Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study

Co-design workshops with families experiencing multiple and interacting adversities including parental mental health, substance use, domestic violence, and poverty: intervention principles and insights from mothers, fathers, and young people

BACKGROUND

Clustering and co-occurring of family adversities, including mental health problems, substance use, domestic violence and abuse, as well as poverty can increase health and behavioural risks for children, which persist throughout the life course. Yet, interventions that acknowledge and account for the complex interactive nature of such risks are limited. This study aimed to develop intervention principles based on reflections from mothers, fathers, and young people who experience multiple and interacting adversities. These principles will show how family members perceive an intervention may bring about positive change and highlight key insights into design and delivery.

METHODS

A series of six co-design workshops with mothers, fathers, and young people who experienced multiple and interacting adversities (n = 41) were iteratively conducted across two regions in England (London and North-East) by four researchers. Workshop content and co-design activities were informed by advisory groups. Data from facilitator notes and activities were analysed thematically, resulting in a set of intervention principles.

RESULTS

The intervention principles highlighted that: (1) to reduce isolation and loneliness parents and young people wanted to be connected to services, resources, and peer support networks within their local community, particularly by a knowledgeable and friendly community worker; (2) to address feelings of being misunderstood, parents and young people wanted the development of specialised trauma informed training for practitioners and to have the space to build trusting, gradual, and non-stigmatising relationships with practitioners; and (3) to address the needs and strengths of individual family members, mothers, fathers, and young people wanted separate, tailored, and confidential support.

CONCLUSIONS

The current study has important implications for practice in supporting families that experience multiple and interacting adversities. The intervention principles from this study share common characteristics with other intervention models currently on offer in the United Kingdom, including social prescribing, but go beyond these to holistically consider the whole families' needs, environments, and circumstances. There should be particular focus on the child's as well as the mothers' and fathers' needs, independently of the family unit. Further refinement and piloting of the developing intervention are needed.

Predictive Utility of Irritability "In Context": Proof-of-Principle for an Early Childhood Mental Health Risk Calculator

OBJECTIVE

We provide proof-of-principle for a mental health risk calculator advancing clinical utility of the irritability construct for identification of young children at high risk for common, early onsetting syndromes.

METHOD

Data were harmonized from two longitudinal early childhood subsamples (total N = 403; 50.1% Male; 66.7% Nonwhite; Mage = 4.3 years). The independent subsamples were clinically enriched via disruptive behavior and violence (Subsample 1) and depression (Subsample 2). In longitudinal models, epidemiologic risk prediction methods for risk calculators were applied to test the utility of the transdiagnostic indicator, early childhood irritability, in the context of other developmental and social-ecological indicators to predict risk of internalizing/externalizing disorders at preadolescence (Mage = 9.9 years). Predictors were retained when they improved model discrimination (area under the receiver operating characteristic curve [AUC] and integrated discrimination index [IDI]) beyond the base demographic model.

RESULTS

Compared to the base model, the addition of early childhood irritability and adverse childhood experiences significantly improved the AUC (0.765) and IDI slope (0.192). Overall, 23% of preschoolers went on to develop a preadolescent internalizing/externalizing disorder. For preschoolers with both elevated irritability and adverse childhood experiences, the likelihood of an internalizing/externalizing disorder was 39-66%.

CONCLUSIONS

Predictive analytic tools enable personalized prediction of psychopathological risk for irritable young children, holding transformative potential for clinical translation.

Prevalence of co-occurring severe depression and psychotic symptoms in college students and its relationship with childhood maltreatment

BACKGROUND

It has been demonstrated that childhood maltreatment (CM) is associated with an increased risk of depression and psychotic symptoms. However, there is still a lack of studies on the prevalence of co-occurring severe depression and psychotic symptoms (CSDPS) and the association between CM and CSDPS among college students.

OBJECTIVE

We aim to investigate the prevalence and demographic distribution of CSDPS among Chinese college students and to examine its relationship with CM.

PARTICIPANTS AND SETTING

A total of 7351 college students aged 18 and above completed the survey. Finally, 6854 valid questionnaires were included for further analysis.

