The definition of treatment resistance in anxiety disorders: a Delphi method-based consensus guideline.

Anxiety disorders are very prevalent and often persistent mental disorders, with a considerable rate of treatment resistance which requires regulatory clinical trials of innovative therapeutic interventions. However, an explicit definition of treatment-resistant anxiety disorders (TR-AD) informing such trials is currently lacking. We used a Delphi method-based consensus approach to provide internationally agreed, consistent and clinically useful operational criteria for TR-AD in adults. Following a summary of the current state of knowledge based on international guidelines and an available systematic review, a survey of free-text responses to a 29-item questionnaire on relevant aspects of TR-AD, and an online consensus meeting, a panel of 36 multidisciplinary international experts and stakeholders voted anonymously on written statements in three survey rounds. Consensus was defined as ≥75% of the panel agreeing with a statement. The panel agreed on a set of 14 recommendations for the definition of TR-AD, providing detailed operational criteria for resistance to pharmacological and/or psychotherapeutic treatment, as well as a potential staging model. The panel also evaluated further aspects regarding epidemiological subgroups, comorbidities and biographical factors, the terminology of TR-AD vs. "difficult-to-treat" anxiety disorders, preferences and attitudes of persons with these disorders, and future research directions. This Delphi method-based consensus on operational criteria for TR-AD is expected to serve as a systematic, consistent and practical clinical guideline to aid in designing future mechanistic studies and facilitate clinical trials for regulatory purposes. This effort could ultimately lead to the development of more effective evidence-based stepped-care treatment algorithms for patients with anxiety disorders.

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.

Pharmacological approaches to the management of panic disorder in older patients: a systematic review.

INTRODUCTION: Recommendations for treating panic disorder (PD) in older patients are scarce. The authors have systematically reviewed whether several recommended medications are superior to others and their optimal doses in this age group. METHODS: A database search of studies involving patients with PD with/without agoraphobia aged ≥ 60 years was carried out using PubMed, PsycINFO, Embase, and Clinical Trials.gov, from their inception dates to 1 March 2023. Only four (published from 2002 to 2010) of the 1292 records screened were included. A risk of bias assessment was provided. This systematic review was performed using The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). RESULTS: Two studies were randomized clinical trials, whereas two were open-label, including paroxetine, citalopram, escitalopram, and sertraline; three studies reported short-term evaluations, whereas one study included a 26-week follow-up. Medications provided benefits, with good tolerability. Preliminary results suggested greater benefits of paroxetine in reducing panic attacks vs. cognitive - behavioral therapy, and an earlier decrease in PAs with escitalopram vs. citalopram. Risk of bias was considerable. CONCLUSIONS: The pharmacological management of PD in older patients has received no attention. Findings are scant, dated, and affected by methodological flaws; thus, they do not provide significant advances.

Depression, Stress and Anxiety among Women and Men Affected by Recurrent Pregnancy Loss (RPL): A Systematic Review and Meta-Analysis.

The aim of the present study is to perform a systematic review and meta-analysis on depression, stress and anxiety in women who experienced recurrent pregnancy loss (RPL) compared to controls and to men who experienced RPL. The pooled results showed a higher level of moderate/severe depression among women who experienced RPL compared to controls (5359 women, random effects model, odds ratio (OR) 3.77, 95% Confidence Interval (CI) 2.71-5.23, p < 0.00001, I2 0%). Anxiety and stress levels were also higher among women experiencing RPL compared to controls. The pooled results showed a higher level of moderate/severe depression in women who experienced RPL compared to men who underwent the same experience (113/577 (19.5%) women versus 33/446 (7%) men versus random effects model, OR 4.63; 95% CI 2.95-7.25, p < 0.00001 I2 0%). Similarly, higher levels of stress and anxiety in women experiencing RPL compared to men experiencing RPL were described. Women who experienced RPL showed higher rates of moderate-severe depression, stress and anxiety compared to both controls and men who experienced RPL. Healthcare professionals should implement screening for anxiety and depression and social support for both partners and support them in dealing with RPL according to sex-specific responses to this stressful event.

