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.

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.

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.

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.

Childhood maltreatment history for guiding personalized antidepressant choice in major depressive disorder: Preliminary results from a systematic review.

Childhood maltreatment (CM) is a predictor of poor outcome across treatments for major depressive disorder (MDD), while its potential role as a predictor of differential responses to specific antidepressants has received little attention. The present systematic review examined pharmacological studies (published up to June 30th, 2020) that included head-to-head comparisons of antidepressant treatments among adult MDD patients with a reported history of CM or no history to evaluate if CM may help clinicians choose antidepressants with greatest likelihood of successful outcome. Only three studies were included, providing limited and provisional results. These preliminary findings suggest that sustained-release bupropion (alone or in combination) or aripiprazole-augmentation as next-step intervention did not demonstrate differential outcome among MDD patients with or without a history of childhood adversity. Further, sertraline and the group of antidepressants with low affinity for the serotonin transporter may be less suitable for MDD patients with childhood abuse history than escitalopram, venlafaxine-XR, or antidepressants with high affinity for the serotonin transporter. The critical question of the most potentially efficacious treatment regimens for adult MDD with CM history requires further large-sample studies involving a greater number of medications, specifically designed to analyse the moderating effects of different types of CM, and possibly including objective biomarkers.

The Co-Morbidity between Bipolar and Panic Disorder in Fibromyalgia Syndrome.

About half of the patients with fibromyalgia (FM) had a lifetime major depression episode and one third had a panic disorder (PD). Because the co-morbidity between bipolar disorder (BD) and PD marks a specific subtype of BD we aimed to investigate if co-morbid BD/PD (comBD/PD) occurs more frequently than the single disorder in FM patients and evaluate the clinical significance and timing of this co-morbidity. Further, we explored the role of co-morbid subthreshold BD and PD. In 118 patients with FM, lifetime threshold and sub-threshold mood disorders and PD were diagnosed with Diagnostic and Statistical Manual of Mental Disorders-IV-Text Revision (DSM-IV-TR) Clinical Interview. Demographic and clinical variables were compared in co-morbid BD/PD (comBD/PD) and not co-morbid BD/PD (nocomBD/PD) subgroups. The co-morbidity BD/PD was seen in 46.6% of FM patients and in 68.6% when patients with minor bipolar (MinBD) and sub-threshold panic were included. These rates are higher than those of the general population and BD outpatients. There were no statistically significant differences between threshold and sub-threshold comBD/PD and nocom-BD/PD subgroups in demographic and clinical parameters. In the majority of patients (78.2%), the onset of comBD/PD preceded or was contemporary with FM. These findings support the hypothesis that comBD/PD is related to the development of FM in a subgroup of patients.

Impact of respiratory protective devices on respiration: Implications for panic vulnerability during the COVID-19 pandemic.

BACKGROUND: The wearing of respiratory protective devices (RPDs) correctly and continually in situations where people are at risk of respiratory infections is crucial for infection prevention. Certain people are poorly compliant with RPDs due to RPD-related annoyance, including respiratory discomfort. We hypothesized that individuals vulnerable to panic attacks are included in this group. No published studies on this topic are available. The evidence for our hypothesis was reviewed in this study as a starting point for future research. METHODS: We selected a set of experimental studies that measured the respiratory physiological burden in RPD wearers through objective and validated methods. We conducted a bibliographic search of publications in the PubMed database (January 2000-May 2020) to identify representative studies that may be of interest for panic respiratory pathophysiology. RESULTS: Five studies were included. Wearing RPDs exerted significant respiratory effects, including increased breathing resistance, CO2 rebreathing due to CO2 accumulation in the RPD cavity, and decreased inhaled O2 concentration. We discussed the implications of these effects on the respiratory pathophysiology of panic. LIMITATIONS: Most studies had a small sample size, with a preponderance of young participants. Different methodologies were used across the studies. Furthermore, differences in physical responses between wearing RPDs in experimental settings or daily life cannot be excluded. CONCLUSIONS: This research supports the idea that panic-prone individuals may be at higher risk of respiratory discomfort when wearing RPDs, thereby reducing their tolerance for these devices. Strategies to decrease discomfort should be identified to overcome the risk of poor compliance.

'Precision' or 'personalized' psychiatry: different terms - same content?

Due to the increased lifetime prevalence and personal, social, and economic burden of mental disorders, psychiatry is in need of a significant change in several aspects of its clinical and research approaches. Over the last few decades, the development of personalized / precision medicine in psychiatry focusing on tailored therapies that fit each patient's unique individual, physiological, and genetic profile has not achieved the same results as those obtained in other branches, such as oncology. The long-awaited revolution has not yet surfaced. There are various explanations for this including imprecise diagnostic criteria, incomplete understanding of the molecular pathology involved, absence of available clinical tools and, finally, the characteristics of the patient. Since then, the co-existence of the two terms has sparked a great deal of discussion around the definition and differentiation between the two types of psychiatry, as they often seem similar or even superimposable. Generally, the two terminologies are used indiscriminately, alternatively, and / or separately, within the same scientific works. In this paper, an overview is provided on the overlap between the application and meaning of the terms 'precision psychiatry' and 'personalized psychiatry'.

Relapse prevention in panic disorder with pharmacotherapy: where are we now?

Introduction: A substantial number of patients with PD experience relapse after the discontinuation of effective pharmacotherapy, leading to detrimental effects on the individuals and considerable societal costs. This suggests the need to optimize pharmacotherapy to minimize relapse risk. Area covered: The present systematic review examines randomized, double-blind, placebo-controlled relapse prevention studies published over the last 20 years involving recommended medications. The authors aim to provide an overview of this topic and evaluate whether recent advances were achieved. Only seven studies were included, providing limited results. One-year maintenance pharmacotherapy with constant doses had protective effects against relapse in patients who had previously exhibited satisfactory responses to the same medication at the same doses. The duration of maintenance treatment did not influence relapse risk. No data were available concerning the use of lower doses or the predictors of relapse. Expert opinion: Relapse prevention in PD has received limited attention. Recent progress and conclusive indications are lacking. Rethinking pharmacological research in PD may be productive. Collecting a wide range of clinical and individual features/biomarkers in large-scale, multicenter long-term naturalistic studies, and implementing recent technological innovations (e.g., electronic medical records/'big data' platforms, wearable devices, and machine learning techniques) may help identify reliable predictive models.