METHODS

The Childhood Trauma Questionnaire and the item for witnessing domestic violence were used to assess the CM. The Prodromal Questionnaire-16 and the depression subscale of the Depression Anxiety and Stress-21 scale were used to assess psychotic symptoms and depressive symptoms.

RESULTS

The prevalence of CSDPS was 1.47 % among the college students. The multivariate logistic regression analysis showed that emotional abuse and emotional neglect were associated with an increased risk of CSDPS (all p < 0.05). The risk of CSDPS was dramatically increased for individuals who had experienced ≥3 types of CM (OR = 22.929, p < 0.001), as compared to those who had experienced 1-2 types of CM (OR = 4.452, p < 0.001) and those without the experience of CM.

CONCLUSION

CM was found to be associated with an increased risk of CSDPS among the college students. Notably, experiencing more types of CM was also associated with a higher risk of CSDPS.

Radiomics Model for Predicting TP53 Status Using CT and Machine Learning Approach in Laryngeal Squamous Cell Carcinoma

Objective

We aim to establish and validate computed tomography (CT)-based radiomics model for predicting TP53 status in patients with laryngeal squamous cell carcinoma (LSCC).

Methods

We divided all patients into a training set 1 (n=66) and a testing set 1 (n=30) to establish and validate radiomics model to predict TP53. Radiomics features were selected by analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (Lasso) regression analysis. Five radiomics models were established by using K-Nearest Neighbor, logistics regressive, linear-support vector machine (SVM), gaussian-SVM, and polynomial-SVM in training set 1. We also divided all patients into a training set 2 and a testing set 2 according to different CT equipment to establish and evaluate the stability of the radiomics models.

Results

After ANOVA and subsequent Lasso regression analysis, 22 radiomics features were selected to build the radiomics model in training set 1. The radiomics model based on linear-SVM has the best predictive performance of the five models, and the area under the receiver operating characteristic curve in training set 1 and testing set 1 were 0.831(95% confidence interval [CI] 0.692–0.970) and 0.797(95% CI 0.632–0.957) respectively. The specificity, sensitivity, and accuracy were 0.971(95% CI 0.834–0.999), 0.714(95% CI 0.535–0.848), and 0.843(95% CI 0.657–0.928) in training set 1 and 0.750(95% CI 0.500–0.938), 0.786(95% CI 0.571–1.000), and 0.667(95% CI 0.467–0.720) in testing set 1, respectively. In addition, the radiomics model also achieved stable prediction results even in different CT equipment. Decision curve analysis showed that the radiomics model for predicting TP53 status could benefit LSCC patients.

Conclusion

We developed and validated a relatively optimal radiomics model for TP53 status prediction by trying five different machine learning methods in patients with LSCC. It shown great potential of radiomics features for predicting TP53 status preoperatively and guiding clinical treatment.

New directions in research on childhood adversity

Childhood adversities are major preventable risk factors for poor mental and physical health. Scientific advances in this area are not matched by clinical gains for affected individuals. We reflect on novel research directions that could accelerate clinical impact.

Poor Individual Risk Classification From Adverse Childhood Experiences Screening

INTRODUCTION

Adverse childhood experiences confer an increased risk for physical and mental health problems across the population, prompting calls for routine clinical screening based on reported adverse childhood experience exposure. However, recent longitudinal research has questioned whether adverse childhood experiences can accurately identify ill health at an individual level.

METHODS

Revisiting data collected for the Adverse Childhood Experience Study between 1995 and 1997, this study derived approximate area under the curve estimates to test the ability of the retrospectively reported adverse childhood experience score to discriminate between adults with and without a range of common health risk factors and disease conditions. Furthermore, the classification accuracy of a recommended clinical definition for high-risk exposure (≥4 versus 0-3 adverse childhood experiences) was evaluated on the basis of sensitivity, specificity, positive and negative predictive values, and positive likelihood ratios.

RESULTS

Across all health outcomes, the levels of discrimination for the continuous adverse childhood experience score ranged from very poor to fair (area under the curve=0.50-0.76). The binary classification of ≥4 versus 0-3 adverse childhood experiences yielded high specificity (true-negative detection) and negative predictive values (absence of ill health among low-risk adverse childhood experience groups). However, sensitivity (true-positive detection) and positive predictive values (presence of ill health among high-risk adverse childhood experience groups) were low, whereas positive likelihood ratios suggested only minimal-to-moderate increases in health risks among individuals reporting ≥4 adverse childhood experiences versus that among those reporting 0-3.

