PSYCHIATRY IN REVIEW
Dr. Aftab, a PGY4 Chief Resident for Education and Research at Case Western Reserve University/University Hospitals Cleveland Medical Center (CWRU/UHCMC) psychiatry residency program, shares his views on important topics in 2017 in the field of psychiatry.
1) Psychedelics in Psychiatry
Multidisciplinary Association for Psychedelic Studies (MAPS) reported that on August 16, 2017, the FDA granted breakthrough therapy designation to 3,4-methylenedioxy-methamphetamine (MDMA, also called Ecastasy or Molly) for the treatment of PTSD as adjunct to psychotherapy. Breakthrough therapy designation is for treatment that treat a serious or life-threatening condition, and preliminary clinical evidence indicates substantial improvement over existing therapies. Two phase 3 randomized, double-blind, placebo-controlled, multi-site clinical trials are currently planned to assess the safety and efficacy of MDMA-assisted psychotherapy in 200-300 participants with severe PTSD.
This breakthrough designation was based on promising evidence from completed phase 2 trials from MAPS which included 107 patients with treatment-resistant PTSD. After three sessions of MDMA-assisted psychotherapy, 61% of the participants no longer met PTSD criteria 2 months post treatment. Earlier in 2016 a single large dose of psilocybin—another psychedelic drug—had shown benefits for patients suffering from cancer-related depression and anxiety for up to six months in two small placebo-controlled, crossover trials.
This is yet another reminder that the resurgence in research investigating therapeutic uses of psychedelics in psychiatry is making an impact, and we are likely going to see more and more work on psychedelics in coming years.
2) Machine Learning and Suicide Attempt Prediction
It is well-established that suicide risk assessment by clinicians are a poor predictor of future suicide attempts, and while research has revealed multiple risk factors, there is no comprehensive model that integrates them leading to accurate predictions of actual suicide attempts.
Walsh and colleagues, explored a machine learning approach to prediction of suicide attempts using electronic health records within a large medical database. Using data from more than five thousand subject records, a highly accurate machine learning algorithm was developed that predicted suicide attempts with an accuracy as high as AUC=0.92 (1 being perfect prediction) 7 days prior to suicide attempt and 0.86 two years prior.
If these are replicated and machine learning continues to show superior results, this could entirely change the way suicide risk assessments are conducted in the future.