Emergency department visits and boarding for pediatric patients with suicidality before and during the COVID-19 pandemic.

OBJECTIVE: To quantify the increase in pediatric patients presenting to the emergency department with suicidality before and during the COVID-19 pandemic, and the subsequent impact on emergency department length of stay and boarding. METHODS: This retrospective cohort study from June 1, 2016, to October 31, 2022, identified patients ages 6 to 21 presenting to the emergency department at a pediatric academic medical center with suicidality using ICD-10 codes. Number of emergency department encounters for suicidality, demographic characteristics of patients with suicidality, and emergency department length of stay were compared before and during the COVID-19 pandemic. Unobserved components models were used to describe monthly counts of emergency department encounters for suicidality. RESULTS: There were 179,736 patient encounters to the emergency department during the study period, 6,215 (3.5%) for suicidality. There were, on average, more encounters for suicidality each month during the COVID-19 pandemic than before the COVID-19 pandemic. A time series unobserved components model demonstrated a temporary drop of 32.7 encounters for suicidality in April and May of 2020 (p<0.001), followed by a sustained increase of 31.2 encounters starting in July 2020 (p = 0.003). The average length of stay for patients that boarded in the emergency department with a diagnosis of suicidality was 37.4 hours longer during the COVID-19 pandemic compared to before the COVID-19 pandemic (p<0.001). CONCLUSIONS: The number of encounters for suicidality among pediatric patients and the emergency department length of stay for psychiatry boarders has increased during the COVID-19 pandemic. There is a need for acute care mental health services and solutions to emergency department capacity issues.

Achieving Interoperable Datasets in Pediatrics: A Data Integration Approach.

Despite their increased secondary value for developing applications and knowledge gain, routine, harmonized and standardized datasets are often not available in Pediatrics. We propose a data integration pipeline towards an interoperable routine dataset in pediatric intensive care medicine. Our three-level approach involves identifying relevant data from primary source systems, developing local data integration processes, and converting data into a standardized, interoperable format using openEHR. We modeled 15 openEHR templates and established 31 interoperable ETL processes, resulting in anonymized, standardized data of about 4,200 pediatric patients that were loaded into a harmonized database. Based on our pipeline and templates, we successfully integrated the first part of this data in our openEHR data repository. We seek to inspire other pediatric intensive care units to adopt similar approaches, with the aim of breaking down heterogenous data silos and promoting secondary use of routine data.

Tracing the Progression of Sepsis in Critically Ill Children: Clinical Decision Support for Detection of Hematologic Dysfunction.

BACKGROUND: One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinician's ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work. Simultaneously, detection of OD by using computerized decision support approaches has been scarcely investigated, especially not in pediatrics. OBJECTIVES: The aim of the study is to enhance an existing, interoperable, and rule-based CDSS prototype for tracing the progression of sepsis in critically ill children by augmenting it with the capability to detect SIRS/sepsis-associated hematologic OD, and to determine its diagnostic accuracy. METHODS: We reproduced an interoperable CDSS approach previously introduced by our working group: (1) a knowledge model was designed by following the commonKADS methodology, (2) routine care data was semantically standardized and harmonized using openEHR as clinical information standard, (3) rules were formulated and implemented in a business rule management system. Data from a prospective diagnostic study, including 168 patients, was used to estimate the diagnostic accuracy of the rule-based CDSS using the clinicians' diagnoses as reference. RESULTS: We successfully enhanced an existing interoperable CDSS concept with the new task of detecting SIRS/sepsis-associated hematologic OD. We modeled openEHR templates, integrated and standardized routine data, developed a rule-based, interoperable model, and demonstrated its accuracy. The CDSS detected hematologic OD with a sensitivity of 0.821 (95% CI: 0.708-0.904) and a specificity of 0.970 (95% CI: 0.942-0.987). CONCLUSION: We could confirm our approach for designing an interoperable CDSS as reproducible and transferable to other critical diseases. Our findings are of direct practical relevance, as they present one of the first interoperable CDSS modules that detect pediatric SIRS/sepsis-associated hematologic OD.

Towards an Evolutionary Open Pediatric Intensive Care Dataset in the ELISE Project.

BACKGROUND: To embrace the need for freely accessible training data sets originating from the real world, in the ELISE project, we integrate source data from a pediatric intensive care unit and provide it to researchers. OBJECTIVE: We present our vision, initial results and steps on a trail towards an evolutionary open pediatric intensive care data set. METHODS: Our evolution plan for the data set comprises three steps. The final data set will include raw clinical data and labels on critical outcomes such as organ dysfunction and sepsis, generated automatically by computerized and well-evaluated methods. RESULTS: First step resulted in an initial version data set available in a central repository. CONCLUSIONS: Our approach has great potential to provide a comprehensive open intensive care data set labeled for critical pediatric outcomes and, thus, contributing to overcome the current lack of real-world pediatric intensive care data usable for training data-driven algorithms.