Balance Rehabilitation with Peripheral Visual Stimulation in Patients with Panic Disorder and Agoraphobia: An Open-Pilot Intervention Study.

Given the involvement of balance system abnormalities in the pathophysiology of panic disorder and agoraphobia (PD-AG), we evaluated initial evidence for feasibility, acceptability, and potential clinical usefulness of 10 sessions of balance rehabilitation with peripheral visual stimulation (BR-PVS) in an open-pilot 5-week intervention study including six outpatients with PD-AG who presented residual agoraphobia after selective serotonin reuptake inhibitor (SSRI) treatment and cognitive-behavioral therapy, dizziness in daily life, and peripheral visual hypersensitivity measured by posturography. Before and after BR-PVS, patients underwent posturography, otovestibular examination (no patients presented peripheral vestibular abnormalities), and panic-agoraphobic symptom and dizziness evaluation with psychometric tools. After BR-PVS, four patients achieved postural control normalization measured by posturography, and one patient exhibited a favorable trend of improvement. Overall, panic-agoraphobic symptoms and dizziness decreased, even though to a lesser extent in one patient who had not completed the rehabilitation sessions. The study presented reasonable levels of feasibility and acceptability. These findings suggest that balance evaluation should be considered in patients with PD-AGO presenting residual agoraphobia and that BR-PVS might be an adjunctive therapeutic option worth being tested in larger randomized controlled studies.

Cardiorespiratory Assessments in Panic Disorder Facilitated by Wearable Devices: A Systematic Review and Brief Comparison of the Wearable Zephyr BioPatch with the Quark-b2 Stationary Testing System.

Abnormalities in cardiorespiratory measurements have repeatedly been found in patients with panic disorder (PD) during laboratory-based assessments. However, recordings performed outside laboratory settings are required to test the ecological validity of these findings. Wearable devices, such as sensor-imbedded garments, biopatches, and smartwatches, are promising tools for this purpose. We systematically reviewed the evidence for wearables-based cardiorespiratory assessments in PD by searching for publications on the PubMed, PsycINFO, and Embase databases, from inception to 30 July 2022. After the screening of two-hundred and twenty records, eight studies were included. The limited number of available studies and critical aspects related to the uncertain reliability of wearables-based assessments, especially concerning respiration, prevented us from drawing conclusions about the cardiorespiratory function of patients with PD in daily life. We also present preliminary data on a pilot study conducted on volunteers at the Villa San Benedetto Menni Hospital for evaluating the accuracy of heart rate (HR) and breathing rate (BR) measurements by the wearable Zephyr BioPatch compared with the Quark-b2 stationary testing system. Our exploratory results suggested possible BR and HR misestimation by the wearable Zephyr BioPatch compared with the Quark-b2 system. Challenges of wearables-based cardiorespiratory assessment and possible solutions to improve their reliability and optimize their significant potential for the study of PD pathophysiology are presented.

High prevalence of major depression in US sleep clinics: the need for routine depression screening in sleep services.

Depression screening is not part of routine clinical practice in US sleep clinics. Our study aimed to report the prevalence of depression among individuals referred to US sleep clinics. According to our findings, approximately 21% of patients had depression, with about 4% reporting severe symptoms, 9% had frequent death and/or self-harming thoughts, and 61% were taking antidepressants. Our results highlighted a considerable risk of prevalent depression in sleep clinics and supported the limited existing data on this topic. Our study advocates for the need for routine depression screening in sleep services to reduce the detrimental consequences of a delayed depression diagnosis and the risk of a worse prognosis for both depression and sleep-wake disorders. CITATION: Daccò S, Caldirola D, Grassi M, Alciati A, Perna G, Defillo A. High prevalence of major depression in US sleep clinics: the need for routine depression screening in sleep services. J Clin Sleep Med. 2023;19(4):835-836.

World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for treatment of anxiety, obsessive-compulsive and posttraumatic stress disorders - Version 3. Part I: Anxiety disorders.