CONCLUSIONS

These findings suggest that screening based on the adverse childhood experience score does not accurately identify those individuals at high risk of health problems. This can lead to both allocation of unnecessary interventions and lack of provision of necessary support.

A mutation-based gene set predicts survival benefit after immunotherapy across multiple cancers and reveals the immune response landscape

BACKGROUND

Immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of many cancers. However, the limited population that benefits from ICI therapy makes it necessary to screen predictive biomarkers for stratifying patients. Currently, many biomarkers, such as tumor mutational burden (TMB), have been used in the clinic as indicative biomarkers. However, some high-TMB patients with mutations in genes that are closely related to immunotherapeutic resistance are not sensitive to ICI therapy. Thus, there is a need to move beyond TMB and identify specific genetic determinants of the response to ICI therapy. In this study, we established a comprehensive mutation-based gene set across different tumor types to predict the efficacy of ICI therapy.

METHODS

We constructed and validated a mutational signature to predict the prognosis of patients treated with ICI therapy. Then, the underlying immune response landscapes of different subtypes were investigated with multidimensional data.

RESULTS

This study included genomic and clinical data for 12,647 patients. An eleven-gene mutation-based gene set was generated to divide patients into a high-risk group and a low-risk group in a training cohort (1572 patients with 9 types of cancers who were treated with ICI therapy). Validation was performed in a validation cohort (932 patients with 5 types of cancers who were treated with ICI therapy). Mutations in these 11 genes were associated with a better response to ICI therapy. In addition, the mutation-based gene set was demonstrated to be an independent prognostic factor after ICI therapy. We further explored the role of the immune context in determining the benefits of immunotherapy in 10,143 patients with 33 types of cancers and found distinct immune landscapes for the high- and low-risk groups.

CONCLUSIONS

The mutation-based gene set developed in this study can be used to reliably predict survival benefit across cancers in patients receiving ICI therapy. The close interplay between the extrinsic and intrinsic immune landscapes in the identified patient subgroups and the subgroups' differing responses to ICI therapy could guide immunotherapy treatment decisions for cancer patients.

Violent experiences and neighbourhoods during adolescence: understanding and mitigating the association with mental health at the transition to adulthood in a longitudinal cohort study

PURPOSE

Violence occurs at multiple ecological levels and can harm mental health. However, studies of adolescents' experience of violence have often ignored the community context of violence, and vice versa. We examined how personal experience of severe physical violence and living in areas with high levels of neighbourhood disorder during adolescence combine to associate with mental health at the transition to adulthood and which factors mitigate this.

METHOD

Data were from the Environmental Risk Longitudinal Twin Study, a nationally representative birth cohort of 2232 British twins. Participants' experience of severe physical violence during adolescence and past-year symptoms of psychiatric disorder were assessed via interviews at age 18. Neighbourhood disorder was reported by residents when participants were aged 13-14. Potential protective factors of maternal warmth, sibling warmth, IQ, and family socio-economic status were assessed during childhood, and perceived social support at age 18.

RESULTS

Personal experience of severe physical violence during adolescence was associated with elevated odds of age-18 psychiatric disorder regardless of neighbourhood disorder exposure. Cumulative effects of exposure to both were evident for internalising and thought disorder, but not externalising disorder. For adolescents exposed to severe physical violence only, higher levels of perceived social support (including from family and friends) were associated with lower odds of psychiatric disorder. For those who also lived in areas with high neighbourhood disorder, only family support mitigated their risk.

CONCLUSION

Increasing support or boosting adolescents' perceptions of their existing support network may be effective in promoting their mental health following violence exposure.

Validation of the youth mood recurrences risk calculator in an adult sample with bipolar disorder

BACKGROUND

The ability to predict an individual's risk of mood episode recurrence can facilitate personalized medicine in bipolar disorder (BD). We sought to externally validate, in an adult sample, a risk calculator of mood episode recurrence developed in youth/young adults with BD from the Course and Outcome of Bipolar Youth (COBY) study.

