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Impact of genetic, sociodemographic, and clinical features on antidepressant treatment trajectories in the perinatal period.
Pregnant women on antidepressants must balance potential fetal harm with the relapse risk. While various clinical and sociodemographic factors are known to influence treatment decisions, the impact of genetic factors remains unexplored. We conducted a cohort study among 2,316 women with diagnosed affective disorders who had redeemed antidepressant prescriptions six months before pregnancy, identified from the Danish Integrated Psychiatric Research study. We calculated polygenic risk scores (PGSs) for major depression (MDD), bipolar disorder (BD), and schizophrenia (SCZ) using individual-level genetic data and summary statistics from genome-wide association studies. We retrieved data on sociodemographic and clinical features from national registers. Applying group-based trajectory modeling, we identified four treatment trajectories across pregnancy and postpartum: Continuers (38.2 %), early discontinuers (22.7 %), late discontinuers (23.8 %), and interrupters (15.3 %). All three PGSs were not associated with treatment trajectories; for instance, the relative risk ratio for continuers versus early discontinuers was 0.93 (95 % CI: 0.81-1.06), 0.98 (0.84-1.13), 1.09 (0.95-1.27) for per 1-SD increase in PGS for MDD, BD, and SCZ, respectively. Sociodemographic factors were generally not associated with treatment trajectories, except for the association between primiparity and continuing antidepressant use. Women who received ≥2 classes or a higher dose of antidepressants had a higher probability of being late discontinuers, interrupters, and continuers. The likelihood of continuing antidepressants or restarting antidepressants postpartum increased with the previous antidepressant treatment duration. Our findings indicate that continued antidepressant use during pregnancy is influenced by the severity of the disease rather than genetic predisposition as measured by PGSs.
A competency framework on simulation modelling-supported decision-making for Master of Public Health graduates
Background Simulation models are increasingly important for supporting decision-making in public health. However, due to lack of training, many public health professionals remain unfamiliar with constructing simulation models and using their outputs for decision-making. This study contributes to filling this gap by developing a competency framework on simulation model-supported decision-making targeting Master of Public Health education. Methods The study combined a literature review, a two-stage online Delphi survey and an online consensus workshop. A draft competency framework was developed based on 28 peer-reviewed publications. A two-stage online Delphi survey involving 15 experts was conducted to refine the framework. Finally, an online consensus workshop, including six experts, evaluated the competency framework and discussed its implementation. Results The competency framework identified 20 competencies related to stakeholder engagement, problem definition, evidence identification, participatory system mapping, model creation and calibration and the interpretation and dissemination of model results. The expert evaluation recommended differentiating professional profiles and levels of expertise and synergizing with existing course contents to support its implementation. Conclusions The competency framework developed in this study is instrumental to including simulation model-supported decision-making in public health training. Future research is required to differentiate expertise levels and develop implementation strategies.
Psilocybin therapy for treatment resistant depression: prediction of clinical outcome by natural language processing.
RATIONALE: Therapeutic administration of psychedelics has shown significant potential in historical accounts and recent clinical trials in the treatment of depression and other mood disorders. A recent randomized double-blind phase-IIb study demonstrated the safety and efficacy of COMP360, COMPASS Pathways' proprietary synthetic formulation of psilocybin, in participants with treatment-resistant depression. OBJECTIVE: While the phase-IIb results are promising, the treatment works for a portion of the population and early prediction of outcome is a key objective as it would allow early identification of those likely to require alternative treatment. METHODS: Transcripts were made from audio recordings of the psychological support session between participant and therapist 1 day post COMP360 administration. A zero-shot machine learning classifier based on the BART large language model was used to compute two-dimensional sentiment (valence and arousal) for the participant and therapist from the transcript. These scores, combined with the Emotional Breakthrough Index (EBI) and treatment arm were used to predict treatment outcome as measured by MADRS scores. (Code and data are available at https://github.com/compasspathways/Sentiment2D .) RESULTS: Two multinomial logistic regression models were fit to predict responder status at week 3 and through week 12. Cross-validation of these models resulted in 85% and 88% accuracy and AUC values of 88% and 85%. CONCLUSIONS: A machine learning algorithm using NLP and EBI accurately predicts long-term patient response, allowing rapid prognostication of personalized response to psilocybin treatment and insight into therapeutic model optimization. Further research is required to understand if language data from earlier stages in the therapeutic process hold similar predictive power.
