Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing. OBJECTIVE: To differentiate the risk of suicide attempt and self-harm following general medical hospitalization among women with depression, bipolar disorder, and chronic psychosis. METHODS: We developed a machine learning algorithm that identified risk factors of suicide attempt and self-harm after general hospitalization using electronic health record data from 1628 women in the University of California Los Angeles Integrated Clinical and Research Data Repository. To assess replicability, we applied the algorithm to a larger sample of 140,848 women in the New York City Clinical Data Research Network. RESULTS: The classification tree algorithm identified risk groups in University of California Los Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73, sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations characterizing key risk groups were replicated in New York City Clinical Data Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and accuracy 0.84). Predictors included medical comorbidity, history of pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as were women below 55 years old without antecedent medical illness (4.0%-7.5% readmitted). CONCLUSIONS: Prevention of suicide attempt and self-harm among women following acute medical illness may be improved by screening for sex-specific predictors including perinatal mental health history.

National Child Mental Health Quality Measures: Adherence Rates and Extent of Evidence for Clinical Validity.

PURPOSE OF REVIEW: To provide an overview of the selection process and annual updates of the child mental health measures within the Child Core Set, describe national and statewide adherence rates, and summarize findings from a systematic literature review examining measure adherence rates and whether adherence is associated with improved clinical outcomes. RECENT FINDINGS: Five national quality measures target child mental health care processes. On average, national adherence varied widely by state, and performance did not substantially improve during the past 5 years. Mean national adherence rates for the two measures related to timeliness of care were below 50%. For each measure, scientific evidence to support the association between adherence and improved clinical outcomes was scarce. Investment in academic-agency partnered research to standardize methods for publicly reporting adherence to national child mental health quality measures and validation of these measures should be a national priority for child healthcare research.

Identifying Precise Targets to Improve Child Mental Health Care Equity: Leveraging Advances in Clinical Research Informatics and Lived Experience.

To reduce child mental health disparities, it is imperative to improve the precision of targets and to expand our vision of social determinants of health as modifiable. Advancements in clinical research informatics and please state accurate measurement of child mental health service use and quality. Participatory action research promotes representation of underserved groups in informatics research and practice and may improve the effectiveness of interventions by informing research across all stages, including the identification of key variables, risk and protective factors, and data interpretation.

Detection of Suicidal Behavior and Self-harm Among Children Presenting to Emergency Departments: A Tree-based Classification Approach.

Suicide is the second leading cause of death of U.S. children over 10 years old. Application of statistical learning to structured EHR data may improve detection of children with suicidal behavior and self-harm. Classification trees (CART) were developed and cross-validated using mental health-related emergency department (MH-ED) visits (2015-2019) of children 10-17 years (N=600) across two sites. Performance was compared with the CDC Surveillance Case Definition ICD-10-CM code list. Gold-standard was child psychiatrist chart review. Visits were suicide-related among 284/600 (47.3%) children. ICD-10-CM detected cases with sensitivity 70.7 (95%CI 67.0-74.3), specificity 99.0 (98.8-100), and 85/284 (29.9%) false negatives. CART detected cases with sensitivity 85.1 (64.7-100) and specificity 94.9 (89.2-100). Strongest predictors were suicide-related code, MH- and suicide-related chief complaints, site, area deprivation index, and depression. Diagnostic codes miss nearly one-third of children with suicidal behavior and self-harm. Advances in EHR-based phenotyping have the potential to improve detection of childhood-onset suicidality.

The impact of exercise in improving executive function impairments among children and adolescents with ADHD, autism spectrum disorder, and fetal alcohol spectrum disorder: a systematic review and meta-analysis

Abstract Objective: he goal of this work was to perform a systematic review and meta-analysis evaluating and comparing exercise related improvements in various executive function (EF) domains among children and adolescents with attention-deficit hyperactivity disorder (ADHD), Autism Spectrum Disorders (ASD), and Fetal Alcohol Spectrum Disorders (FASD). Methods: A systematic literature research was conducted in PubMed, CENTRAL, and PsycInfo from October 1st, 2018 through January 30th, 2019 for original peer-reviewed articles investigating the relationship between exercise interventions and improvements in three domains of executive function (working memory, attention/set shifting, and response inhibition) among children and adolescents with ADHD, ASD, and FASD. Effect sizes (ES) were extracted and combined with random-effects meta-analytic methods. Covariates and moderators were then analyzed using meta-regression and subgroup analyses. Results: A total of 28 studies met inclusion criteria, containing information on 1,281 youth (N=1197 ADHD, N= 54 ASD, N=30 FASD). For ADHD, exercise interventions were associated with moderate improvements in attention/set-shifting (ES 0.38, 95% CI 0.01-0.75, k=14) and approached significance for working memory (ES 0.35, 95%CI −0.17-0.88, k=5) and response inhibition (ES 0.39, 95%CI −0.02-0.80, k=12). For ASD and FASD, exercise interventions were associated with large improvements in working memory (ES 1.36, 95%CI 1.08-1.64) and response inhibition (ES 0.78, 95%CI 0.21-1.35) and approached significance for attention/set-shifting (ES 0.69, 95% −0.28-1.66). There was evidence of substantial methodologic and substantive heterogeneity among studies. Sample size, mean age, study design, and the number or duration of intervention sessions did not significantly moderate the relationship between exercise and executive function. Conclusion: Exercise interventions among children and adolescents with neurodevelopmental disorders were associated with moderate improvements in executive function domains. Of note, studies of youth with ASD and FASD tended to report higher effect sizes compared to studies of youth with ADHD, albeit few existing studies. Exercise may be a potentially cost-effective and readily implementable intervention to improve executive function in these populations.