AIM: This is the third version of the guideline of the World Federation of Societies of Biological Psychiatry (WFSBP) Task Force for the Pharmacological Treatment of Anxiety, Obsessive-Compulsive and Posttraumatic Stress Disorders (published in 2002, revised in 2008). METHOD: A consensus panel of 33 international experts representing 22 countries developed recommendations based on efficacy and acceptability of available treatments. In total, 1007 RCTs for the treatment of these disorders in adults, adolescents, and children with medications, psychotherapy and other non-pharmacological interventions were evaluated, applying the same rigorous methods that are standard for the assessment of medications. RESULT: This paper, Part I, contains recommendations for the treatment of panic disorder/agoraphobia (PDA), generalised anxiety disorder (GAD), social anxiety disorder (SAD), specific phobias, mixed anxiety disorders in children and adolescents, separation anxiety and selective mutism. Selective serotonin reuptake inhibitors (SSRI) and serotonin-norepinephrine reuptake inhibitors (SNRIs) are first-line medications. Cognitive behavioural therapy (CBT) is the first-line psychotherapy for anxiety disorders. The expert panel also made recommendations for patients not responding to standard treatments and recommendations against interventions with insufficient evidence. CONCLUSION: It is the goal of this initiative to provide treatment guidance for these disorders that has validity throughout the world.

World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for treatment of anxiety, obsessive-compulsive and posttraumatic stress disorders - Version 3. Part II: OCD and PTSD.

AIM: This is the third version of the guideline of the World Federation of Societies of Biological Psychiatry (WFSBP) Task Force for the Pharmacological Treatment of Anxiety, Obsessive-Compulsive and Posttraumatic Stress Disorders which was published in 2002 and revised in 2008. METHOD: A consensus panel of 34 international experts representing 22 countries developed recommendations based on efficacy and acceptability of the treatments. In this version, not only medications but also psychotherapies and other non-pharmacological interventions were evaluated, applying the same rigorous methods that are standard for the assessment of medication treatments. RESULT: The present paper (Part II) contains recommendations based on published randomised controlled trials (RCTs) for the treatment of OCD (n = 291) and PTSD (n = 234) in children, adolescents, and adults. The accompanying paper (Part I) contains the recommendations for the treatment of anxiety disorders.For OCD, first-line treatments are selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioural therapy (CBT). Internet-CBT was also superior to active controls. Several second-line medications are available, including clomipramine. For treatment-resistant cases, several options are available, including augmentation of SSRI treatment with antipsychotics and other drugs.Other non-pharmacological treatments, including repetitive transcranial magnetic stimulation (rTMS), deep brain stimulation (DBS) and others were also evaluated.For PTSD, SSRIs and the SNRI venlafaxine are first-line treatments. CBT is the psychotherapy modality with the best body of evidence. For treatment-unresponsive patients, augmentation of SSRI treatment with antipsychotics may be an option. CONCLUSION: OCD and PTSD can be effectively treated with CBT and medications.

Human-Induced Pluripotent Stem Cell Technology: Toward the Future of Personalized Psychiatry.

The polygenic and multifactorial nature of many psychiatric disorders has hampered implementation of the personalized medicine approach in clinical practice. However, induced pluripotent stem cell (iPSC) technology has emerged as an innovative tool for patient-specific disease modeling to expand the pathophysiology knowledge and treatment perspectives in the last decade. Current technologies enable adult human somatic cell reprogramming into iPSCs to generate neural cells and direct neural cell conversion to model organisms that exhibit phenotypes close to human diseases, thereby effectively representing relevant aspects of neuropsychiatric disorders. In this regard, iPSCs reflect patient pathophysiology and pharmacological responsiveness, particularly when cultured under conditions that emulate spatial tissue organization in brain organoids. Recently, the application of iPSCs has been frequently associated with gene editing that targets the disease-causing gene to deepen the illness pathophysiology and to conduct drug screening. Moreover, gene editing has provided a unique opportunity to repair the putative causative genetic lesions in patient-derived cells. Here, we review the use of iPSC technology to model and potentially treat neuropsychiatric disorders by illustrating the key studies on a series of mental disorders, including schizophrenia, major depressive disorder, bipolar disorder, and autism spectrum disorder. Future perspectives will involve the development of organ-on-a-chip platforms that control the microenvironmental conditions so as to reflect individual pathophysiological by adjusting physiochemical parameters according to personal health data. This strategy could open new ways by which to build a disease model that considers individual variability and tailors personalized treatments.

