3 results
Development of a model to predict antidepressant treatment response for depression among Veterans
- Victor Puac-Polanco, Hannah N. Ziobrowski, Eric L. Ross, Howard Liu, Brett Turner, Ruifeng Cui, Lucinda B. Leung, Robert M. Bossarte, Corey Bryant, Jutta Joormann, Andrew A. Nierenberg, David W. Oslin, Wilfred R. Pigeon, Edward P. Post, Nur Hani Zainal, Alan M. Zaslavsky, Jose R. Zubizarreta, Alex Luedtke, Chris J. Kennedy, Andrea Cipriani, Toshiaki A. Furukawa, Ronald C. Kessler
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- Journal:
- Psychological Medicine / Volume 53 / Issue 11 / August 2023
- Published online by Cambridge University Press:
- 15 July 2022, pp. 5001-5011
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Background
Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
MethodsA 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
ResultsIn total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
ConclusionsAlthough these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
Development of a model to predict psychotherapy response for depression among Veterans
- Hannah N. Ziobrowski, Ruifeng Cui, Eric L. Ross, Howard Liu, Victor Puac-Polanco, Brett Turner, Lucinda B. Leung, Robert M. Bossarte, Corey Bryant, Wilfred R. Pigeon, David W. Oslin, Edward P. Post, Alan M. Zaslavsky, Jose R. Zubizarreta, Andrew A. Nierenberg, Alex Luedtke, Chris J. Kennedy, Ronald C. Kessler
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- Journal:
- Psychological Medicine / Volume 53 / Issue 8 / June 2023
- Published online by Cambridge University Press:
- 11 February 2022, pp. 3591-3600
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Background
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
MethodsThis prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
Results32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
ConclusionsPatients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder
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- R. C. Kessler, H. M. van Loo, K. J. Wardenaar, R. M. Bossarte, L. A. Brenner, D. D Ebert, P. de Jonge, A. A. Nierenberg, A. J. Rosellini, N. A. Sampson, R. A. Schoevers, M. A. Wilcox, A. M. Zaslavsky
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- Journal:
- Epidemiology and Psychiatric Sciences / Volume 26 / Issue 1 / February 2017
- Published online by Cambridge University Press:
- 26 January 2016, pp. 22-36
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Backgrounds.
Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.
Method.We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.
Results.Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.
Conclusions.Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.