METHODS

Adult participants from the National Institute of Mental Health Collaborative Depression Study (CDS; N=258; mean(SD) age=35.5(12.0) years; mean follow-up=24.9 years) were utilized as a sample to validate the youth COBY risk calculator for onset of depressive, manic, or any mood episodes.

RESULTS

In this older validation sample, the risk calculator predicted recurrence of any episode over 1, 2, 3, or 5-year follow-up intervals, with Area Under the Curves (AUCs) approximating 0.77. The AUC for prediction of depressive episodes was about 0.81 for each of the time windows, which was higher than for manic or hypomanic episodes (AUC=0.72). While the risk calculator was well-calibrated across the range of risk scores, it systematically underestimated risk in the CDS sample by about 20%. The length of current remission was a highly significant predictor of recurrence risk in the CDS sample.

LIMITATIONS

Predominantly self-reported White samples may limit generalizability; the risk calculator does not assess more proximal risk (e.g., 1 month).

CONCLUSIONS

Risk of mood episode recurrence can be predicted with good accuracy in youth and adults with BD in remission. The risk calculators may help identify higher risk BD subgroups for treatment and research.

Translating RDoC to Real-World Impact in Developmental Psychopathology: A Neurodevelopmental Framework for Application of Mental Health Risk Calculators

The National Institute of Mental Health Research Domain Criteria's (RDoC) has prompted a paradigm shift from categorical psychiatric disorders to considering multiple levels of vulnerability for probabilistic risk of disorder. However, the lack of neurodevelopmentally-based tools for clinical decision-making has limited RDoC's real-world impact. Integration with developmental psychopathology principles and statistical methods actualize the clinical implementation of RDoC to inform neurodevelopmental risk. In this conceptual paper, we introduce the probabilistic mental health risk calculator as an innovation for such translation and lay out a research agenda for generating an RDoC- and developmentally-informed paradigm that could be applied to predict a range of developmental psychopathologies from early childhood to young adulthood. We discuss methods that weigh the incremental utility for prediction based on intensity and burden of assessment, the addition of developmental change patterns, considerations for assessing outcomes, and integrative data approaches. Throughout, we illustrate the risk calculator approach with different neurodevelopmental pathways and phenotypes. Finally, we discuss real-world implementation of these methods for improving early identification and prevention of developmental psychopathology. We propose that mental health risk calculators can build a needed bridge between RDoC's multiple units of analysis and developmental science.

Risk Factors for Revolving Door in Children and Adolescents with Psychiatric Disorders

Revolving Door (RD) is a frequent phenomenon afflicting children and adolescents with psychiatric diagnoses. Nevertheless, risk factors for RD are still a matter of debate. To better understand RD phenomenon, we conducted a retrospective study on 224 children and adolescents (165 females and 59 males, aged 6-16 years) with a psychiatric hospitalization, taking the multiple risk factors together. At this aim, 108 patients with multiple hospitalizations and 116 patients with only one hospitalization were compared on demographic characteristics, clinical conditions, psychiatric ward stay, and post-discharge management factors. More than half of psychiatric patients were readmitted within three months of discharge. RD patients presented greater severity of illness, needed longer stays, and were more frequently placed in residential facilities than non-RD patients. Non-suicidal self-injurious and adoption were the main predictors of RD. Clinical instruments that detected behavioural and emotional symptoms, suicidal ideation severity, and level of impairment of the person's functioning were useful to identify patients at high risk for RD. In conclusion, our findings pointed out that several risk factors have to be considered to better understand and, in the future, prevent RD phenomenon.

Prediction models for child and adolescent mental health: A systematic review of methodology and reporting in recent research

BACKGROUND

There has been a rapid growth in the publication of new prediction models relevant to child and adolescent mental health. However, before their implementation into clinical services, it is necessary to appraise the quality of their methods and reporting. We conducted a systematic review of new prediction models in child and adolescent mental health, and examined their development and validation.

METHOD

We searched five databases for studies developing or validating multivariable prediction models for individuals aged 18 years old or younger from 1 January 2018 to 18 February 2021. Quality of reporting was assessed using the Transparent Reporting of a multivariable prediction models for Individual Prognosis Or Diagnosis checklist, and quality of methodology using items based on expert guidance and the PROBAST tool.