No Sex Differences in Self-Reported Childhood Maltreatment in Major Depressive and Bipolar Disorders: A Retrospective Study.

BACKGROUND: We investigated, for the first time, whether there are any sex differences in retrospective self-reported childhood maltreatment (CM) in Italian adult patients with major depressive disorder (MDD) or bipolar disorder (BD). Furthermore, the potential impacts of patients' age on the CM self-report were investigated. METHODS: This retrospective study used the data documented in the electronic medical records of patients who were hospitalized for a 4-week psychiatric rehabilitation program. CM was assessed using the 28-item Childhood Trauma Questionnaire (CTQ), which evaluates emotional, physical, and sexual abuse, as well as emotional and physical neglect. The linear and logistic regression models were used (α = 0.01). RESULTS: Three hundred thirty-five patients with MDD (255 women and 80 men) and 168 with BD (97 women and 71 men) were included. In both samples, considerable CM rates were identified, but no statistically significant sex differences were detected in the variety of CTQ-based CM aspects. There was a significant association, with no sex differences, between increasing patients' age and a decreasing burden of CM. CONCLUSION: Both women and men with MDD or BD experienced a similar and considerable CM burden. Our findings support routine CM assessment in psychiatric clinical practice.

First-onset major depression during the COVID-19 pandemic: A predictive machine learning model.

BACKGROUND: This study longitudinally evaluated first-onset major depression rates during the pandemic in Italian adults without any current clinician-diagnosed psychiatric disorder and created a predictive machine learning model (MLM) to evaluate subsequent independent samples. METHODS: An online, self-reported survey was released during two pandemic periods (May to June and September to October 2020). Provisional diagnoses of major depressive disorder (PMDD) were determined using a diagnostic algorithm based on the DSM criteria of the Patient Health Questionnaire-9 to maximize specificity. Gradient-boosted decision trees and the SHapley Additive exPlanations technique created the MLM and estimated each variable's predictive contribution. RESULTS: There were 3532 participants in the study. The final sample included 633 participants in the first wave (FW) survey and 290 in the second (SW). First-onset PMDD was found in 7.4% of FW participants and 7.2% of the SW. The final MLM, trained on the FW, displayed a sensitivity of 76.5% and a specificity of 77.8% when tested on the SW. The main factors identified in the MLM were low resilience, being an undergraduate student, being stressed by pandemic-related conditions, and low satisfaction with usual sleep before the pandemic and support from relatives. Current smoking and taking medication for medical conditions also contributed, albeit to a lesser extent. LIMITATIONS: Small sample size; self-report assessment; data covering 2020 only. CONCLUSIONS: Rates of first-onset PMDD among Italians during the first phases of the pandemic were considerable. Our MLM displayed a good predictive performance, suggesting potential goals for depression-preventive interventions during public health crises.

Predicting New-Onset Psychiatric Disorders Throughout the COVID-19 Pandemic: A Machine Learning Approach.

