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Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.
Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.
Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.
All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.
The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
While taxonomy segregates anxiety symptoms into diagnoses, patients typically present with multiple diagnoses; this poses major challenges, particularly for youth, where mixed presentation is particularly common. Anxiety comorbidity could reflect multivariate, cross-domain interactions insufficiently emphasized in current taxonomy. We utilize network analytic approaches that model these interactions by characterizing pediatric anxiety as involving distinct, inter-connected, symptom domains. Quantifying this network structure could inform views of pediatric anxiety that shape clinical practice and research.
Participants were 4964 youths (ages 5–17 years) from seven international sites. Participants completed standard symptom inventory assessing severity along distinct domains that follow pediatric anxiety diagnostic categories. We first applied network analytic tools to quantify the anxiety domain network structure. We then examined whether variation in the network structure related to age (3-year longitudinal assessments) and sex, key moderators of pediatric anxiety expression.
The anxiety network featured a highly inter-connected structure; all domains correlated positively but to varying degrees. Anxiety patients and healthy youth differed in severity but demonstrated a comparable network structure. We noted specific sex differences in the network structure; longitudinal data indicated additional structural changes during childhood. Generalized-anxiety and panic symptoms consistently emerged as central domains.
Pediatric anxiety manifests along multiple, inter-connected symptom domains. By quantifying cross-domain associations and related moderation effects, the current study might shape views on the diagnosis, treatment, and study of pediatric anxiety.
We compared cohorts of raters from different countries who received training on the PANSS. We attempted to determine if there was any consistent by-country impact on specific items, factors, or subscales. We also queried raters about their perceptions of the instrument they were asked to use vis-à-vis their local patient population.
The data set comes from standardized rater training events involving raters from four countries: India (n = 83), Russia (n = 59), the US (n = 63), and Romania (n = 76). Raters scored interviews of schizophrenic patients using the PANSS. Scores were compared and intra-class correlation coefficients (ICCs) and rater agreement with “gold standard” scores were evaluated. The results were viewed against raters’ responses to questions about how well the PANSS items correlated to the presentation of symptoms.
Raters from the US and Russia demonstrated a higher level of inter-rater consistency with ICCs of 0.883 and 0.835, respectively. For eight PANSS items, all raters demonstrated at least 80% agreement with the gold standard scores. For ten PANSS items, there was at least one country whose raters scored below 60% agreement. The PANSS items with the lower inter-rater reliability were the same items raters indicated as problematic in local settings.
The differences in rater performance indicate that standardized rater training is broadly effective but that there are some important differences in the way in which different groups conceptualize symptomatology and corresponding PANSS items. This suggests a need to tailor training to ensure reliability and validity in the use of this instrument.
Though rater drift in clinical trials has long been understood to negatively impact trial results, few studies have systematically quantified this. We examined training data for the HAM-D (Hamilton Depression Scale, 17-item version) at two time points to measure the impact.
Raters participating in a standardized training scored the HAM-D based on two videotaped interviews of depressed patients. To assess drift, data from an initial, post-online training session was compared to data obtained 12 months later. Intra-class correlation coefficients (Shrout & Fliess, 1979) and concordance with expert ratings were compared.
Intra-class correlation coefficients (ICC) for raters (n = 167) following initial training were good to excellent for individual raters (.695–.976, p < .0001) and good for the overall cohort (.752, p < .0001). Concordance with expert ratings was excellent at 99.3%. The overall ICC fell to .730 at the second assessment and although the upper bound of individual performance remained in the good to excellent range, the frequency of scores in the poor to fair range (< .65) increased. Concordance also fell slightly to 87%.
Rater drift occurred over 12 months, as gauged by the metrics of reliability and concordance. Drift was apparent in a limited portion of the cohort but resulted in a lower overall ICC at the second time point. Because studies are generally powered assuming that the ICC remains stable, there are implications for both this power calculation and the required sample size.
Recent advances in biomarker technology have allowed for the development of highly predictive tests for Alzheimer's disease (AD) when combined with standard psychometric tests. Current research in AD utilizes the ADAS-Cog and/or the MMSE as standard measures; they do not exclusively address the specific deficits expected in an amnesic syndrome of the hippocampal type as express with AD.
Because episodic memory degradation is most strongly predictive of conversion from mild cognitive impairment (MCI) to AD, a clinical measure targeting this deficit is warranted.
To utilize current knowledge of neural correlates of different stages of episodic memory function and their modulation by AD to develop a psychometrically sound instrument.