RESULTS

We identified 100 eligible studies: 41 developing a new prediction model, 48 validating an existing model and 11 that included both development and validation. Most publications (k = 75) reported a model discrimination measure, while 26 investigations reported calibration. Of 52 new prediction models, six (12%) were for suicidal outcomes, 18 (35%) for future diagnosis, five (10%) for child maltreatment. Other outcomes included violence, crime, and functional outcomes. Eleven new models (21%) were developed for use in high-risk populations. Of development studies, around a third were sufficiently statistically powered (k = 16%, 31%), while this was lower for validation investigations (k = 12, 25%). In terms of performance, the discrimination (as measured by the C-statistic) for new models ranged from 0.57 for a tool predicting ADHD diagnosis in an external validation sample to 0.99 for a machine learning model predicting foster care permanency.

CONCLUSIONS

Although some tools have recently been developed for child and adolescent mental health for prognosis and child maltreatment, none can be currently recommended for clinical practice due to a combination of methodological limitations and poor model performance. New work needs to use ensure sufficient sample sizes, representative samples, and testing of model calibration.

Development and external validation of a risk calculator to predict internalising symptoms among Ugandan youths affected by HIV

The aim of the study was to develop and externally validate a model to predict individualized risk of internalizing symptoms among AIDS-affected youths in low-resource settings in sub-Saharan Africa. Longitudinal data from 558 Ugandan adolescents orphaned by AIDS was used to develop our predictive model. Least Absolute Shrinkage and Selection Operator logistic regression was used to select the best subset of predictors using 10-fold cross-validation. External validation of the final model was conducted in a sample of 372 adolescents living with HIV in Uganda. Best predictors for internalizing symptoms were gender, family cohesion, social support, asset ownership, recent sexually transmitted infection (STI) diagnosis, physical health self-rating, and previous poor mental health; area under the curve (AUC)  = 72.2; 95% CI  = 67.9-76.5. For adolescents without history of internalizing symptoms, the AUC = 69.0, 95% CI = 63.4-74.6, and was best predicted by gender, drug use, social support, asset ownership, recent STI diagnosis, and physical health self-rating. Both models were well calibrated. External validation in adolescents living with HIV sample was similar, AUC = 69.7; 95% CI = 64.1-75.2. The model predicted internalizing symptoms among African AIDS-affected youth reasonably well and showed good generalizability. The model offers opportunities for the design of public health interventions addressing poor mental health among youth affected by HIV/AIDS.

Childhood exposure to ambient air pollution and predicting individual risk of depression onset in UK adolescents

Knowledge about early risk factors for major depressive disorder (MDD) is critical to identify those who are at high risk. A multivariable model to predict adolescents' individual risk of future MDD has recently been developed however its performance in a UK sample was far from perfect. Given the potential role of air pollution in the aetiology of depression, we investigate whether including childhood exposure to air pollution as an additional predictor in the risk prediction model improves the identification of UK adolescents who are at greatest risk for developing MDD. We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally representative UK birth cohort of 2232 children followed to age 18 with 93% retention. Annual exposure to four pollutants - nitrogen dioxide (NO₂), nitrogen oxides (NO_X), particulate matter <2.5 μm (PM_2.5) and <10 μm (PM₁₀) - were estimated at address-level when children were aged 10. MDD was assessed via interviews at age 18. The risk of developing MDD was elevated most for participants with the highest (top quartile) level of annual exposure to NO_X (adjusted OR = 1.43, 95% CI = 0.96-2.13) and PM_2.5 (adjusted OR = 1.35, 95% CI = 0.95-1.92). The separate inclusion of these ambient pollution estimates into the risk prediction model improved model specificity but reduced model sensitivity - resulting in minimal net improvement in model performance. Findings indicate a potential role for childhood ambient air pollution exposure in the development of adolescent MDD but suggest that inclusion of risk factors other than this may be important for improving the performance of the risk prediction model.

