Literature review: Substance use screening and co-morbidity in medically hospitalized youth

Emergency department management of opioid use disorder in pediatric patients

Opioid use disorder (OUD) has emerged as a significant public health crisis affecting individuals across all age groups. However, there remains a critical gap in understanding the specific nuances and challenges associated with OUD in pediatric populations. This article provides a comprehensive review of the epidemiology, definition of OUD, screening recommendations for OUD, and evidence-based management strategies for OUD in pediatric patients.

Alternative psychopharmacologic treatments for pediatric catatonia: a retrospective analysis

Introduction

Pediatric catatonia is a highly co-morbid condition with treatment options often limited to electroconvulsive therapy (ECT) or lorazepam. However, lorazepam may not be readily available, and access to ECT is limited by restrictive legislation and stigma. This study aims to provide alternative treatment options for pediatric catatonia.

Methods

The study involved a single-site retrospective analysis of a private university hospital in the southern United States. The study included patients under eighteen with catatonia who received psychopharmacologic treatment with an agent other than lorazepam. The patients were evaluated with the Bush-Francis Catatonia Rating Scale (BFCRS), Kanner Catatonia Severity Scale (KCS), and Kanner Catatonia Examination (KCE) at the time of initial evaluation and upon stabilization. A retrospective clinical global impressions-improvement (CGI-I) score was assigned by four authors.

Results

102 pediatric patients diagnosed with catatonia were identified, and 31 met criteria for the study. 20 (65%) were white, 6 (19%) were Black, 4 (13%) were Hispanic, and 1 (3%) were Indian. Most patients (N = 18; 58%) were insured by Medicaid. The mean age at the time of catatonia diagnosis was 13.5 years. All patients were stabilized on either clonazepam or diazepam, with 21 (68%) requiring treatment with an additional medication of either an anti-epileptic, N-methyl-D-aspartate (NMDA) receptor antagonist, and aripiprazole or clozapine. Statistically significant reductions in the BFCRS [t = 11.2, df = 30, std = 6.3, p < 0.001, 95% CI = (7.8, 15.1)], KCS [t = 4.6, df = 38, p < 0.001, 95% CI = (12.0, 31.0)], and KCE [t = 7.8, df = 30, std = 1.8, p < 0.001, 95% CI = (1.9, 3.2)] were observed. For CGI-I the results showed that the estimated probability of observing a score better than no change (>4) is 0.976 [t.s. = 43.2, p < 0.001, 95% CI = (0.931,0.992)], indicating the average subject is expected to experience some improvement.

Discussion

In conclusion, all patients responded to these treatments with improvement in their catatonic symptoms. Alternative pharmacologic interventions for catatonia, including benzodiazepines other than lorazepam, valproic acid, NMDA receptor antagonists, and atypical antipsychotics were safe and effective in treating catatonia in this population.

Examining the clinical correlates of conduct disorder in youth with bipolar disorder

BACKGROUND

Conduct Disorder (CD) is highly comorbid with Bipolar Disorder (BP) and this comorbidity is associated with high morbidity and dysfunction. We sought to better understand the clinical characteristics and familiality of comorbid BP + CD by examining children with BP with and without co-morbid CD.

METHODS

357 subjects with BP were derived from two independent datasets of youth with and without BP. All subjects were evaluated with structured diagnostic interviews, the Child Behavior Checklist (CBCL), and neuropsychological testing. We stratified the sample of subjects with BP by the presence or absence of CD and compared the two groups on measures of psychopathology, school functioning, and neurocognitive functioning. First-degree relatives of subjects with BP +/- CD were compared on rates of psychopathology in relatives.

RESULTS

Subjects with BP + CD compared to BP without CD had significantly more impaired scores on the CBCL Aggressive Behavior (p < 0.001), Attention Problems (p = 0.002), Rule-Breaking Behavior (p < 0.001), Social Problems (p < 0.001), Withdrawn/Depressed clinical scales (p = 0.005), the Externalizing Problems (p < 0.001), and Total Problems composite scales(p < 0.001). Subjects with BP + CD had significantly higher rates of oppositional defiant disorder (ODD) (p = 0.002), any SUD (p < 0.001), and cigarette smoking (p = 0.001). First-degree relatives of subjects with BP + CD had significantly higher rates of CD/ODD/ASPD and cigarette smoking compared to first-degree relatives of subjects without CD.

LIMITATIONS

The generalization of our findings was limited due to a largely homogeneous sample and no CD only comparison group.

CONCLUSIONS

Given the deleterious outcomes associated with comorbid BP + CD, further efforts in identification and treatment are necessary.

The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design

BACKGROUND

Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system.

OBJECTIVE

This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use.

METHODS

A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models.

RESULTS

The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025.

CONCLUSIONS

The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42971.