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.
Clinically relevant and simple immune system measure is related to symptom burden in bipolar disorder
- Ole Köhler-Forsberg, Louisa Sylvia, Thilo Deckersbach, Michael Joshua Ostacher, Melvin McInnis, Dan Iosifescu, Charles Bowden, Susan McElroy, Joseph Calabrese, Michael Thase, Richard Charles Shelton, Mauricio Tohen, James Kocsis, Edward Friedman, Terence Ketter, Andrew Alan Nierenberg
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- Journal:
- Acta Neuropsychiatrica / Volume 30 / Issue 5 / October 2018
- Published online by Cambridge University Press:
- 07 December 2017, pp. 297-305
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Objective
Immunological theories, particularly the sickness syndrome theory, may explain psychopathology in mood disorders. However, no clinical trials have investigated the association between overall immune system markers with a wide range of specific symptoms including potential gender differences.
MethodsWe included two similar clinical trials, the lithium treatment moderate-dose use study and clinical and health outcomes initiatives in comparative effectiveness for bipolar disorder study, enrolling 765 participants with bipolar disorder. At study entry, white blood cell (WBC) count was measured and psychopathology assessed with the Montgomery and Aasberg depression rating scale (MADRS). We performed analysis of variance and linear regression analyses to investigate the relationship between the deviation from the median WBC, and multinomial regression analysis between different WBC levels. All analyses were performed gender-specific and adjusted for age, body mass index, smoking, race, and somatic diseases.
ResultsThe overall MADRS score increased significantly for each 1.0×109/l deviation from the median WBC among 322 men (coefficient=1.10; 95% CI=0.32–1.89; p=0.006), but not among 443 women (coefficient=0.56; 95% CI=−0.19–1.31; p=0.14). Among men, WBC deviations were associated with increased severity of sadness, inner tension, reduced sleep, reduced appetite, concentration difficulties, inability to feel, and suicidal thoughts. Among women, WBC deviations were associated with increased severity of reduced appetite, concentration difficulties, lassitude, inability to feel, and pessimistic thoughts. Both higher and lower WBC levels were associated with increased severity of several specific symptoms.
ConclusionImmune system alterations were associated with increased severity of specific mood symptoms, particularly among men. Our results support the sickness syndrome theory, but furthermore emphasise the relevance to study immune suppression in bipolar disorder. Due to the explorative nature and cross-sectional design, future studies need to confirm these findings.