Madhukar H Trivedi1, Charles South1, Manish K Jha1, A John Rush2,3,4, Jing Cao5, Benji Kurian1, Mary Phillips6,7, Diego A Pizzagalli8, Joseph M Trombello1, Maria A Oquendo9, Crystal Cooper1, Daniel G Dillon8, Christian Webb8, Bruce D Grannemann1, Gerard Bruder10, Patrick J McGrath10, Ramin Parsey11, Myrna Weissman10, Maurizio Fava12. 1. Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA. 2. Duke-National University of Singapore, Singapore, Singapore. 3. Duke Medical School, Durham, North Carolina, USA. 4. Texas Tech University Health Sciences Center, Permian Basin, Texas, USA. 5. Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA. 6. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. 7. Columbia University, New York, New York, USA. 8. Center for Depression, Anxiety and Stress Research, Mclean Hospital, Belmont, Massachusetts, USA. 9. Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA. 10. New York State Psychiatric Institute and Department of Psychiatry, College of Physicians and Surgeons, New York, New York, USA. 11. Stony Brook University School of Medicine, Stony Brook, New York, USA. 12. Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts, USA.
Abstract
BACKGROUND: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. METHODS: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. RESULTS: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. CONCLUSION: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.
BACKGROUND: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. METHODS: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. RESULTS: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. CONCLUSION: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.
Authors: Hitoshi Sakurai; Linda L Carpenter; Audrey R Tyrka; Lawrence H Price; George I Papakostas; Christina M Dording; Albert S Yeung; Cristina Cusin; Elizabeth Ludington; Richard Bernard-Negron; Maurizio Fava; David Mischoulon Journal: J Affect Disord Date: 2019-10-31 Impact factor: 4.839
Authors: Erica A Smith; William P Horan; Dominique Demolle; Peter Schueler; Dong-Jing Fu; Ariana E Anderson; Joseph Geraci; Florence Butlen-Ducuing; Jasmine Link; Ni A Khin; Robert Morlock; Larry D Alphs Journal: Innov Clin Neurosci Date: 2022 Jan-Mar
Authors: Diego A Pizzagalli; Moria Smoski; Yuen-Siang Ang; Alexis E Whitton; Gerard Sanacora; Sanjay J Mathew; John Nurnberger; Sarah H Lisanby; Dan V Iosifescu; James W Murrough; Hongqiu Yang; Richard D Weiner; Joseph R Calabrese; Wayne Goodman; William Z Potter; Andrew D Krystal Journal: Neuropsychopharmacology Date: 2020-06-16 Impact factor: 7.853