This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Predicting treatment dropout after antidepressant initiation
Translational Psychiatry Open Access 06 February 2020
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Wisniewski SR, Rush AJ, Nierenberg AA, Gaynes BN, Warden D, Luther JF, et al. Can phase III trial results of antidepressant medications be generalized to clinical practice? A STAR*D report. Am J Psychiatry. 2009;166:599–607.
Carroll J. AstraZeneca cutting 2,200 R&D jobs, slashing neuroscience in restructuring [Internet]. 2012. Available from: https://www.fiercebiotech.com/r-d/updated-astrazeneca-cutting-2-200-r-d-jobs-slashing-neuroscience-restructuring.
Bech P. Rating scales in depression: limitations and pitfalls. Dialog Clin Neurosci. 2006;8:207–15.
Regier DA, Narrow WE, Clarke DE, Kraemer HC, Kuramoto SJ, Kuhl EA, et al. DSM-5 field trials in the United States and Canada, part II: test-retest reliability of selected categorical diagnoses. Am J Psychiatry. 2013;170:59–70.
Sellgren C, Sheridan S, Gracias J, Xuan D, Fu T, Perlis R. Patient-specific models of microglia-mediated engulfment of synapses and neural progenitors. Mol Psychiatry. 2017;22:170–7.
Grundmeier RW, Swietlik M, Bell LM. Research subject enrollment by primary care pediatricians using an electronic health record. AMIA Annu Symp Proc 2007;289–93.
Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, Teich JM, et al. Drug–drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc JAMIA. 2013;20:489–93.
Perlis RH. Abandoning personalization to get to precision in the pharmacotherapy of depression. World Psychiatr Assoc. 2016;15:228–35.
Perlis RH, Iosifescu DV, Castro VM, Murphy SN, Gainer VS.Minnier J,et al. Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model. Psychol Med. 42:41–50..
US Food and Drug Administration. Early Alzheimer’s Disease: developing drugs for treatment guidance for industry [Internet]. 2018. Available from: https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM596728.pdf.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
In the past three years, Roy Perlis has served on advisory boards or provided consulting to Genomind, Psy Therapeutics, RIDVentures, and Takeda. He receives patent royalties from Massachusetts General Hospital. In the past three years, Maurizio Fava reports the following research support: Alkermes, Inc., Johnson & Johnson, Axsome, Acadia Pharmaceuticals, Cerecor, Lundbeck Inc., Neuralstem, Otsuka, Taisho, Marinus Pharmaceuticals, BioHaven, Takeda, Vistagen, Relmada Therapeutics Inc., Stanley Medical Research Institute (SMRI), National Institute of Drug Abuse (NIDA), National Institute of Mental Health (NIMH), and PCORI. Dr. Fava has not done any personal consulting. Any consulting he has done has been on behalf of Massachusetts General Hospital. Dr. Fava has equity holdings in Compellis and PsyBrain Inc. Dr. Fava has patents for Sequential Parallel Comparison Design (SPCD), licensed by MGH to Pharmaceutical Product Development, LLC; a patent application for a combination of Ketamine plus Scopolamine in Major Depressive Disorder (MDD), licensed by MGH to Biohaven; and patents for pharmacogenomics of Depression Treatment with Folate. Dr. McCoy declares that he has no conflict of interest.
Rights and permissions
About this article
Cite this article
Perlis, R.H., Fava, M. & McCoy, T.H. Can electronic health records revive central nervous system clinical trials?. Mol Psychiatry 24, 1096–1098 (2019). https://doi.org/10.1038/s41380-018-0278-z
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41380-018-0278-z
This article is cited by
-
Predicting treatment dropout after antidepressant initiation
Translational Psychiatry (2020)