Reducing intrusive memories after trauma via an imagery-competing task intervention in COVID-19 intensive care staff: a randomised controlled trial.
Intrusive memories (IMs) after traumatic events can be distressing and disrupt mental health and functioning. We evaluated the impact of a brief remotely-delivered digital imagery-competing task intervention on the number of IMs for intensive care unit (ICU) staff who faced repeated trauma exposure during the COVID-19 pandemic using a two-arm, parallel-group, single-blind randomised controlled trial, with the comparator arm receiving delayed access to active treatment (crossover). Eligible participants worked clinically in a UK NHS ICU during the pandemic and had at least 3 IMs of work-related traumatic events in the week before recruitment. Participants were randomly assigned (1:1) to immediate (weeks 1-4) or delayed (weeks 5-8) intervention access. Sequential Bayesian analyses to optimise the intervention and increase trial efficiency are reported elsewhere [1]. The primary endpoint for the pre-specified frequentist analysis of the final study population compared the number of IMs experienced in week 4 between the immediate and delayed access arms. Secondary outcomes included clinical symptoms, work functioning and wellbeing. Safety was assessed throughout the trial by scheduled questions and free report. All analyses were undertaken on an intention-to-treat basis (86 randomised participants). There were significantly fewer intrusive memories during week 4 in the immediate (median = 1, IQR = 0-3, n = 43), compared to the comparator delayed arm (median = 10, IQR = 6-17, n = 43), IRR 0.31, 95% CI: 0.20-0.48, p
Combining antidepressants and attention bias modification in primary health care (DEPTREAT): Protocol for a pragmatic randomized controlled trial.
BACKGROUND: Major depressive disorder (MDD) is a highly prevalent psychiatric condition associated with significant disability, mortality and economic burden. A large proportion of MDD patients are treated in primary health care in the local community. Attentional Bias Modification (ABM) training in combination with antidepressants could be an effective treatment. Here we test the hypothesis that adding an ABM procedure to regular treatment with antidepressants in primary health care will result in further improvement of symptoms compared to treatment with antidepressants alone (treatment as usual, TAU) and as compared to an active comparison condition. METHODS: A total of 246 patients with a diagnosis of MDD will be included in this study. The study is a three-armed pragmatic randomized controlled trial comparing the efficacy of ABM as add-on to treatment with antidepressants in primary care (ABM condition) compared to standard antidepressant treatment (TAU condition). In a third group participants will complete the same schedule of intermediate assessments as the ABM condition in addition to TAU, but no ABM, thus controlling for the non-training-specific aspects of the ABM condition (Antidepressant active comparison group). DISCUSSION: The clinical outcome of this study may help develop easily accessible, low-cost treatment of depression in primary health care. Moreover, the study aims to broaden our knowledge of optimal treatment for patients with a MDD by providing adjunct treatment to facilitate recovery and long-term gain.
USING ELECTRONIC HEALTH RECORDS TO FACILITATE PRECISION PSYCHIATRY.
The use of clinical prediction models to produce individualised risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implementing them in routine clinical care. The present review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number-needed-to-test). We review four externally validated clinical prediction models designed to predict, respectively: psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models, and the potential added value of integrating data from evidence syntheses, standardised psychometric assessments, and biological data into EHRs. Clinical prediction models can utilise routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g. meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve performance of clinical prediction models.
Suicide risk assessment tools and prediction models: new evidence, methodological innovations, outdated criticisms
The number of prediction models for suicide-related outcomes has grown substantially in recent years. These models aim to assist in stratifying risk, improve clinical decision-making, and facilitate a personalised medicine approach to the prevention of suicidal behaviour. However, there are contrasting views as to whether prediction models have potential to inform and improve assessment of suicide risk. In this perspective, we discuss common misconceptions that characterise criticisms of suicide risk prediction research. First, we discuss the limitations of a classification approach to risk assessment (eg, categorising individuals as low-risk vs high-risk), and highlight the benefits of probability estimation. Second, we argue that the preoccupation with classification measures (such as positive predictive value) when assessing a model’s predictive performance is inappropriate, and discuss the importance of clinical context in determining the most appropriate risk threshold for a given model. Third, we highlight that adequate discriminative ability for a prediction model depends on the clinical area, and emphasise the importance of calibration, which is almost entirely overlooked in the suicide risk prediction literature. Finally, we point out that conclusions about the clinical utility and health-economic value of suicide prediction models should be based on appropriate measures (such as net benefit and decision-analytic modelling), and highlight the role of impact assessment studies. We conclude that the discussion around using suicide prediction models and risk assessment tools requires more nuance and statistical expertise, and that guidelines and suicide prevention strategies should be informed by the new and higher quality evidence in the field.