The authors developed a brief scale that captures registration, storage and retrieval of information along four identified domains of episodic memory in AD. A second stage was to confirm BEMA in institutionalized subjects, and assess reliability and validity.
Preliminary results indicate good test-retest reliability and adequate sensitivity and specificity. the BEMA was positively and significantly correlated with other measures of episodic memory. the [insert scale name or abbreviation] yields a total score, scores for 3 lifetime periods and the duration of episodic memory impairment.
Findings suggest that a richer understanding of the memory deficits in AD can lead to the development of an instrument which taps different aspects of episodic memory function. This scale can aid in the screening, assessment and treatment of early AD and complement the newly developed one-plus-one strategy.
Prognostic models discriminate between groups of individuals likely to experience better or worse outcomes and to predict response to treatment.
The premise of the analysis was the assumption that baseline PANSS measurements could be a prognostic factor to inform decisions on the expected response (completion or early-termination) to treatment during participation in a clinical trial.
To examine early patterns/profiles based on PANSS and response to treatment (Study-Completer (SC), Early-Termination (ET)).
Receiver Operating Curves (ROC) was conducted on 809 subjects with SC versus ET. Factor structure assessed whether psychopathology constructs are comparable across SC and ET.
Positive-Symptoms: P5.Grandiosity, P7.Hostility and P4.Excitement are not as good as others in predicting ET. 91.1% ET would have scores of 5, 6 or 7 on P1.Delusions.
Negative-Symptoms: N5. Difficulty in Abstract Thinking and N6.Lack of Spontaneity and Flow of Conversation are not as good in predicting ET. 67.9% ET may have scores of 5, 6 or 7 on N1.Blunted Affect. General-Psychopathology: G3.Guilt Feelings, G6.Depression, G7.Motor Retardation, and G10.Disorientation are not as good in predicting ET. 73.2% ET have scores of 5, 6 or 7 on G9.Unusual Thought Content. Positive Factor accounted for the most variance 15.885%, then Negative factor=14.592%, then Hostile-Excitement=11.973% for SC. For ET, Negative Factor=13.713% variance, cognitive factor=12.451%, Excitement Factor=10.396%.
These findings represent patterns of early detection of response in clinical trials, and have led to the development of sophisticated algorithms that may allow investigators to identify ET and SC, which is important in trial success.
Evidence has suggested that immune imbalance is involved with bipolar disorder (BD); however, its precise mechanism is poorly understood.
This study investigated whether biochemical changes in the serum from BD patients could modulate the phenotype of macrophages.
Eighteen subjects with BD and healthy individuals (n = 5) were included in this study. The human monocyte cell line U-937 was activated with PMA (phorbol 12-myristate 13-acetate) and polarization was induced with RPMI-1640 media supplemented with 10% serum from each patient for 24 h. Gene expression of selected M1 and M2 markers was assessed by qPCR.
Macrophages exposed to serum of manic and depressive BD patients displayed an increase of IL-1β (6.40 ± 3.47 and 9.04 ± 5.84 versus 0.23 ± 0.11; P < 0.05) and TNF-α (2.23 ± 0.91 and 2.03 ± 0.45 versus 0.62 ± 0.24; P = 0.002 and P = 0.004, respectively) compared to remitted group. In parallel, U-937 macrophages treated with serum of patients in acute episode displayed a down-regulation of CXCL9 (0.29 ± 0.20 versus 1.86 ± 1.61; P = 0.006) and CXCL10 expression (0.36 ± 0.15 and 0.86 ± 0.24 versus 1.83 ± 0.88; P < 0.000 and P = 0.04) compared to remitters.
Our results are consistent with previous studies showing that changes in peripheral blood markers could modulate M1/M2 polarization in BD. The evidence of macrophages as source of inflammatory cytokines might be helpful to unravel how the mononuclear phagocyte system can be involved in the etiology of BD.
Disclosure of interest
The authors have not supplied their declaration of competing interest.
Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.
We estimated seven regularized Mixed Graphical Models in the Netherlands Study of Depression and Anxiety (NESDA) data (N = 2321) to explore shared variances among (1) depression severity, modeled via depression sum-score, nine DSM-5 symptoms, or 28 individual depressive symptoms; (2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); (3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.
The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al., CRP was associated with ‘sleep problems’, ‘energy level’, and ‘weight/appetite changes’; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms ‘insomnia’, ‘hypersomnia’, and ‘aches and pain’ showed unique positive relations to all inflammatory markers.
We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers.
Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node – its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality.
We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes.
Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis.
Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.