E-Cigarette Use in Young Adult Never Cigarette Smokers with Disabilities: Results from the Behavioral Risk Factor Surveillance System Survey

Young adult never cigarette smokers with disabilities may be at particular risk for adopting e-cigarettes, but little attention has been paid to these people. This study examines the associations between different types of disability and e-cigarette use in this population. Young adult never-smokers from the 2016-2017 Behavioral Risk Factor Surveillance System (BRFSS) survey who were either never or current e-cigarette users (n = 79,177) were selected for the analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select confounders for multivariable logistic regression models. Multivariable logistic regression models were used to determine the associations between current e-cigarette use and different types of disability after incorporating BRFSS survey design and adjusting for confounders. Young adult never-smokers who reported any disability had increased odds (OR 1.44, 95% CI 1.18-1.76) of e-cigarette use compared to those who reported no disability. Young adult never-smokers who reported self-care, cognitive, vision, and independent living disabilities had higher odds of e-cigarette use compared to those who reported no disability. There was no statistically significant difference in the odds of e-cigarette use for those reporting hearing and mobility disabilities compared to those who reported no disability. This study highlights the need for increased public education and cessation programs for this population.

Population vs Individual Prediction of Poor Health From Results of Adverse Childhood Experiences Screening

IMPORTANCE

Adverse childhood experiences (ACEs) are well-established risk factors for health problems in a population. However, it is not known whether screening for ACEs can accurately identify individuals who develop later health problems.

OBJECTIVE

To test the predictive accuracy of ACE screening for later health problems.

DESIGN, SETTING, AND PARTICIPANTS

This study comprised 2 birth cohorts: the Environmental Risk (E-Risk) Longitudinal Twin Study observed 2232 participants born during the period from 1994 to 1995 until they were aged 18 years (2012-2014); the Dunedin Multidisciplinary Health and Development Study observed 1037 participants born during the period from 1972 to 1973 until they were aged 45 years (2017-2019). Statistical analysis was performed from May 28, 2018, to July 29, 2020.

EXPOSURES

ACEs were measured prospectively in childhood through repeated interviews and observations in both cohorts. ACEs were also measured retrospectively in the Dunedin cohort through interviews at 38 years.

MAIN OUTCOMES AND MEASURES

Health outcomes were assessed at 18 years in E-Risk and at 45 years in the Dunedin cohort. Mental health problems were assessed through clinical interviews using the Diagnostic Interview Schedule. Physical health problems were assessed through interviews, anthropometric measurements, and blood collection.

RESULTS

Of 2232 E-Risk participants, 2009 (1051 girls [52%]) were included in the analysis. Of 1037 Dunedin cohort participants, 918 (460 boys [50%]) were included in the analysis. In E-Risk, children with higher ACE scores had greater risk of later health problems (any mental health problem: relative risk, 1.14 [95% CI, 1.10-1.18] per each additional ACE; any physical health problem: relative risk, 1.09 [95% CI, 1.07-1.12] per each additional ACE). ACE scores were associated with health problems independent of other information typically available to clinicians (ie, sex, socioeconomic disadvantage, and history of health problems). However, ACE scores had poor accuracy in predicting an individual's risk of later health problems (any mental health problem: area under the receiver operating characteristic curve, 0.58 [95% CI, 0.56-0.61]; any physical health problem: area under the receiver operating characteristic curve, 0.60 [95% CI, 0.58-0.63]; chance prediction: area under the receiver operating characteristic curve, 0.50). Findings were consistent in the Dunedin cohort using both prospective and retrospective ACE measures.

CONCLUSIONS AND RELEVANCE

This study suggests that, although ACE scores can forecast mean group differences in health, they have poor accuracy in predicting an individual's risk of later health problems. Therefore, targeting interventions based on ACE screening is likely to be ineffective in preventing poor health outcomes.

Preoperative prognostic nomogram for prophylactic steroid treatment of patients with subclinical Cushing's syndrome

Background

Subclinical Cushing’s syndrome (SCS) is incidentally detected in a growing number of patients by advanced imaging technology. However, there is no consensus on the clinical management of SCS, especially in terms of whether prophylactic steroid treatment is necessary following adrenalectomy. In this study we developed a model based on preoperative indices for predicting postoperative adrenal insufficiency (AI) that can guide therapeutic decision-making.

