Evidence That Environmental and Genetic Risks for Psychotic Disorder May Operate by Impacting on Connections Between Core Symptoms of Perceptual Alteration and Delusional Ideation

Background: Relational models of psychopathology propose that symptoms are dynamically connected and hypothesize that genetic and environmental influences moderate the strength of these symptom connections. Previous findings suggest that the interplay between hallucinations and delusions may play a crucial role in the development of psychotic disorder. The current study examined whether the connection between hallucinations and delusions is impacted by proxy genetic and environmental risk factors. Methods: Hallucinations and delusions at baseline and at 3-year follow-up were assessed in a sample of 1054 healthy siblings and 918 parents of 1109 patients with psychosis, and in 589 healthy controls (no familial psychosis risk). Environmental factors assessed were cannabis use, childhood trauma, and urbanicity during childhood. Logistic regression analyses tested whether familial psychosis risk predicted increased risk of delusions, given presence of hallucinations. Moderating effects of environmental factors on the hallucination-delusion association were tested in a similar fashion, restricted to the control and sibling groups. Results: The risk of delusions, given hallucinations, was associated with proxy genetic risk: 53% in parents, 47% in siblings, and 36% in controls. The hallucination-delusion association was stronger in those reporting cannabis use (risk difference: 32%) and childhood trauma (risk difference: 15%) although not all associations were statistically conclusive (respectively: p = .037; p = .054). A directionally similar but nonsignificant effect was found for urb anicity during childhood (risk difference: 14%, p = .357). Conclusion: The strength of the connection between delusions and hallucinations is associated with familial and environmental risks for psychotic disorder, suggesting that specific symptom connections in the early psychosis psychopathology network are informative of underlying mechanisms

The resource group method in severe mental illness:Study protocol for a randomized controlled trial and a qualitative multiple case study

Background The resource group method provides a structure to facilitate patients’ empowerment and recovery processes, and to systematically engage significant others in treatment and care. A patient chooses members of a resource group (RG) that will work together on fulfilling patients’ recovery plan. By adopting shared decision-making processes and stimulating collaboration of different support systems, a broad and continuous support of patients’ chosen goals and wishes is preserved and problem solving and communication skills of the RG members are addressed. Objective The objectives of this study are (1) to establish the effectiveness of the RG method in increasing empowerment in patients with severe mental illnesses (SMI) in the Netherlands; (2) to investigate the cost-effectiveness and cost utility of the RG method; and (3) to qualitatively explore its dynamics and processes. Methods/design This multisite randomized controlled trial will compare the effects of the RG-method integrated in Flexible Assertive Community Treatment (FACT) (90 patients) with those of standard FACT (90 patients). Baseline assessments and 9-month and 18-month follow-up assessments will be conducted in face-to-face home visits. The primary outcome measure, empowerment, will be assessed using the Netherlands Empowerment List (NEL). The secondary outcomes will be quality of life (MANSA); personal, community and clinical recovery (I.ROC); general, social and community functioning (WHODAS 2.0); general psychopathological signs and symptoms (BSI-18); and societal costs (TiC-P). An economic evaluation of the cost-effectiveness and cost utility of the RG method will also be conducted. A qualitative multiple case-study will be added to collect patients’, RG members’ and professionals’ perspectives by in-depth interviews, observations and focus groups. Discussion This trial will be the first to study the effects of the RG method on empowerment in patients with SMI. By combining clinical-effectiveness data with an economic evaluation and in-depth qualitative information from primary stakeholders, it will provide a detailed overview of the RG method as a mean of improving care for patients with SMI

Does monitoring need for care in patients diagnosed with severe mental illness impact on Psychiatric Service Use? Comparison of monitored patients with matched controls