Faster eating rates are associated with increased energy intake, but little is known about the relationship between children’s eating rate, food intake and adiposity. We examined whether children who eat faster consume more energy and whether this is associated with higher weight status and adiposity. We hypothesised that eating rate mediates the relationship between child weight and ad libitum energy intake. Children (n 386) from the Growing Up in Singapore Towards Healthy Outcomes cohort participated in a video-recorded ad libitum lunch at 4·5 years to measure acute energy intake. Videos were coded for three eating-behaviours (bites, chews and swallows) to derive a measure of eating rate (g/min). BMI and anthropometric indices of adiposity were measured. A subset of children underwent MRI scanning (n 153) to measure abdominal subcutaneous and visceral adiposity. Children above/below the median eating rate were categorised as slower and faster eaters, and compared across body composition measures. There was a strong positive relationship between eating rate and energy intake (r 0·61, P<0·001) and a positive linear relationship between eating rate and children’s BMI status. Faster eaters consumed 75 % more energy content than slower eating children (Δ548 kJ (Δ131 kcal); 95 % CI 107·6, 154·4, P<0·001), and had higher whole-body (P<0·05) and subcutaneous abdominal adiposity (Δ118·3 cc; 95 % CI 24·0, 212·7, P=0·014). Mediation analysis showed that eating rate mediates the link between child weight and energy intake during a meal (b 13·59; 95 % CI 7·48, 21·83). Children who ate faster had higher energy intake, and this was associated with increased BMI z-score and adiposity.
As a pilot study to investigate whether personalized medicine approaches could have value for the reduction of malaria-related mortality in young children, we evaluated questionnaire and biomarker data collected from the Mother Offspring Malaria Study Project birth cohort (Muheza, Tanzania, 2002–2006) at the time of delivery as potential prognostic markers for pediatric severe malarial anemia. Severe malarial anemia, defined here as a Plasmodium falciparum infection accompanied by hemoglobin levels below 50 g/L, is a key manifestation of life-threatening malaria in high transmission regions. For this study sample, a prediction model incorporating cord blood levels of interleukin-1β provided the strongest discrimination of severe malarial anemia risk with a C-index of 0.77 (95% CI 0.70–0.84), whereas a pragmatic model based on sex, gravidity, transmission season at delivery, and bed net possession yielded a more modest C-index of 0.63 (95% CI 0.54–0.71). Although additional studies, ideally incorporating larger sample sizes and higher event per predictor ratios, are needed to externally validate these prediction models, the findings provide proof of concept that risk score-based screening programs could be developed to avert severe malaria cases in early childhood.
Researchers have studied psychological disorders extensively from a common cause perspective, in which symptoms are treated as independent indicators of an underlying disease. In contrast, the causal systems perspective seeks to understand the importance of individual symptoms and symptom-to-symptom relationships. In the current study, we used network analysis to examine the relationships between and among depression and anxiety symptoms from the causal systems perspective.
We utilized data from a large psychiatric sample at admission and discharge from a partial hospital program (N = 1029, mean treatment duration = 8 days). We investigated features of the depression/anxiety network including topology, network centrality, stability of the network at admission and discharge, as well as change in the network over the course of treatment.
Individual symptoms of depression and anxiety were more related to other symptoms within each disorder than to symptoms between disorders. Sad mood and worry were among the most central symptoms in the network. The network structure was stable both at admission and between admission and discharge, although the overall strength of symptom relationships increased as symptom severity decreased over the course of treatment.
Examining depression and anxiety symptoms as dynamic systems may provide novel insights into the maintenance of these mental health problems.
For diagnostic purposes, the nine symptoms that compose the DSM-5 criteria for major depressive disorder (MDD) are assumed to be interchangeable indicators of one underlying disorder, implying that they should all have similar risk factors. The present study investigates this hypothesis, using a population cohort that shifts from low to elevated depression levels.
We assessed the nine DSM-5 MDD criterion symptoms (using the Patient Health Questionnaire; PHQ-9) and seven depression risk factors (personal and family MDD history, sex, childhood stress, neuroticism, work hours, and stressful life events) in a longitudinal study of medical interns prior to and throughout internship (n = 1289). We tested whether risk factors varied across symptoms, and whether a latent disease model could account for heterogeneity between symptoms.
All MDD symptoms increased significantly during residency training. Four risk factors predicted increases in unique subsets of PHQ-9 symptoms over time (depression history, childhood stress, sex, and stressful life events), whereas neuroticism and work hours predicted increases in all symptoms, albeit to varying magnitudes. MDD family history did not predict increases in any symptom. The strong heterogeneity of associations persisted after controlling for a latent depression factor.
