The association between self-reported and clinically determined hypomanic symptoms and the onset of major mood disorders.

Hypomanic symptoms may be a useful predictor of mood disorder among young people at high risk for bipolar disorder. To determine whether hypomanic symptoms differentiate offspring of parents with bipolar disorder (high risk) and offspring of well parents (control) and predict the development of mood episodes. High-risk and control offspring were prospectively assessed using semi-structured clinical interviews annually and completed the Hypomania Checklist-32 Revised (HCL-32). Clinically significant sub-threshold hypomanic symptoms (CSHS) were coded. HCL-32 total and active or elated scores were higher in control compared with high-risk offspring, whereas 14% of high-risk and 0% of control offspring had CSHS. High-risk offspring with CSHS had a fivefold increased risk of developing recurrent major depression (P=0.0002). The median onset of CSHS in high-risk offspring was 16.4 (6-31) years and was before the onset of major mood episodes. CSHS are precursors to major mood episodes in high-risk offspring and could identify individuals at ultra-high risk for developing bipolar disorder. None. © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license

Development and validation of a risk calculator for major mood disorders among the offspring of bipolar parents using information collected in routine clinical practice.

Family history is a significant risk factor for bipolar disorders (BD), but the magnitude of risk varies considerably between individuals within and across families. Accurate risk estimation may increase motivation to reduce modifiable risk exposures and identify individuals appropriate for monitoring over the peak risk period. Our objective was to develop and independently replicate an individual risk calculator for bipolar spectrum disorders among the offspring of BD parents using data collected in routine clinical practice. Data from the longitudinal Canadian High-Risk Offspring cohort study collected from 1996 to 2020 informed the development of a 5 and 10-year risk calculator using parametric time-to-event models with a cure fraction and a generalized gamma distribution. The calculator was then externally validated using data from the Lausanne-Geneva High-Risk Offspring cohort study collected from 1996 to 2020. A time-varying C-index by age in years was used to estimate the probability that the model correctly classified risk. Bias corrected estimates and 95% confidence limits were derived using a jackknife resampling approach. The primary outcome was age of onset of a major mood disorder. The risk calculator was most accurate at classifying risk in mid to late adolescence in the Canadian cohort (n = 285), and a similar pattern was replicated in the Swiss cohort (n = 128). Specifically, the time-varying C-index indicated that there was approximately a 70% chance that the model would correctly predict which of two 15-year-olds would be more likely to develop the outcome in the future. External validation within a smaller Swiss cohort showed mixed results. Findings suggest that this model may be a useful clinical tool in routine practice for improved individualized risk estimation of bipolar spectrum disorders among the adolescent offspring of a BD parent; however, risk estimation in younger high-risk offspring is less accurate, perhaps reflecting the evolving nature of psychopathology in early childhood. Based on external validation with a Swiss cohort, the risk calculator may not be as predictive in more heterogenous high-risk populations. The Canadian High-Risk Study has been funded by consecutive operating grants from the Canadian Institutes for Health Research, currently CIHR PJT Grant 152796 he Lausanne-Geneva high-risk study was and is supported by five grants from the Swiss National Foundation (#3200-040,677, #32003B-105,969, #32003B-118,326, #3200-049,746 and #3200-061,974), three grants from the Swiss National Foundation for the National Centres of Competence in Research project "The Synaptic Bases of Mental Diseases" (#125,759, #158,776, and #51NF40 - 185,897), and a grant from GlaxoSmithKline Clinical Genetics

Maximizing the use of social and behavioural information from secondary care mental health electronic health records

Purpose The contribution of social and behavioural factors in the development of mental health conditions and treatment effectiveness is widely supported, yet there are weak population level data sources on social and behavioural determinants of mental health. Enriching these data gaps will be crucial to accelerating precision medicine. Some have suggested the broader use of electronic health records (EHR) as a source of non-clinical determinants, although social and behavioural information are not systematically collected metrics in EHRs, internationally. Objective In this commentary, we highlight the nature and quality of key available structured and unstructured social and behavioural data using a case example of value counts from secondary mental health data available in the UK from the UK Clinical Record Interactive Search (CRIS) database; highlight the methodological challenges in the use of such data; and possible solutions and opportunities involving the use of natural language processing (NLP) of unstructured EHR text. Conclusions Most structured non-clinical data fields within secondary care mental health EHR data have too much missing data for adequate use. The utility of other non-clinical fields reported semi-consistently (e.g., ethnicity and marital status) is entirely dependent on treating them appropriately in analyses, quantifying the many reasons behind missingness in consideration of selection biases. Advancements in NLP offer new opportunities in the exploitation of unstructured text from secondary care EHR data particularly given that clinical notes and attachments are available in large volumes of patients and are more routinely completed by clinicians. Tackling ways to re-use, harmonize, and improve our existing and future secondary care mental health data, leveraging advanced analytics such as NLP is worth the effort in an attempt to fill the data gap on social and behavioural contributors to mental health conditions and will be necessary to fulfill all of the domains needed to inform personalized interventions

Temperament and self-esteem in high-risk offspring of bipolar parents: Vulnerability and scar effects.

The nature of the temporal relationship between psychological factors and mood episodes is unclear. The objectives of this study were to determine if temperament and self-esteem predict the onset of mood episodes, and if prior mood episodes influence the stability of these factors over time in high-risk offspring of bipolar parents. Offspring of a parent with bipolar disorder participating in the Flourish Prospective Offspring Study were clinically assessed repeatedly using semi-structured KSADS-PL/SADS-L format interviews, and completed repeated measures of self-esteem, and temperament. Shared frailty survival models and mixed effects regression models were used to determine if psychological factors predicted incident mood episodes, and whether these factors change over time after the incident mood episode, respectively. Emotionality, shyness and self-esteem were not associated with the hazard of incident major depression; however, increased activity reduced the hazard of this outcome (hazard ratio [HR]: 0.51; 95% CI: 0.27, 0.98). Emotionality and shyness scores increased, while sociability, activity and self-esteem scores decreased after the incident major depressive episode (emotionality: mean change [MC]: 0.35, p = 0.0289; shyness: MC: 0.40, p = 0.0196; sociability: MC: -0.49, p = 0.0001, activity: MC: -0.32, p = 0.0001; self-esteem: MC: -0.79, p = 0.001). Psychological measures were based on self-report and some models had low numbers limiting the numbers of covariates included as potential confounders. Among the assessed temperamental dimensions, activity showed a protective effect for major depressive episode onset suggesting this temperamental characteristic could serve as a protective target in high risk youth. Conversely, all assessed psychological factors shifted towards increased vulnerability after the first depressive episode