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Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches.

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Title: Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches.
Authors: Buckman, J. E. J., Cohen, Z. D., O'Driscoll, C., Fried, E. I., Saunders, R., Ambler, G., DeRubeis, R. J., Gilbody, S., Hollon, S. D., Kendrick, T., Watkins, E., Eley, T.C., Peel, A. J., Rayner, C., Kessler, D., Wiles, N., Lewis, G., Pilling, S.
Source: Psychological Medicine. Jan2023, Vol. 53 Issue 2, p408-418. 11p.
Subjects: Life change events, Social support, Treatment effectiveness, Psychological tests, Mental depression, Alcohol drinking, Factor analysis, Prediction models, Anxiety, Adults
Abstract: Background: This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. Methods: Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1–3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3–4 months. Results: Models 1–7 all outperformed the null model and model 8. Model performance was very similar across models 1–6, meaning that differential weights applied to the baseline sum scores had little impact. Conclusions: Any of the modelling techniques (models 1–7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches.
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  Data: <searchLink fieldCode="JN" term="%22Psychological+Medicine%22">Psychological Medicine</searchLink>. Jan2023, Vol. 53 Issue 2, p408-418. 11p.
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  Data: Background: This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. Methods: Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1–3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3–4 months. Results: Models 1–7 all outperformed the null model and model 8. Model performance was very similar across models 1–6, meaning that differential weights applied to the baseline sum scores had little impact. Conclusions: Any of the modelling techniques (models 1–7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychological Medicine is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1017/S0033291721001616
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        Text: English
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      – SubjectFull: Life change events
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      – SubjectFull: Treatment effectiveness
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      – SubjectFull: Adults
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