New Morbidities During Critical Illness and Associated Risk of ICU Readmission: Virtual Pediatric Systems Cohort, 2017-2020.

OBJECTIVES: To describe change in Functional Status Scale (FSS) associated with critical illness and assess associated development of new morbidities with PICU readmission. DESIGN: Retrospective, cross-sectional cohort study using the Virtual Pediatric Systems (VPS; Los Angeles, CA) database. SETTING: One hundred twenty-six U.S. PICUs participating in VPS. SUBJECTS: Children younger than 21 years old admitted 2017-2020 and followed to December 2022. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 40,654 patients, 86.2% were classified as having good function or mild dysfunction before illness. Most patients did not have a change in their FSS category during hospitalization. Survival with new morbidity occurred most in children with baseline good/mild dysfunction (8.7%). Hospital mortality increased across categories of baseline dysfunction. Of 39,701 survivors, 14.2% were readmitted within 1 year. Median time to readmission was 159 days. In multivariable, mixed-effects Cox modeling, time to readmission was most associated with discharge functional status (hazard ratio [HR], 5.3 [95% CI, 4.6-6.1] for those with very severe dysfunction), and associated with lower hazard in those who survived with new morbidity (HR, 0.7 [95% CI, 0.6-0.7]). CONCLUSIONS: Development of new morbidities occurs commonly in pediatric critical illness, but we failed to find an association with greater hazard of PICU readmission. Instead, patient functional status is associated with hazard of PICU readmission.

Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children. DESIGN: Observational cohort study. SETTING: PICU. PATIENTS: Children age between 6 and 18 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Continuous minute-by-minute physiologic data were available for a total of 493 critically ill children admitted to a tertiary care PICU over an 8-month period, 20 of whom developed severe sepsis. Using an alert time stamp generated by an electronic screening algorithm as a reference point, we studied up to 24 prior hours of continuous physiologic data. We identified physiomarkers, including SD of heart rate, systolic and diastolic blood pressure, and symbolic transitions probabilities of those variables that discriminated severe sepsis patients from controls (all other patients admitted to the PICU who did not meet severe sepsis criteria). We used logistic regression, random forests, and deep Convolutional Neural Network methods to derive our models. Analysis was performed using data generated in two windows prior to the firing of the electronic screening algorithm, namely, 2-8 and 8-24 hours. When analyzing the physiomarkers present in the 2-8 hours analysis window, logistic regression performed with specificity of 87.4% and sensitivity of 55.0%, random forest performed with 79.6% specificity and 80.0% sensitivity, and the Convolutional Neural Network performed with 83.0% specificity and 75.0% sensitivity. When analyzing physiomarkers from the 8-24 hours window, logistic regression resulted in 77.1% specificity and 39.3% sensitivity, random forest performed with 82.3% specificity and 61.1% sensitivity, whereas the Convolutional Neural Network method achieved 81% specificity and 76% sensitivity. CONCLUSIONS: Artificial intelligence can be used to predict the onset of severe sepsis using physiomarkers in critically ill children. Further, it may detect severe sepsis as early as 8 hours prior to a real-time electronic severe sepsis screening algorithm.

Diversity of release patterns for jail detainees: implications for public health interventions.

OBJECTIVES: We sought to develop a detailed description of the variety of jail release patterns and to learn what factors affect the length of stay (LOS). METHODS: The main data set for the study came from a biennial Bureau of Justice Statistics survey on felony defendants in large urban counties. RESULTS: The median LOS for the felony defendants was 7 days. One quarter of the jails had a median LOS of less than 2 days; median LOS for 75% of the jails was less than 15 days. Median regression showed that male gender, previous arrests, and violent charges were predictive of longer LOS. CONCLUSIONS: The diversity in release patterns among jails has not been previously described. A public health intervention feasible in one jail may not be feasible in another because of the heterogeneity of release patterns. Individual inmate characteristics could predict a slower rate of release.

Outcome Measures Following Critical Illness in Children With Disabilities: A Scoping Review.

Children with disabilities compose a substantial portion of admissions and bed-days in the pediatric intensive care unit (PICU) and often experience readmissions over time. Impacts of a PICU admission on post-discharge health status may be difficult to distinguish from pre-existing disability in this population. Efforts to standardize outcome measures used for children with disabilities may help identify morbidities associated with PICU hospitalizations. Although a scoping review of outcome measures to assess children after episodes of critical illness has recently been published, it is not known to what extent these measures are appropriate for use in children with disabilities. This limits our ability to effectively measure long-term outcomes following critical illness in this important patient population. Through mixed methodology of scoping review and multi-stakeholder consensus, we aimed to identify and describe instruments previously utilized for this purpose and to explore additional tools for consideration. This yielded 51 measures across a variety of domains that have been utilized in the PICU setting and may be appropriate for use in children with disabilities. We describe characteristics of these instruments, including the type of developmental domains assessed, availability of population data, validation and considerations regarding administration in children with disabilities, and ease of availability of the instrument to researchers. Additionally, we suggest needed alterations or accommodations for these instruments to augment their utility in these populations, and highlight areas for future instrument development.

Severe Neonatal Coronavirus Disease 2019 Presenting as Acute Respiratory Distress Syndrome.

Since initial identification of severe acute respiratory syndrome coronavirus 2 in 2019, the virus has proved to be highly transmissible, resulting in a global pandemic with emerging reports of infected neonates. This report highlights a severe case of neonatal coronavirus disease 2019 with acute respiratory distress syndrome.