OBJECTIVE: The investigators estimated new-onset psychiatric disorders (PsyDs) throughout the COVID-19 pandemic in Italian adults without preexisting PsyDs and developed a machine learning (ML) model predictive of at least one new-onset PsyD in subsequent independent samples. METHODS: Data were from the first (May 18-June 20, 2020) and second (September 15-October 20, 2020) waves of an ongoing longitudinal study, based on a self-reported online survey. Provisional diagnoses of PsyDs (PPsyDs) were assessed via DSM-based screening tools to maximize assessment specificity. Gradient-boosted decision trees as an ML modeling technique and the SHapley Additive exPlanations technique were applied to identify each variable's contribution to the model. RESULTS: From the original sample of 3,532 participants, the final sample included 500 participants in the first wave and 236 in the second. Some 16.0% of first-wave participants and 18.6% of second-wave participants met criteria for at least one new-onset PPsyD. The final best ML predictive model, trained on the first wave, displayed a sensitivity of 70% and a specificity of 73% when tested on the second wave. The following variables made the largest contributions: low resilience, being an undergraduate student, and being stressed by pandemic-related conditions. Living alone and having ceased physical activity contributed to a lesser extent. CONCLUSIONS: Substantial rates of new-onset PPsyDs emerged among Italians throughout the pandemic, and the ML model exhibited moderate predictive performance. Results highlight modifiable vulnerability factors that are suitable for targeting by public campaigns or interventions to mitigate the pandemic's detrimental effects on mental health.

Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning.

INTRODUCTION: The course of OCD differs widely among OCD patients, varying from chronic symptoms to full remission. No tools for individual prediction of OCD remission are currently available. This study aimed to develop a machine learning algorithm to predict OCD remission after two years, using solely predictors easily accessible in the daily clinical routine. METHODS: Subjects were recruited in a longitudinal multi-center study (NOCDA). Gradient boosted decision trees were used as supervised machine learning technique. The training of the algorithm was performed with 227 predictors and 213 observations collected in a single clinical center. Hyper-parameter optimization was performed with cross-validation and a Bayesian optimization strategy. The predictive performance of the algorithm was subsequently tested in an independent sample of 215 observations collected in five different centers. Between-center differences were investigated with a bootstrap resampling approach. RESULTS: The average predictive performance of the algorithm in the test centers resulted in an AUROC of 0.7820, a sensitivity of 73.42%, and a specificity of 71.45%. Results also showed a significant between-center variation in the predictive performance. The most important predictors resulted related to OCD severity, OCD chronic course, use of psychotropic medications, and better global functioning. LIMITATIONS: All recruiting centers followed the same assessment protocol and are in The Netherlands. Moreover, the sample of the data recruited in some of the test centers was limited in size. DISCUSSION: The algorithm demonstrated a moderate average predictive performance, and future studies will focus on increasing the stability of the predictive performance across clinical settings.

Elevated C-reactive protein levels across diagnoses: The first comparison among inpatients with major depressive disorder, bipolar disorder, or obsessive-compulsive disorder.

OBJECTIVE: We addressed elevated C-reactive protein level (eCRP) specificity comparing, for the first time, eCRP (i.e., serum CRP > 3 and ≤10 mg/L) in patients with major depressive disorder (MDD), bipolar disorder (BD), or obsessive-compulsive disorder (OCD). We also assessed to what extent multiple variables that can potentially increase inflammation may have influenced eCRP in our sample. METHODS: We performed a retrospective, observational, cross-sectional study using information documented in the electronic medical records (EMRs) of patients hospitalized for a 4-week psychiatric rehabilitation program. We collected all information according to the standardized procedures of the hospital's clinical practice and applied a logistic regression model (α = 0.05). RESULTS: We included 388 inpatients, that is, 156 (40.2%) with MDD, 135 (34.8%) with BD, and 97 (25.0%) with OCD, and found considerable eCRP rates among them (36.5%, 47.4%, and 29.9% in MDD, BD, and OCD, respectively) but without significant differences across groups. In the whole sample, eCRP variations were only partially attributable (approximately for one-third) to potential confounders. All groups presented considerable rates of cardiovascular risk factors, and we classified most patients as having medium or high CRP-based cardiovascular risk. CONCLUSION: This first study comparing eCRP in MDD, BD, and OCD suggests that eCRP may be a transdiagnostic feature of different psychiatric disorders, and other mechanisms beyond the effects of multiple confounders may explain the presence of eCRP in a substantial portion of psychiatric patients. Therefore, we encourage the routine measurement of CRP in psychiatric clinical practice.

Oxidative Stress in the Early Stage of Psychosis.

