Treatment Resistant Depression, Ketamine versus ECT

Recent studies have highlighted the increase in treatment resistant depression. Of particular concern is the rising trend of depression and suicide rates among Young Adults. Ketamine was approved for treatment resistant depression in 2019 by the US Food and Drug Administration. It received an additional indication for treatment of suicidality. While intranasal Ketamine is approved for depression, recent data about intravenous infusion of Ketamine in controlled inpatient settings has been promising. ECT has a long-standing trend for being used for resistant depression and recent comparison trials have revealed positive results when head-to-head comparisons are made with Ketamine. Future studies need to focus on patient selection and wherein treatment algorithm should Ketamine be selected as treatment modality.

Pramipexole in Treatment Resistant Depression: A Case Review

Pramipexole is a dopamine agonist used in the treatment of Parkinson’s disease and Restless legs syndrome. Although off-label, the use of Pramipexole as an adjunct therapy in treatment resistant depression has recently been documented in the literature with promising results. We present a 75-year-old male with MDD who has failed trials of SSRIs, SNRIs, TCAs, SGA, TMS, Ketamine, and ECT who was initiated on Pramipexole. We discuss, based on existing literature, the probability of a favorable long-term response to Pramipexole and the potential side effects for our patient. We also highlight the need for future studies designed to test the efficacy of Pramipexole in geriatric patients with TRD.

The Utility of Artificial Intelligence for Mood Analysis,Depression Detection,and Suicide Risk Management

Mood disorders are often an indication or a sign of depression,and individuals suffering from mood swings may face higher probability and increased suicidal tendencies.Depression-also called“clinical depression”or a“depressive disorder”-is a mood disorder that adversely impacts how an individual feels,thinks,and handles daily activities,such as sleeping,eating,or working.To be diagnosed with depression,symptoms must be present most of the time,nearly every day for at least minimum of 2 to 3 weeks.Feeling sad or having low emotional energy may be common among people.For most,however,these feelings are transitory and can be managed by changing daily life routines.But for some,prolonged mood disorders can lead to depression and foster suicidal tendencies.Suicide is a major public health concern.Over 47,000 people died by suicide in the United States in 2017.It is the 10th leading cause of death overall according to NIMH(National Institute of Mental Health).Suicide is complicated and tragic,but it is often preventable.Identifying the warning signs for suicide and how to get help can be a major mitigating factor.In this short communication,we are reviewing the promise and limitations of AI(artificial intelligence)with its integrated tools such as ML(machine learning)and DL(deep learning)for mood analysis as a means for detecting early signs of depression and increased suicidal tendencies for possible suicide risk management.

Can the management of depression in type 2 diabetes be democratized?

Both type 2 diabetes and depression are common and are projected to increase.There is increasing evidence for a bidirectional relationship between the two.Diabetes is a risk factor for depression;contrariwise,individuals with depression are at greater risk of developing diabetes.They are a burden for both the individual and the society.Co-existent depression worsens diabetic control because of obesity,insulin resistance and the adverse metabolic effects of anti-diabetes medicines.In addition,compliance to lifestyle measures required for diabetes is also compromised such as following a specific diet,taking proper medications on time,getting metabolic parameters assessed and maintaining a sleep cycle.Depression occurs in many grades;mild depression is more common in diabetes than frank or full-blown depression leading to suicide.Unfortunately,there are not enough trained and accessible mental health professionals such as psychologists or psychiatrists to deal with the increasing burden of depression in diabetes.Therefore,alternate models for management of mild to moderate depression are required.There is evidence that a team-approach by employing health care assistants can lower the risk of cardiac risk factors.INtegrating DEPrEssioN and Diabetes treatmENT study was carried out to determine whether the team-approach using non-health care professionals could be effective in managing mild to moderate depression and to study its effects on metabolic parameters among subjects with type 2 diabetes mellitus.The international study,carried out in four independent centers in India assessed the impact of a trained but not qualified non-psychiatrist in coordinating and forming a fulcrum between the patient,the family and the consultant endocrinologist/diabetologist.The interventions were fine-tuned to be culturally appropriate by qualitative interviews before they began.It was shown that the outcomes of both depression and diabetes could be improved by the employment of a clinical care coordinator.It is possible to scale up the studies to wider geographical areas and health-care organizations.

Acupuncture Treatment of Depressive Syndromes after Cerebral Vascular Accidents

Depressive syndrome is a common complication in cerebral vascular accidents (CVA), which accounts for 70% of the complications. A mild case of the syndrome is marked by grief, sleeping disturbance, weakened mental activities, worry, loss of interest, and irritability. A severe case may have worse symptoms and signs, except the above mentioned ones, such as anxiety, reduced appetite, hallucination, delusion, desperation, etc. The author has adopted acupuncture to treat depressive syndromes after CVA with good therapeutic result as reported in the following.

Subgrouping time-dependent prescribing patterns of first-onset major depressive episodes by psychotropics dissection

BACKGROUND Subgrouping patients with major depressive disorder is a promising solution for the issue of heterogeneity.However,the link between available subtypes and distinct pathological mechanisms is weak and yields disappointing results in clinical application.AIM To develop a novel approach for classification of patients with time-dependent prescription patterns at first onset in real-world settings.METHODS Drug-naive patients experiencing their first major depressive episode(n=105)participated in this study.Psychotropic agents prescribed in the first 24 mo following disease onset were recorded monthly and categorized as antidepressants,augmentation agents,and hypnosedatives.Monthly cumulative doses of agents in each category were converted into relevant equivalents.Four parameters were used to summarize the time-dependent prescription patterns for each psychotropic load:Stability,amount,frequency,and the time trend of monthly prescriptions.A K-means cluster analysis was used to derive subgroups of participants based on these input parameters of psychotropic agents across 24 mo.Clinical validity of the resulting data-driven clusters was compared using relevant severity indicators.RESULTS Four distinct clusters were derived from K-means analysis,which matches experts’consent:"Short-term antidepressants use","long-term antidepressants use","long-term antidepressants and sedatives use",and"long-term antidepressants,sedatives,and augmentation use".At the first 2 years of disease course,the four clusters differed on the number of antidepressants used at adequate dosage and duration,frequency of outpatient service use,and number of psychiatric admissions.After the first 2 years following disease onset,depression severity was differed in the four subgroups.CONCLUSION Our findings suggested a new approach to optimize the subgrouping of patients with major depressive disorder,which may assist future etiological and treatment response studies.