Recommendation for Long-term Management of Adult Attention-Deficit/Hyperactivity Disorder in Military Populations, Veterans, and Dependents: A Narrative Review.

INTRODUCTION: In addition to the higher burden of mental health disease in the military, there is a compounding antecedent association between behavioral health comorbidities and the treatment of attention-deficit/hyperactivity disorder (ADHD) in this population. Despite the low prevalence of new-onset ADHD in adults globally, the rate of stimulant (i.e., amphetamines) prescription is increasing. Stimulants can exacerbate mental health disease (often masquerading as ADHD symptomatology), precluding optimal treatment of the underlying etiology and imposing unnecessary dangerous side effects. This study aimed to evaluate the long-term safety and efficacy of stimulants for managing adult ADHD. METHODS: A nine-member multidisciplinary team reviewed a PubMed search with the terms "adult," "ADHD," and "stimulant." Targeted PubMed and Google Scholar searches for "adult ADHD" paired with Food and Drug Administration -approved ADHD medications and Google Scholar literature using forward and reverse snowballing methods were performed for high-quality studies focusing on long-term treatment in ADHD. An evidence table and clinical algorithm were developed from the review. RESULTS: Of the 1,039 results, 50 articles were fully reviewed, consisting of 21 descriptive and experimental studies, 18 observational, and 11 systematic reviews and meta-analyses. Illustrative cases within the structured discussion of the results highlighted ADHD and psychiatric comorbidities, risks, harms, and benefits of stimulant use, medication mechanisms of action, and limitations of the current evidence. DISCUSSION: The dearth of high-quality studies on long-term ADHD management in adults fails to establish a causal relationship between stimulant use and physiological harm. Despite mixed evidence supporting the benefit of stimulants, there is clear evidence regarding the risk of harm. The serious risks of stimulants include arrhythmias, myocardial infarction, stroke/transient ischemic attack, sudden death, psychosis, and worsening of behavioral health disease. Additionally, there is a possible long-term risk of harm due to chronic sympathetic load (i.e., cardiovascular system remodeling). Stimulants pose a greater risk for addiction and abuse compared to other evidence-based nonstimulant medications that have similar effectiveness. Both stimulants and nonstimulants might promote favorable neuroanatomical changes for long-term improvement of ADHD symptoms, but nonstimulants (atomoxetine) have the pharmacological advantage of also mitigating the effects of sympathetic load (sympatholysis) and anxiety (anxiolysis). Given the physiological uncertainty of extended stimulant use for adults, especially older adults with vulnerable cardiovascular systems, clinicians should proceed cautiously when considering initiating or sustaining stimulant therapy. For long-term treatment of ADHD in adults, clinicians should consider nonstimulant alternatives (including behavioral therapy) due to the comparatively lower side effect risk and the possible additional benefit in patients with behavioral health comorbidities. CONCLUSION: Long-term safety of stimulant use for adults with ADHD is uncertain, as existing studies are limited in quality and duration. This is particularly important for military populations with higher rates of mental health conditions. Managing ADHD and related conditions requires prioritizing cardiovascular safety, especially for older adults. Nonstimulant options can be helpful, especially in comorbid psychiatric disease. Before treating ADHD, ruling out and controlling other behavioral health conditions is essential to avoid masking or worsening underlying issues and reducing unnecessary medication side effects.

Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS.

Suicide is the 10th leading cause of death in the U.S (1999-2019). However, predicting when someone will attempt suicide has been nearly impossible. In the modern world, many individuals suffering from mental illness seek emotional support and advice on well-known and easily-accessible social media platforms such as Reddit. While prior artificial intelligence research has demonstrated the ability to extract valuable information from social media on suicidal thoughts and behaviors, these efforts have not considered both severity and temporality of risk. The insights made possible by access to such data have enormous clinical potential-most dramatically envisioned as a trigger to employ timely and targeted interventions (i.e., voluntary and involuntary psychiatric hospitalization) to save lives. In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS). In particular, we employ two deep learning approaches: time-variant and time-invariant modeling, for user-level suicide risk assessment, and evaluate their performance against a clinician-adjudicated gold standard Reddit corpus annotated based on the C-SSRS. Our results suggest that the time-variant approach outperforms the time-invariant method in the assessment of suicide-related ideations and supportive behaviors (AUC:0.78), while the time-invariant model performed better in predicting suicide-related behaviors and suicide attempt (AUC:0.64). The proposed approach can be integrated with clinical diagnostic interviews for improving suicide risk assessments.

Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study.

BACKGROUND: In clinical diagnostic interviews, mental health professionals (MHPs) implement a care practice that involves asking open questions (eg, "What do you want from your life?" "What have you tried before to bring change in your life?") while listening empathetically to patients. During these interviews, MHPs attempted to build a trusting human-centered relationship while collecting data necessary for professional medical and psychiatric care. Often, because of the social stigma of mental health disorders, patient discomfort in discussing their presenting problem may add additional complexities and nuances to the language they use, that is, hidden signals among noisy content. Therefore, a focused, well-formed, and elaborative summary of clinical interviews is critical to MHPs in making informed decisions by enabling a more profound exploration of a patient's behavior, especially when it endangers life. OBJECTIVE: The aim of this study is to propose an unsupervised, knowledge-infused abstractive summarization (KiAS) approach that generates summaries to enable MHPs to perform a well-informed follow-up with patients to improve the existing summarization methods built on frequency heuristics by creating more informative summaries. METHODS: Our approach incorporated domain knowledge from the Patient Health Questionnaire-9 lexicon into an integer linear programming framework that optimizes linguistic quality and informativeness. We used 3 baseline approaches: extractive summarization using the SumBasic algorithm, abstractive summarization using integer linear programming without the infusion of knowledge, and abstraction over extractive summarization to evaluate the performance of KiAS. The capability of KiAS on the Distress Analysis Interview Corpus-Wizard of Oz data set was demonstrated through interpretable qualitative and quantitative evaluations. RESULTS: KiAS generates summaries (7 sentences on average) that capture informative questions and responses exchanged during long (58 sentences on average), ambiguous, and sparse clinical diagnostic interviews. The summaries generated using KiAS improved upon the 3 baselines by 23.3%, 4.4%, 2.5%, and 2.2% for thematic overlap, Flesch Reading Ease, contextual similarity, and Jensen Shannon divergence, respectively. On the Recall-Oriented Understudy for Gisting Evaluation-2 and Recall-Oriented Understudy for Gisting Evaluation-L metrics, KiAS showed an improvement of 61% and 49%, respectively. We validated the quality of the generated summaries through visual inspection and substantial interrater agreement from MHPs. CONCLUSIONS: Our collaborator MHPs observed the potential utility and significant impact of KiAS in leveraging valuable but voluminous communications that take place outside of normally scheduled clinical appointments. This study shows promise in generating semantically relevant summaries that will help MHPs make informed decisions about patient status.