Methods

A total of 27 patients with SCS who underwent adrenalectomy between August 2016 and August 2019 were enrolled and divided into AI and non-AI groups. Cox proportional hazards regression and least absolute shrinkage and selection operator analyses were performed to select relevant clinical parameters. The predictive performance of our model was evaluated by time-dependent receiver operating characteristic (ROC) curve and calibration curve analyses.

Results

Five clinical parameters (apolipoprotein A1, neutrophil–lymphocyte ratio, total cholesterol, platelet count, and homocysteine) were identified as the best predictors of replacement therapy (RT). The areas under the ROC curve for our prognostic model were 0.833, 0.945, and 0.967 for 3-, 4-, and 5-day non-(N)RT, respectively. The calibration curve of the 5 independent RT-related markers showed a good fit between nomogram-predicted probability of NRT and actual NRT, suggesting that our model has good predictive value.

Conclusions

Our prognostic nomogram can help clinicians identify patients with AI who would benefit from RT so that timely treatment can be initiated.

Keywords

Subclinical Cushing’s syndrome (SCS); Replacement therapy (RT); Adrenal insufficiency (AI); Nomogram; Receiver operating characteristic (ROC)

Understanding practitioners' and young people's views of a risk calculator for future psychopathology and poor functioning in young people victimised during childhood

BACKGROUND

Although children who are exposed to victimisation (including abuse, neglect, domestic violence and bullying) have an increased risk of later psychopathology and functional impairment, not all go on to develop these outcomes. Risk calculators that generate individualised probabilities of a victimised child developing future psychopathology and poor functioning have the potential to help practitioners identify the most vulnerable children and efficiently target preventive interventions.

AIM

This study explored the views of young people and practitioners regarding the acceptability and feasibility of potentially using a risk calculator to predict victimised children's individual risk of poor outcomes.

METHODS

Young people (n = 6) with lived experience of childhood victimisation took part in two focus groups. Health and social care practitioners (n = 13) were interviewed individually. Focus groups and interviews were audio-recorded, transcribed and thematically analysed.

RESULTS

Ten themes were identified, organised according to the three main topics of discussion: (i) identifying risk (risk factors, current practice, accuracy, implementation, response); (ii) protective factors and prevention (individual, environment, preventative intervention); and (iii) communication of research (stakeholders, methods).

CONCLUSION

Risk calculators have the potential to enhance health and social care practice in the United Kingdom, but we highlight key factors that require consideration for successful implementation.

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18-34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis (n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

Reciprocal links between anxiety sensitivity and obsessive-compulsive symptoms in youth: a longitudinal twin study

BACKGROUND

Anxiety sensitivity, the tendency to fear the symptoms of anxiety, is a key risk factor for the development anxiety disorders. Although obsessive-compulsive disorder was previously classified as an anxiety disorder, the prospective relationship between anxiety sensitivity and obsessive-compulsive symptoms (OCS) has been largely overlooked. Furthermore, a lack of genetically informative studies means the aetiology of the link between anxiety sensitivity and OCS remains unclear.

METHODS

Adolescent twins and siblings (N = 1,579) from the G1219 study completed self-report questionnaires two years apart assessing anxiety sensitivity, OCS, anxiety and depression. Linear regression models tested prospective associations between anxiety sensitivity and OCS, with and without adjustment for anxiety and depressive symptoms. A phenotypic cross-lagged model assessed bidirectional influences between anxiety sensitivity and OCS over time, and a genetic version of this model examined the aetiology of these associations.

RESULTS

Anxiety sensitivity was prospectively associated with changes in OCS, even after controlling for comorbid anxiety and depressive symptoms. The longitudinal relationship between anxiety sensitivity and OCS was bidirectional, and these associations were predominantly accounted for by nonshared environmental influences.

CONCLUSIONS

Our findings are consistent with the notion that anxiety sensitivity is a risk factor for OCS during adolescence, but also suggest that experiencing OCS confers risk for heightened anxiety sensitivity. The reciprocal links between OCS and anxiety sensitivity over time are likely to be largely mediated by nonshared environmental experiences, as opposed to common genes. Our findings raise the possibility that interventions aimed at ameliorating anxiety sensitivity could reduce risk for OCS, and vice versa.

