Mental Health Emergency Department Visits by Children Before and During the COVID-19 Pandemic.

OBJECTIVES: To describe pediatric mental health emergency department (ED) visit rates and visit characteristics before and during the COVID-19 pandemic. METHODS: We conducted a cross-sectional study of ED visits by children 5-17 years old with a primary mental health diagnosis from March 2018 to February 2021 at a 10-hospital health system and a children's hospital in the Chicago area. We compared demographic and clinical characteristics of children with mental health ED visits before and during the pandemic. We conducted an interrupted time series analysis to determine changes in visit rates. RESULTS: We identified 8,127 pediatric mental health ED visits (58.5% female, 54.3% White, Not Hispanic/Latino and 42.4% age 13-15). During the pandemic, visits for suicide or self-injury increased 6.69% (95% CI 4.73, 8.65), and visits for disruptive, impulse control, conduct disorders increased 1.94% (95% CI 0.85, 3.03). Mental health ED visits by children with existing mental health diagnoses increased 2.29% (95% CI 0.34, 4.25). Mental health ED visits that resulted in medical admission increased 4.32% (95% CI 3.11, 5.53). The proportion of mental health ED visits at community hospitals increased by 5.49% (95% CI 3.31, 7.67). Mental health ED visit rates increased at the onset of the pandemic (adjusted incidence rate ratio [aIRR] 1.27, 95% CI 1.06, 1.50), followed by a monthly increase thereafter (aIRR 1.04, 95% CI 1.02, 1.06). CONCLUSION: Mental health ED visit rates by children increased during the COVID-19 pandemic. Changes in mental health ED visit characteristics during the pandemic may inform interventions to improve children's mental health.

Implementation of automatic data extraction from an enterprise database warehouse (EDW) for validating pediatric VTE decision rule: a prospective observational study in a critical care population.

Multiple clinical risk prediction tools for hospital acquired venous thromboembolism (HA-VTE) have been developed. The objectives of this study were to develop and assess the feasibility of data extraction from Electronic Medical Records (EMR) from an enterprise database warehouse (EDW) and to test the validity of a previously developed Pediatric Clot Decision Rule (PCDR). This single-center prospective observational cohort study was conducted between March 2016 and March 2017 and included eligible patients admitted to the intensive care units. Risk score was calculated using the PCDR tool. Sensitivity, specificity, positive and negative predicted value (PPV and NPV) were calculated based on a cut-point of 3. A total of 2822 children were eligible for analysis and 5.1% (95% CI 4.2-6.2) children had a PCDR score of 3. Children with PCDR score of ≥ 3 had a 3 times higher odd of developing VTE compared to those with scores < 3 (OR 3.1; 95% CI 1.93-4.80; p < 0.001). The model performance showed that at the cutoff point of ≥ 3, both the specificity and sensitivity of the PCDR in predicting VTE was 69% and NPV of 98%. We successfully demonstrated using our EDW to populate a research database using an automatic data import. A PCDR score of ≥ 3 was associated with VTE. Collaboration through large registries will be useful in informing practices and guidelines for rare disorders such as pediatric VTE.

Automated data extraction: merging clinical care with real-time cohort-specific research and quality improvement data.

BACKGROUND/PURPOSE: Although prohibitively labor intensive, manual data extraction (MDE) is the prevailing method used to obtain clinical research and quality improvement (QI) data. Automated data extraction (ADE) offers a powerful alternative. The purposes of this study were to 1) assess the feasibility of ADE from provider-authored outpatient documentation, and 2) evaluate the effectiveness of ADE compared to MDE. METHODS: A prospective collection of data was performed on 90 ADE-templated notes (N=71 patients) evaluated in our bowel management clinic. ADE captured data were compared to 59 MDE notes (N=51) collected under an IRB-exempt review. Sixteen variables were directly comparable between ADE and MDE. RESULTS: MDE for 59 clinic notes (27 unique variables) took 6months to complete. ADE-templated notes for 90 clinic notes (154 unique variables) took 5min to run a research/QI report. Implementation of ADE included eight weeks of development and testing. Pre-implementation clinical documentation was similar to post-implementation documentation (5-10min). CONCLUSIONS: ADE-templated notes allow for a 5-fold increase in clinically relevant data that can be captured with each encounter. ADE also results in real-time data extraction to a research/QI database that is easily queried. The immediate availability of these data, in a research-formatted spreadsheet, allows for rapid collection, analyses, and interpretation of the data. LEVEL OF EVIDENCE: IV. TYPE OF STUDY: Retrospective Study.