The influence of risk factors varies substantially across DSM depression criterion symptoms. As symptoms are etiologically heterogeneous, considering individual symptoms in addition to depression diagnosis might offer important insights obfuscated by symptom sum scores.
To explore how hand hygiene observer scheduling influences the number of events and unique individuals observed.
We deployed a mobile sensor network to capture detailed movement data for 6 categories of healthcare workers over a 2-week period.
University of Iowa Hospital and Clinic medical intensive care unit (ICU).
We recorded 33,721 time-stamped healthcare worker entries to and exits from patient rooms and considered each entry or exit to be an opportunity for hand hygiene. Architectural drawings were used to derive 4 optimal line-of-sight placements for observers. We ran simulations for different observer movement schedules, all with a budget of 1 hour of total observation time. We considered observation times of 1–15, 15–30, 30, and 60 minutes per station. We stochastically generated healthcare worker hand hygiene compliance on the basis of all data and recorded the total unit compliance as it would be reported by each simulated observer.
Considering a 60-minute total observation period, aggregate simulated observers captured 1.7% of the average total number of opportunities per day at best and 0.5% at worst. The 1–15-minute schedule captures, on average, 16% fewer events than does the 60-minute (ie, static) schedule, but it samples 17% more unique individuals. The 1–15-minute schedule also provides the best estimator of compliance for the duration of the shift, with a mean standard deviation of 17%, compared with 23% for the 60-minute schedule.
Our results show that observations are sensitive to different observers' schedules and suggest the importance of using data-driven approaches to schedule hand hygiene audits.
We study the dynamics of semigroups of Möbius transformations on the Riemann sphere, especially their Julia sets and attractors. This theory relates to the dynamics of rational functions, rational semigroups, and Möbius groups and we compare and contrast these theories. We particularly examine Caruso’s family of Möbius semigroups, based on a random dynamics variant of the Fibonacci sequence.
This study examined whether participation in a variety of lifestyle activities was comparable to frequent participation in cognitively challenging activities in mitigating impairments in cognitive abilities susceptible to aging in healthy, community-dwelling older women. Frequencies of participation in various lifestyle activities on the Lifestyle Activities Questionnaire (LAQ) were divided according to high (e.g., reading), moderate (e.g., discussing politics), and low (e.g., watching television) cognitive demand. We also considered the utility of participation in a variety of lifestyle activities regardless of cognitive challenge. Immediate and delayed verbal recall, psychomotor speed, and executive function were each measured at baseline and at five successive exams, spanning a 9.5-year interval. Greater variety of participation in activities, regardless of cognitive challenge, was associated with an 8 to 11% reduction in the risk of impairment in verbal memory and global cognitive outcomes. Participation in a variety of lifestyle activities was more predictive than frequency or level of cognitive challenge for significant reductions in risk of incident impairment on measures sensitive to cognitive aging and risk for dementia. Our findings offer new perspectives in promoting a diverse repertoire of activities to mitigate age-related cognitive declines. (JINS, 2012, 18, 286–294)
Réus GZ, Stringari RB, Ribeiro KF, Luft T, Abelaira HM, Fries GR, Aguiar BW, Kapczinski F, Hallak JE, Zuardi AW, Crippa JA, Quevedo J. Administration of cannabidiol and imipramine induces antidepressant-like effects in the forced swimming test and increases brain-derived neurotrophic factor levels in the rat amygdala.
Objective: Cannabidiol is a chemical constituent from Cannabis sativa and it has multiple mechanisms of action, including antidepressant effects. The main objective of the present study was to evaluate behavioural and molecular effects induced by administration of cannabidiol and imipramine in rats.
Methods: In the present study, rats were acutely or chronically treated for 14 days once a day with saline, cannabidiol (15, 30 and 60 mg/kg) or imipramine (30 mg/kg) and the animals behaviour was assessed in forced swimming and open-field tests. Afterwards, the prefrontal cortex, hippocampus and amygdala brain-derived neurotrophic factor (BDNF) levels were assessed by enzyme-linked immunosorbent sandwich assay.
Results: We observed that both acute and chronic treatments with imipramine at the dose of 30 mg/kg and cannabidiol at the dose of 30 mg/kg reduced immobility time and increased swimming time; climbing time was increased only with imipramine at the dose of 30 mg/kg, without affecting locomotor activity. In addition, chronic treatment with cannabidiol at the dose of 15 mg/kg and imipramine at the dose of 30 mg/kg increased BDNF levels in the rat amygdala.
Conclusion: In conclusion, our results indicate that cannabidiol has an antidepressant-like profile and could be a new pharmacological target for the treatment of major depression.