Risk factors predicting need for the pediatric intensive care unit (PICU) post-hematopoietic cell transplant, PICU utilization, and outcomes following HCT: a single center retrospective analysis.

Hematopoietic cell transplant (HCT) is a curative treatment for multiple malignant and non-malignant disorders. While morbidity and mortality have decreased significantly over the years, some patients still require management in the pediatric intensive care unit (PICU) during their HCT course for additional respiratory, cardiovascular, and/or renal support. We retrospectively reviewed pediatric patients (0-18 years) who underwent HCT from January 2015-December 2020 at our institution to determine risk factors for PICU care and evaluate PICU utilization and outcomes. We also assessed pulmonary function testing (PFT) data to determine if differences were noted between PICU and non-PICU patients as well as potential evolution of pulmonary dysfunction over time. Risk factors of needing PICU care were lower age, lower weight, having an underlying inborn error of metabolism, and receiving busulfan-based conditioning. Nearly half of PICU encounters involved use of each of respiratory support types including high-flow nasal cannula, non-invasive positive pressure ventilation, and mechanical ventilation. Approximately one-fifth of PICU encounters involved renal replacement therapy. Pulmonary function test results largely did not differ between PICU and non-PICU patients at any timepoint aside from individuals who required PICU care having lower DLCO scores at one-year post-HCT. Future directions include consideration of combining our data with other centers for a multi-center retrospective analysis with the goal of gathering and reporting additional multi-center data to work toward continuing to decrease morbidity and mortality for patients undergoing HCT.

The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.

OBJECTIVE: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. DESIGN: Scoping review and expert opinion. SETTING: We queried CINAHL Plus with Full Text (EBSCO), Cochrane Library (Wiley), Embase (Elsevier), Ovid Medline, and PubMed for articles published between 2000 and 2022 related to machine learning concepts and pediatric critical illness. Articles were excluded if the majority of patients were adults or neonates, if unsupervised machine learning was the primary methodology, or if information related to the development, validation, and/or implementation of the model was not reported. Article selection and data extraction were performed using dual review in the Covidence tool, with discrepancies resolved by consensus. SUBJECTS: Articles reporting on the development, validation, or implementation of supervised machine learning models in the field of pediatric critical care medicine. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 5075 identified studies, 141 articles were included. Studies were primarily (57%) performed at a single site. The majority took place in the United States (70%). Most were retrospective observational cohort studies. More than three-quarters of the articles were published between 2018 and 2022. The most common algorithms included logistic regression and random forest. Predicted events were most commonly death, transfer to ICU, and sepsis. Only 14% of articles reported external validation, and only a single model was implemented at publication. Reporting of validation methods, performance assessments, and implementation varied widely. Follow-up with authors suggests that implementation remains uncommon after model publication. CONCLUSIONS: Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.

Comparing two definitions of pediatric complexity among children cared for in general and pediatric emergency departments in a statewide sample.

Objective: The number of children cared for in emergency departments (EDs) with medical complexity continues to rise. We sought to identify the concordance between 2 commonly used criteria of medical complexity among children presenting to a statewide sample of EDs. Methods: We conducted a retrospective cross-sectional study of children presenting to a statewide sample of Illinois EDs between 2016 and 2021. We classified patients as having medical complexity when using 2 definitions (≥1 pediatric Complex Chronic Condition [CCC] or complex chronic disease using the Pediatric Medical Complexity Algorithm [PMCA]) and compared their overlap and clinical outcomes. Results: Of 6,550,296 pediatric ED encounters, CCC criteria and PMCA criteria were met in 217,609 (3.3%) and 175,708 (2.7%) encounters, respectively. Among patients with complexity, 100,015 (34.1%) met both criteria, with moderate agreement (κ = 0.49). Children with complexity by CCC had similar rates of presentation to a pediatric hospital (16.3% vs 14.8%), admission (28.5% vs 33.7%), ICU stay (10.0% vs 10.1%), and in-hospital mortality (0.5% vs 0.5%) compared to children with complexity by PMCA. The most common visit diagnoses for children with CCCs were related to sickle cell disease with crisis (3.9%), abdominal pain (3.6%), and non-specific chest pain (2.7%). The most common diagnoses by PMCA were related to depressive disorders (4.9%), sickle cell disease with crisis (4.8%), and seizures (3.2%). Conclusions and Relevance: The CCC and PMCA criteria of multisystem complexity identified different populations, with moderate agreement. Careful selection of operational definitions is required for proper application and interpretation in clinical and health services research.

Machine learning models to predict and benchmark PICU length of stay with application to children with critical bronchiolitis.

