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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- Article
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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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- Article
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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.
13 - Managing Carbon: Ecological Limits and Constraints
- Edited by Daniel G. Brown, University of Michigan, Ann Arbor, Derek T. Robinson, University of Waterloo, Ontario, Nancy H. F. French, Michigan Technological University, Bradley C. Reed, United States Geological Survey, California
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- Book:
- Land Use and the Carbon Cycle
- Published online:
- 05 February 2013
- Print publication:
- 28 January 2013, pp 331-358
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Summary
Introduction
Humans have been managing terrestrial carbon (C) since time immemorial as a way to obtain energy stored in vegetation, food, and fiber from domesticated crops and animals, as well as wood products from forests. This manipulation of terrestrial C, inadvertent at first, has been more deliberate since 1800 and led to a net release of C to the atmosphere of about 200 Pg C since then. The net annual carbon dioxide (CO2) flux to the atmosphere from vegetation and soils, currently estimated at 1.2 Pg C·y−1 mainly due to land-use changes in tropical environments, is a major factor contributing to rising atmospheric CO2.
In 1977, Freeman Dyson hypothesized that the accumulation of CO2 in the atmosphere could be controlled via tree planting and estimated that approximately 4.5 Pg C · y−1 could be sequestered this way (Dyson 1977). The possibility of storing C in soils as a way to mitigate atmospheric CO2 increase and to restore lost soil organic matter and fertility emerged about two decades ago. Cole et al. (1997) estimated that about two-thirds of the historical losses of soil organic carbon (SOC) (approximately 40 Pg C) could be sequestered over 50 to 100 years through the implementation of nutrient management, cropping intensity, diversified crop rotation, and reduced tillage practices. In the Intergovernmental Panel on Climate Change (IPCC) second assessment report, Brown et al. (1996) estimated that about 38 Pg C could be sequestered on 345 × 106 hectares during 50 years via afforestation, reforestation, and agroforestry practices. Indeed, the Kyoto Protocol recognized afforestation and reforestation as mitigation practices implementable through the Clean Development Mechanism (CDM). Although the importance of soils as a C repository was recognized in the Kyoto Protocol, this technology was not included as a mitigation practice during the first commitment period (2008 to 2012) due to measurement uncertainties.
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