyses in MDD participants who remitted were conducted.
DM+ had lower baseline global cognition, processing speed, and memory v. HM−, with no significant baseline differences amongst DM−, HM+, and HM− groups. There were no significant between-group differences in cognitive change over 16 weeks. Post-treatment remitted DM+, but not remitted DM−, scored significantly lower than HM− in working memory and processing speed.
Childhood maltreatment was associated with cognitive deficits in depressed and remitted adults with MDD. Maltreatment may be a risk factor for more severe and persistent cognitive deficits in adult MDD.
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