Metabolic alterations in children with obstructive sleep apnea.

IMPORTANCE: The incidence of obesity is rising in the United States and has been linked to Obstructive Sleep Apnea (OSA) even in young children. Understanding the role that obesity and OSA play in alterations in metabolic variables that can lead to serious health issues is essential to the care and counseling of affected children. OBJECTIVES: To evaluate the association of alterations in metabolic variables, including insulin resistance, to OSA in young, obese children. DESIGN: Retrospective, case-control series. SETTING: Tertiary care children's hospital. PARTICIPANTS: Obese children aged 2-12 years who had undergone overnight polysomography and routine laboratory testing for lipid levels, fasting glucose, and insulin from January 1, 2006 to December 31, 2012 were identified from a TransMed Bio-Integration Suite and Epic's clarity database search. RESULTS: A total of 76 patients were included for analysis. Forty-three (56.6%) were male, and the mean age was 8.3±2.5 years (range, 2.4-11.9 years). The mean body mass index (BMI) z score was 2.8±0.75 (range, 1.7-6.3), and all patients were obese (BMI z score>95th percentile). Twenty two patients (28.9%) had an apnea-hypopnea index (AHI) <1/h (no OSA), 27 (35.5%) an AHI≥1<5/h, 12 (15.8%) had an AHI ≥5<9.99/h, and 15 (19.7%) had an AHI≥10/h. There was no significant difference in total cholesterol, triglycerides, high and low density lipoprotein levels, systolic and diastolic blood pressure in those patients with or without OSA. Fasting insulin, blood glucose, and homeostasis model assessment (HOMA) were significantly higher in patients with OSA compared to those with no OSA (p<0.01). AHI correlated to alterations in insulin as well as glucose homeostasis on multivariate analysis. Results from logistic regression analysis showed that fasting insulin (p<0.01), and HOMA (p<0.01) predicted severe OSA independent of age, gender, and BMI z score in these patients. CONCLUSION: Metabolic alterations in glucose and insulin levels, known to be associated with obesity and increased risk for cardiovascular disease, appear to relate to the severity of OSA in young children.

Physician assessment of disease activity in JIA subtypes. Analysis of data extracted from electronic medical records.

OBJECTIVE: Although electronic medical records (EMRs) have facilitated care for children with juvenile idiopathic arthritis (JIA), analyses of treatment outcomes have required paper based or manually re-entered data. We have started EMR discrete data entry for JIA patient visits, including joint examination and global assessment, by physician and patient. In this preliminary study, we extracted data from the EMR to Xenobase™ (TransMed Systems, Inc., Cupertino, CA), an application permitting cohort analyses of the relationship between global assessment to joint examination and subtype. METHODS: During clinic visits, data were entered into discrete fields in ambulatory visit forms in the EMR (EpicCare™, Epic Systems, Verona, WI). Data were extracted using Clarity Reports, then de-identified and uploaded for analyses to Xenobase™. Parameters included joint examination, ILAR diagnostic classification, physician global assessment, patient global assessment, and patient pain score. Data for a single visit for each of 160 patients over a 2 month period, beginning March, 2010, were analyzed. RESULTS: In systemic JIA patients, strong correlations for physician global assessment were found with pain score, joint count and patient assessment. In contrast, physician assessment for patients with persistent oligoarticular and rheumatoid factor negative patients showed strong correlation with joint counts, but only moderate correlation with pain scores and patient global assessment. Conversely, for enthesitis patients, physician assessment correlated strongly with pain scores, and moderately with joint count and patient global assessment. Rheumatoid factor positive patients, the smallest group studied, showed moderate correlation for all three measures. Patient global assessment for systemic patients showed strong correlations with pain scores and joint count, similar to data for physician assessment. For polyarticular and enthesitis patients, correlation of patient global assessment with pain scores was strong. Moderate correlations were found between patient global assessment and joint count in oligoarticular and polyarticular patients. CONCLUSION: Data extraction from the EMR is feasible and useful to evaluate JIA patients for indicators of treatment responsiveness. In this pilot study, we found correlates for physician global assessment of arthritis differed, according to disease subtype. Further data extraction and analyses will determine if these findings can be confirmed, and will assess other outcome measures, compare longitudinal responses to treatment, and export extracted data to multi-center databases.