Springer Science and Business Media LLC

Panic disorder is a distressing and disabling disorder characterized by recurring panic attacks; anticipatory anxiety about having additional attacks; and agoraphobic fear and avoidance of situations in which attacks have occurred, ready escape is difficult, or help is unavailable in the event of an attack. It is typically associated with impairment in social, emotional, and vocational functioning, as well as excessive utilization of the medical care system. A number of pharmacologic agents and cognitive-behavioral treatments have proven effective for the treatment of panic disorder. Selective serotonin reuptake inhibitors are becoming first-line pharmacotherapy because of their favorable side effect profile and greater spectrum of efficacy in comparison with older classes of agents. In this article, we provide an overview of the prevalence, associated health care utilization, impairment in quality of life, and treatment of panic disorder.

This article takes stock of how far the field of consultation-liaison psychiatry has come since its inception in the 20th century. In order to do this, we review its past in terms of its knowledge base in psychosomatic medicine and in terms of its practice at the bedside in the general hospital setting. We also offer a contemporary account of the field and finish with a subjective view of the opportunities and pitfalls faced during the next phase of consultation-liaison psychiatry in the 21st century.

The cognitive model of OCD suggests that misinterpreting intrusive thoughts as unacceptable leads to increased anxiety and attempts to suppress or ignore the thoughts through avoidance or compulsive rituals. An insidious negative feedback loop develops as one’s attention focuses on these thoughts and in turn the unwanted thoughts do not respond to efforts to avoid or suppress. This article is a current review of the research on cognitive processes in obsessive-compulsive disorder (OCD). We review research that has (1) empirically validated the theoretical underpinnings of the cognitive model, (2) altered maladaptive cognitive processes through state-of-the-art experimental procedures, (3) refined our understanding of the relationship between obsessive beliefs and OC symptoms and (4) examined how underlying traits (e.g., anxiety and disgust sensitivity) relate to the development and maintenance of OCD. We discuss the clinical implications of this research.

Alzheimer’s disease (AD) progresses from a preclinical period, through a middle phase of cognitive deterioration, to a late, profound state. The temporal progression of disability can be modeled with a horologic (time-based) function using “time-index” (TI) intervals (day-or yearunits) to quantify an individual’s disability across multiple cognitive and functional domains relative to a reference AD population. Clinicians and researchers can use TI quantification to assess dementia severity and initial therapy benefits. Rate of progression and confidence intervals require at least two successive measurements. Rate of progression measures can be used to support diagnosis and to investigate disease-course-modifying therapies.

We aimed to examine the factors that differentiate single and multiple suicide attempters in adult population. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to conduct this review and meta-analysis. The review protocol was registered in PROSPERO. We carried out a systematic literature search in three databases to identify original studies that explored the differences between single and multiple suicide attempters among adult population. There might be meaningful differences between those individuals that attempt suicide once in their lifespan and those who make multiple attempts in terms of sociodemographic and clinical characteristics. There are no previous meta-analysis addressing this topic in the adult population. A total of 75 studies were included in the review and 69 were included in the meta-analysis. Multiple attempters were more likely to present certain disorders such as mood and psychotic disorders, as well as personality or substance use disorders. Higher suicide ideation and suicide intent scores also characterized this group. Childhood trauma experiences, stressful life events, and higher rates of hopelessness were statistically significant in multiple attempters. Identifying the factors predicting multiple suicide attempts helps to delineate a high-risk suicidal profile that should be taken into account in the clinical and suicide prevention scenario.

Artificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology. We reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms to predict, classify, or subgroup mental health illnesses including depression, schizophrenia or other psychiatric illnesses, and suicide ideation and attempts. Collectively, these studies revealed high accuracies and provided excellent examples of AI’s potential in mental healthcare, but most should be considered early proof-of-concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions, and which types of algorithms yield the best performance. As AI techniques continue to be refined and improved, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual’s unique characteristics. However, caution is necessary in order to avoid over-interpreting preliminary results, and more work is required to bridge the gap between AI in mental health research and clinical care.