Value-based decision-making between affective and non-affective memories.
Affective biases can change how past events are recalled from memory. To capture mechanisms underlying affective memory formation, recall, and bias, we studied value-based decision-making (VBDM) between reward memories encoded in different mood states. Our findings suggest that following discrete affective events, created by large magnitude wins and losses on a Wheel of Fortune (WoF), healthy volunteers display an overall positive memory bias [favoring higher probability shapes learned after a WoF win compared with those learnt after a WoF loss outcome]. During this VBDM process, participants' pupils constrict before decision-onset for higher-value choices, and remained dilated for a sustained period after choice. Sustained pupil dilation was particularly sensitive to the reward values of abstract memories encoded in a positive mood. Taken together, we demonstrate that experimentally induced affective memories are recalled with a positive bias, and pupil-linked central arousal systems are actively engaged during VBDM between affective and non-affective memories.
Physical activity as a tool for preventing and treating depression: Lessons learned from the COVID‐19 pandemic
AbstractPhysical activity (PA) is understood to be important for the prevention and treatment of depression, however, less is known about the effects of withdrawal from PA on mood. Here we consider evidence published since the outbreak of the SARS‐CoV‐2 virus to assess the impact of the COVID‐19 pandemic on PA patterns and to evaluate whether engagement in PA in the context of the pandemic had an impact on depression vulnerability. During the initial stages of the pandemic and consequent lockdowns, there were global decreases in PA, with women, ethnic minorities, lower‐education, lower‐income, younger, and elderly people displaying more marked reductions in PA. Less PA was associated with a higher risk of experiencing moderate‐to‐severe depression symptoms, particularly for those who decreased their PA levels compared to pre‐pandemic. Both PA and sedentary behavior were independently associated with depression, such that low activity and high amounts of sitting both increased the likelihood of clinically significant symptoms. We also consider the role social connection during movement; while both in‐person and online PA can foster a sense of belonging, there is some evidence that socially distant, pandemic‐safe movement might disincentivise certain groups such as older adults and experienced exercisers from participating in PA. We conclude with several implications for prospective public health communications regarding PA, especially in the event of another global pandemic.
Dementia risk and thalamic nuclei volumetry in healthy midlife adults: the PREVENT Dementia study.
A reduction in the volume of the thalamus and its nuclei has been reported in Alzheimer's disease, mild cognitive impairment and asymptomatic individuals with risk factors for early-onset Alzheimer's disease. Some studies have reported thalamic atrophy to occur prior to hippocampal atrophy, suggesting thalamic pathology may be an early sign of cognitive decline. We aimed to investigate volumetric differences in thalamic nuclei in middle-aged, cognitively unimpaired people with respect to dementia family history and apolipoprotein ε4 allele carriership and the relationship with cognition. Seven hundred participants aged 40-59 years were recruited into the PREVENT Dementia study. Individuals were stratified according to dementia risk (approximately half with and without parental dementia history). The subnuclei of the thalamus of 645 participants were segmented on T1-weighted 3 T MRI scans using FreeSurfer 7.1.0. Thalamic nuclei were grouped into six regions: (i) anterior, (ii) lateral, (iii) ventral, (iv) intralaminar, (v) medial and (vi) posterior. Cognitive performance was evaluated using the computerized assessment of the information-processing battery. Robust linear regression was used to analyse differences in thalamic nuclei volumes and their association with cognitive performance, with age, sex, total intracranial volume and years of education as covariates and false discovery rate correction for multiple comparisons. We did not find significant volumetric differences in the thalamus or its subregions, which survived false discovery rate correction, with respect to first-degree family history of dementia or apolipoprotein ε4 allele status. Greater age was associated with smaller volumes of thalamic subregions, except for the medial thalamus, but only in those without a dementia family history. A larger volume of the mediodorsal medial nucleus (Pfalse discovery rate = 0.019) was associated with a faster processing speed in those without a dementia family history. Larger volumes of the thalamus (P = 0.016) and posterior thalamus (Pfalse discovery rate = 0.022) were associated with significantly worse performance in the immediate recall test in apolipoprotein ε4 allele carriers. We did not find significant volumetric differences in thalamic subregions in relation to dementia risk but did identify an interaction between dementia family history and age. Larger medial thalamic nuclei may exert a protective effect on cognitive performance in individuals without a dementia family history but have little effect on those with a dementia family history. Larger volumes of posterior thalamic nuclei were associated with worse recall in apolipoprotein ε4 carriers. Our results could represent initial dysregulation in the disease process; further study is needed with functional imaging and longitudinal analysis.