Background: Effectiveness of services for patients diagnosed with severe mental illness (SMI) may improve when treatment plans are needs based. A regional Cumulative Needs for Care Monitor (CNCM) introduced diagnostic and evaluative tools, allowing clinicians to explicitly assess patients' needs and negotiate treatment with the patient. We hypothesized that this would change care consumption patterns. Methods: Psychiatric Case Registers (PCR) register all in-patient and out-patient care in the region. We matched patients in the South-Limburg PCR, where CNCM was in place, with patients from the PCR in the North of the Netherlands (NN), where no CNCM was available. Matching was accomplished using propensity scoring including, amongst others, total care consumption and out-patient care consumption. Date of the CNCM assessment was copied to the matched controls as a hypothetical index date had the CNCM been in place in NN. The difference in care consumption after and before this date (after minus before) was analysed. Results: Compared with the control region, out-patient care consumption in the CNCM region was significantly higher after the CNCM index date regardless of treatment status at baseline (new, new episode, persistent), whereas a decrease in in-patient care consumption could not be shown. Conclusions: Monitoring patients may result in different patterns of care by flexibly adjusting level of out-patient care in response to early signs of clinical deterioration

Attachment as a framework to facilitate empowerment for people with severe mental illness

Objectives:  Recovery and empowerment have evolved into key objectives in the treatment and care of people with severe mental illness (SMI), and interest has grown in the role of social relationships in recovery. This study is the first to explore whether attachment styles are related to levels of empowerment, and secondly, whether attachment anxiety and attachment avoidance are associated with lower empowerment levels, independently of quality and frequency of social contact.  Design:  We used a cross-sectional design. Methods: In a sample of 157 participants with SMI in outpatient care, associations between attachment (Revised Adult Attachment Scale), self-reported social functioning, and empowerment (Netherlands Empowerment List) were assessed.  Results:  Attachment anxiety and attachment avoidance were both associated with lower levels of empowerment. A stepwise multiple regression analysis showed that the prediction of empowerment was significantly improved by adding attachment anxiety and attachment avoidance to quality and frequency of social contact. Attachment anxiety, attachment avoidance, and quality of social contact were significant predictors; frequency of social contact was not.  Conclusions:  Although our design does not allow causal conclusions, our results highlight the importance of interpersonal processes and behaviours as routes to improving empowerment for people with SMI. A promising approach might thus consist of securing attachment bonds with significant others so that the self and the other are perceived as reliable resources. Our findings also feature the importance of reciprocity and equality in social relationships. Taken together, our study emphasizes the value of social, contextualized interventions in recovery work for people with SMI.  Practitioner points:  Working towards attachment safety in interpersonal relations may be important in recovery-oriented treatment and care for people with severe mental illness (SMI). Helping people with SMI to recognize and change how they tend to relate themselves to others may promote engagement and effectiveness of recovery-oriented treatment and care. Reciprocity and equality in social relationships as vital complements to the more one-sided nature of ‘standing alongside’ and offering support may be important requisites for empowerment

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches

The ubiquity of smartphones have opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions (momentary mental states) as an indicator for later mental ill-health. In this study, ESM data of patients with psychosis spectrum disorder and controls were used to examine daily life emotions and higher order patterns thereof. We attempted to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, were able to distinguish patients from controls in a predictive modelling framework. Variable importance, recursive feature elimination, and ReliefF methods were used for feature selection. Model training, tuning, and testing were performed in nested cross-validation, based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression and Neural Networks. ROC analysis was used to post-process these models. Stability of model performance was studied using Monte Carlo simulations. The results provide evidence that patterns in emotion changes can be captured by applying a combination of these techniques. Acceleration in the variables anxious and insecure was particularly successful in adding further predictive power to the models. The best results were achieved by Support Vector Machines with radial kernel (accuracy=82% and sensitivity=82%). This proof-of-concept work demonstrates that synergistic machine learning and statistical modeling may be used to harness the power of ESM data in the future

The development and evaluation of a computerized decision aid for the treatment of psychotic disorders