Embracing the positive: an examination of how well resilience factors at age 14 can predict distress at age 17

One-in-two people suffering from mental health problems develop such distress before or during adolescence. Research has shown that distress can predict itself well over time. Yet, little is known about how well resilience factors (RFs), i.e. those factors that decrease mental health problems, predict subsequent distress. Therefore, we investigated which RFs are the best indicators for subsequent distress and with what accuracy RFs predict subsequent distress. We examined three interpersonal (e.g. friendships) and seven intrapersonal RFs (e.g. self-esteem) and distress in 1130 adolescents, at age 14 and 17. We estimated the RFs and a continuous distress-index using factor analyses, and ordinal distress-classes using factor mixture models. We then examined how well age-14 RFs and age-14 distress predict age-17 distress, using stepwise linear regressions, relative importance analyses, as well as ordinal and linear prediction models. Low brooding, low negative and high positive self-esteem RFs were the most important indicators for age-17 distress. RFs and age-14 distress predicted age-17 distress similarly. The accuracy was acceptable for ordinal (low/moderate/high age-17 distress-classes: 62-64%), but low for linear models (37-41%). Crucially, the accuracy remained similar when only self-esteem and brooding RFs were used instead of all ten RFs (ordinal = 62%; linear = 37%); correctly predicting for about two-in-three adolescents whether they have low, moderate or high distress 3 years later. RFs, and particularly brooding and self-esteem, seem to predict subsequent distress similarly well as distress can predict itself. As assessing brooding and self-esteem can be strength-focussed and is time-efficient, those RFs may be promising for risk-detection and translational intervention research.

Annual Research Review: Rethinking childhood trauma-new research directions for measurement, study design and analytical strategies

Childhood trauma is a key modifiable risk factor for psychopathology. Despite significant scientific advances, traumatised children still have poorer long-term outcomes than nontraumatised children. New research paradigms are, thus, needed. To this end, the review examines three dominant assumptions about measurement, design and analytical strategies. Current research warns against using prospective and retrospective measures of childhood trauma interchangeably; against interpreting cross-sectional differences in putative mediating mechanisms between adults with or without a history of childhood trauma as evidence of longitudinal changes from pre-trauma conditions; and against directly applying explanatory models of resilience or vulnerability to psychopathology in traumatised children to forecast individual risk in unseen cases. The warnings equally apply to research on broader measures of adverse childhood experiences (ACEs). Further research examining these assumptions can generate new insights on how to prevent childhood trauma and its detrimental effects.

Development of an individualized risk calculator for poor functioning in young people victimized during childhood: A longitudinal cohort study

BACKGROUND

Childhood victimization elevates the average risk of developing functional impairment in adulthood. However, not all victimized children demonstrate poor outcomes. Although research has described factors that confer vulnerability or resilience, it is unknown if this knowledge can be translated to accurately identify the most vulnerable victimized children.

OBJECTIVE

To build and internally validate a risk calculator to identify those victimized children who are most at risk of functional impairment at age 18 years.

PARTICIPANTS

We utilized data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative birth cohort of 2232 UK children born in 1994-95.

METHODS

Victimization exposure was assessed repeatedly between ages 5 and 12 years along with a range of individual-, family- and community-level predictors. Functional outcomes were assessed at age 18 years. We developed and evaluated a prediction model for psychosocial disadvantage and economic disadvantage using the Least Absolute Shrinkage and Selection Operator (LASSO) regularized regression with nested 10-fold cross-validation.

RESULTS

The model predicting psychosocial disadvantage following childhood victimization retained 12 of 22 predictors, had an area under the curve (AUC) of 0.65, and was well-calibrated within the range of 40-70% predicted risk. The model predicting economic disadvantage retained 10 of 22 predictors, achieved excellent discrimination (AUC = 0.80), and a high degree of calibration.

CONCLUSIONS

Prediction modelling techniques can be applied to estimate individual risk for poor functional outcomes in young adulthood following childhood victimization. Such risk prediction tools could potentially assist practitioners to target interventions, which is particularly useful in a context of scarce resources.