The perception, understanding and experience of flourishing in young people living with chronic pain: A Q-methodology study
Much research has adopted a deficits-based approach to chronic pain, neglecting the study of flourishing. Using a Q-methodological framework, this study sought to explore how individuals experience, understand and perceive flourishing in the context of young people living with chronic pain. Fifty-four individuals completed a Q-sorting task, indicating their level of agreement and disagreement with 52 statements. Q-analysis generated three factors that represented clear viewpoints of participants: ‘Pain is not a barrier to flourishing’, ‘Adapting to a new life’ and ‘Adopting a positive perspective’. Factors expressed the viewpoints that flexibility is key to enjoying life despite chronic pain, while resilience, management of stressors, acceptance and problem-solving may be required to flourish with chronic pain. Participants’ understanding of flourishing also focused on the appreciation and enjoyment of life and achievements. This study provides a useful contribution towards furthering our understanding of flourishing in young people living with chronic pain.
The experience of seeking and accessing help from mental health services among young people of Eastern European backgrounds: A qualitative interview study.
OBJECTIVES: Most lifetime mental health problems (MHP) start before the age of 25. Yet young people-particularly those of minority backgrounds-often do not seek or access professional help. In the UK, young people of Eastern European (EE) backgrounds represent a large minority group; however, little is known about their experiences of MHP and help-seeking. In this study, we aim to understand the help-seeking process from the perspectives of EE young people. DESIGN: We used a qualitative study design with semi-structured individual interviews. The results were analysed using reflexive thematic analysis. METHOD: Twelve young people (18-25 years) of EE backgrounds, living in Oxfordshire, UK, took part. All participants had experienced a severe MHP and were identified in the community. RESULTS: EE young people's experiences of MHP and help-seeking were driven by a sense of being caught between different cultures and simultaneously needing to navigate the potentially contrasting expectations of both cultures. This process was reinforced or tempered by the perceived continuing influence of young people's families, that is, families with more open views about MHP made it easier for young people to navigate through the process of help-seeking. Young people's internalised cultural and familial beliefs about MHP affected their decision-making when experiencing difficulties, their levels of trust in services, and perceived sense of resourcefulness and ability to cope. CONCLUSIONS: Recognising and responding to the cultural tension that young people of EE backgrounds may experience can help us to develop more accessible and inclusive mental health services.
Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization.
Adolescence is a timed process with an onset, tempo, and duration. Nevertheless, the temporal dimension, especially the pace of maturation, remains an insufficiently studied aspect of developmental progression. The primary objective is to estimate the precise influence of pubertal maturational tempo on the configuration of associative brain regions. To this end, the connection between maturational stages and the level of hierarchical organization of large-scale brain networks in 12-13-year-old females is analyzed. Skeletal maturity is used as a proxy for pubertal progress. The degree of maturity is defined by the difference between bone age and chronological age. To assess the level of hierarchical organization in the brain, the temporal dynamic of closed eye resting state high-density electroencephalography (EEG) in the alpha frequency range is analyzed. Different levels of hierarchical order are captured by the measured asymmetry in the directionality of information flow between different regions. The calculated EEG-based entropy production of participant groups is then compared with accelerated, average, and decelerated maturity. Results indicate that an average maturational trajectory optimally aligns with cerebral hierarchical order, and both accelerated and decelerated timelines result in diminished cortical organization. This suggests that a "Goldilocks rule" of brain development is favoring a particular maturational tempo.
Empathy and the work of clinical psychiatrists: narrative review
Clinical research suggests that empathy is associated with better clinical outcomes in various areas of medical care, raising the question of whether a similar effect occurs in psychiatry. The aim of this review is to explore philosophical, neuroscientific and psychological perspectives on the concept of empathy in the context of the day-today work of clinical psychiatrists. The definition of empathy is outlined and sociodemographic factors, working conditions and psychiatrists’ beliefs that can potentially affect empathy in clinical encounters are explored; educational and training aspects are also reviewed. The review concludes suggesting that research on empathy is needed to understand contextual, training and relational factors that could benefit mental healthcare as well as the working conditions of clinical psychiatrists, both inextricably linked.