BACKGROUND: In the past few decades, increasing evidence in the literature has appeared describing the role of the antioxidant defense system and redox signaling in the multifactorial pathophysiology of psychosis. It is of interest to clinicians and researchers alike that abnormalities of the antioxidant defense system are associated with alterations of cellular membranes, immune functions and neurotransmission, all of which have some clinical implications. METHODS: This narrative review summarizes the evidence regarding oxidative stress in the early stages of psychosis. We included 136 peer-reviewed articles published from 2007 to 2020 on PubMed EMBASE, The Cochrane Library and Google Scholar. RESULTS: Patients affected by psychotic disorders show a decreased level of non-enzymatic antioxidants, an increased level of lipid peroxides, nitric oxides, and a homeostatic imbalance of purine catabolism. In particular, a significantly reduced antioxidant defense has been described in the early onset first episode of psychosis, including reduced levels of glutathione. Also, it has been shown that a decreased basal low-antioxidant capacity correlates with cognitive deficits and negative symptoms, mostly related to glutamate-receptor hypofunction. In addition, atypical antipsychotic drugs seem to show significant antioxidant activity. These factors are critical in order to treat cases of first-onset psychosis effectively. CONCLUSION: This systematic review indicates the importance that must be given to anti-oxidant defense systems.

Suicide and Genetic Biomarkers: Toward Personalized Tailored-treatment with Lithium and Clozapine.

BACKGROUND: Suicide is a major public health problem on a global scale, with about 800.000 deaths every year. In particular, it represents one of the main causes of death among adolescents and young adults aged between 15 and 29 years. The World Health Organization (WHO) describes suicide as "an act of deliberate killing" and that is placed at the extreme end of the continuous spectrum of suicidal behaviors (SBs). These include suicidal ideation, attempted suicide and suicide itself. OBJECTIVE: The aim of the present review was to better clarify the suicide vulnerability genetic biomarkers and genetic variants correlated with the response to lithium and clozapine and to evaluate some correspondences. METHODS: We reviewed the current literature, focusing our attention on genetic molecular studies about neurobiological systems involved in SBs and pharmacogenetic studies about antisuicidal drugs (lithium and clozapine). RESULTS: The studies that we have reviewed have shown mixed results. Interestingly, rs1800532 polymorphism of the SLC6A4 gene, encoding for the serotonin transporter, is potentially correlated with both suicide vulnerability and a poor response to lithium and clozapine. CONCLUSION: Due to the impact of suicide on public health, more studies are needed to open a promising route to prevent suicide in personalized and precise psychiatry.

Experimental Drugs for Panic Disorder: An Updated Systematic Review.

Several effective pharmacological therapies for panic disorder (PD) are available, but they have some drawbacks, and unsatisfactory outcomes can occur. Expanding the variety of anti-panic medications may allow for improving PD treatment. The authors performed an updated systematic review of preclinical and clinical (Phase I-III) pharmacological studies to look for advances made in the last six years concerning novel-mechanism-based anti-panic compounds or using medications approved for nonpsychiatric medical conditions to treat PD. The study included seven published articles presenting a series of preclinical studies, two Phase I clinical studies with orexin receptor (OXR) antagonists, and two clinical studies investigating the effects of D-cycloserine (DCS) and xenon gas in individuals with PD. The latest preclinical findings confirmed and expanded previous promising indications of OXR1 antagonists as novel-mechanism-based anti-panic compounds. Translating preclinical research into clinical applications remains in the early stages. However, limited clinical findings suggested the selective OXR1 antagonist JNJ-61393115 may exert anti-panic effects in humans. Overall, OXR1 antagonists displayed a favorable profile of short-term safety and tolerability. Very preliminary suggestions of possible anti-panic effects of xenon gas emerged but need confirmation with more rigorous methodology. DCS did not seem promising as an enhancer of cognitive-behavioral therapy in PD. Future studies, including objective panic-related physiological parameters, such as respiratory measures, and expanding the use of panic vulnerability biomarkers, such as hypersensitivity to CO2 panic provocation, may allow for more reliable conclusions about the anti-panic properties of new compounds.