Abstract Background Routinely monitoring of symptoms and medical needs can improve the diagnostics and treatment of medical problems, including psychiatric. However, several studies show that few clinicians use Routine Outcome Monitoring (ROM) in their daily work. We describe the development and first evaluation of a ROM based computerized clinical decision aid, Treatment-E-Assist (TREAT) for the treatment of psychotic disorders. The goal is to generate personalized treatment recommendations, based on international guidelines combined with outcomes of mental and physical health acquired through ROM. We present a pilot study aimed to assess the feasibility of this computerized clinical decision aid in daily clinical practice by evaluating clinicians’ experiences with the system. Methods Clinical decision algorithms were developed based on international schizophrenia treatment guidelines and the input of multidisciplinary expert panels from multiple psychiatric institutes. Yearly obtained diagnostic (ROM) information of patients was presented to treating clinicians combined with treatment suggestions generated by the algorithms of TREAT. In this pilot study 6 clinicians and 16 patients of Lentis Psychiatric Institute used the application. Clinicians were interviewed and asked to fill out self-report questionnaires evaluating their opinions about ROM and the effectiveness of TREAT. Results Six clinicians and 16 patients with psychotic disorders participated in the pilot study. The clinicians were psychiatrists, physicians and nurse-practitioners which all worked at least 8 years in mental health care of which at least 3 years treating patients with psychotic illnesses. All Clinicians found TREAT easy to use and would like to continue using the application. They reported that TREAT offered support in using diagnostic ROM information when drafting the treatment plans, by creating more awareness of current treatment options. Conclusion This article presents a pilot study on the implementation of a computerized clinical decision aid linking routine outcome monitoring to clinical guidelines in order to generate personalized treatment advice. TREAT was found to be feasible for daily clinical practice and effective based on this first evaluation by clinicians. However, adjustments have to be made to the system and algorithms of the application. The ultimate goal is to provide appropriate evidence based care for patients with severe mental illnesses

The effects of a computerized clinical decision aid on clinical decision-making in psychosis care

Objective Clinicians in mental healthcare have few objective tools to identify and analyze their patient's care needs. Clinical decision aids are tools that support this process. This study examines whether 1) clinicians working with a clinical decision aid (TREAT) discuss more of their patient's care needs compared to usual treatment, and 2) agree on more evidence-based treatment decisions. Methods Clinicians participated in consultations (n = 166) with patients diagnosed with psychotic disorders from four Dutch mental healthcare institutions (research registration number 201700763). Primary outcomes were measured with the modified Clinical Decision-making in Routine Care questionnaire and combined with psychiatric, physical and social wellbeing related care needs. A multilevel analysis compared discussed care needs and evidence-based treatment decisions between treatment as usual (TAU) before, TAU after and the TREAT condition. Results First, a significant increase in discussed care needs for TREAT compared to both TAU conditions (β = 20.2, SE = 5.2, p = 0.00 and β = 15.8, SE = 5.4, p = 0.01) was found. Next, a significant increase in evidence-based treatments decisions for care needs was observed for TREAT compared to both TAU conditions (β = 16.7, SE = 4.8, p = 0.00 and β = 16.0, SE = 5.1, p = 0.01). Conclusion TREAT improved the discussion about physical health issues and social wellbeing related topics. It also increased evidence-based treatment decisions for care needs which are sometimes overlooked and difficult to treat. Our findings suggest that TREAT makes sense of routine outcome monitoring data and improves guideline-informed care

Measuring Health-Related Quality of Life by Experiences: The Experience Sampling Method

AbstractObjectiveTo explore the potential value of obtaining momentary, instead of retrospective, accounts of the description and valuation of a person’s own health-related quality of life (HRQOL).MethodsMomentary HRQOL was examined with the experience sampling method (ESM) in 139 participants from four different samples. The ESM consists of a so-called beep questionnaire that was administered 10 times a day by an electronic device. Feasibility was determined by assessing willingness to participate in the study and by analyzing the percentage of dropouts and the number of completed beep questionnaires. Multilevel analysis was used to investigate the relation between momentary HRQOL and momentary feelings and symptoms. The relation between momentary outcomes and the EuroQol visual analogue scale was investigated with a multiple regression model.ResultsThe overall participation rate was low, but there were no dropouts and the number of completed beeps was comparable to that in other studies. Multilevel analysis showed that feelings and symptoms were significant predictors of momentary HRQOL. The strength of these relations differed among three patient groups and a population-based sample. The EuroQol visual analogue scale was not predicted by momentary feelings and symptoms.ConclusionsWe can conclude that the use of the ESM to measure accounts of the momentary experience of health in different populations is feasible. Retrospective measures may provide a biased account of the impact of health problems in the daily lives of people who are affected. Moreover, the bias may be different in different conditions