OBJECTIVE: To create models for prediction and benchmarking of pediatric intensive care unit (PICU) length of stay (LOS) for patients with critical bronchiolitis. HYPOTHESIS: We hypothesize that machine learning models applied to an administrative database will be able to accurately predict and benchmark the PICU LOS for critical bronchiolitis. DESIGN: Retrospective cohort study. PATIENTS: All patients less than 24-month-old admitted to the PICU with a diagnosis of bronchiolitis in the Pediatric Health Information Systems (PHIS) Database from 2016 to 2019. METHODOLOGY: Two random forest models were developed to predict the PICU LOS. Model 1 was developed for benchmarking using all data available in the PHIS database for the hospitalization. Model 2 was developed for prediction using only data available on hospital admission. Models were evaluated using R2 values, mean standard error (MSE), and the observed to expected ratio (O/E), which is the total observed LOS divided by the total predicted LOS from the model. RESULTS: The models were trained on 13,838 patients admitted from 2016 to 2018 and validated on 5254 patients admitted in 2019. While Model 1 had superior R2 (0.51 vs. 0.10) and (MSE) (0.21 vs. 0.37) values compared to Model 2, the O/E ratios were similar (1.18 vs. 1.20). Institutional median O/E (LOS) ratio was 1.01 (IQR 0.90-1.09) with wide variability present between institutions. CONCLUSIONS: Machine learning models developed using an administrative database were able to predict and benchmark the length of PICU stay for patients with critical bronchiolitis.

Child Opportunity Index and Emergent PICU Readmissions: A Retrospective, Cross-Sectional Study of 43 U.S. Hospitals.

OBJECTIVES: To examine the association between a validated composite measure of neighborhood factors, the Child Opportunity Index (COI), and emergent PICU readmission during the year following discharge for survivors of pediatric critical illness. DESIGN: Retrospective cross-sectional study. SETTING: Forty-three U.S. children's hospitals contributing to the Pediatric Health Information System administrative dataset. PATIENTS: Children (< 18 yr) with at least one emergent PICU admission in 2018-2019 who survived an index admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 78,839 patients, 26% resided in very low COI neighborhoods, 21% in low COI, 19% in moderate COI, 17% in high COI, and 17% in very high COI neighborhoods, and 12.6% had an emergent PICU readmission within 1 year. After adjusting for patient-level demographic and clinical factors, residence in neighborhoods with moderate, low, and very low COI was associated with increased odds of emergent 1-year PICU readmission relative to patients in very high COI neighborhoods. Lower COI levels were associated with readmission in diabetic ketoacidosis and asthma. We failed to find an association between COI and emergent PICU readmission in patients with an index PICU admission diagnosis of respiratory conditions, sepsis, or trauma. CONCLUSIONS: Children living in neighborhoods with lower child opportunity had an increased risk of emergent 1-year readmission to the PICU, particularly children with chronic conditions such as asthma and diabetes. Assessing the neighborhood context to which children return following critical illness may inform community-level initiatives to foster recovery and reduce the risk of adverse outcomes.

Defining Pediatric Chronic Critical Illness: A Scoping Review.

OBJECTIVES: Children with chronic critical illness (CCI) are hypothesized to be a high-risk patient population with persistent multiple organ dysfunction and functional morbidities resulting in recurrent or prolonged critical care; however, it is unclear how CCI should be defined. The aim of this scoping review was to evaluate the existing literature for case definitions of pediatric CCI and case definitions of prolonged PICU admission and to explore the methodologies used to derive these definitions. DATA SOURCES: Four electronic databases (Ovid Medline, Embase, CINAHL, and Web of Science) from inception to March 3, 2021. STUDY SELECTION: We included studies that provided a specific case definition for CCI or prolonged PICU admission. Crowdsourcing was used to screen citations independently and in duplicate. A machine-learning algorithm was developed and validated using 6,284 citations assessed in duplicate by trained crowd reviewers. A hybrid of crowdsourcing and machine-learning methods was used to complete the remaining citation screening. DATA EXTRACTION: We extracted details of case definitions, study demographics, participant characteristics, and outcomes assessed. DATA SYNTHESIS: Sixty-seven studies were included. Twelve studies (18%) provided a definition for CCI that included concepts of PICU length of stay (n = 12), medical complexity or chronic conditions (n = 9), recurrent admissions (n = 9), technology dependence (n = 5), and uncertain prognosis (n = 1). Definitions were commonly referenced from another source (n = 6) or opinion-based (n = 5). The remaining 55 studies (82%) provided a definition for prolonged PICU admission, most frequently greater than or equal to 14 (n = 11) or greater than or equal to 28 days (n = 10). Most of these definitions were derived by investigator opinion (n = 24) or statistical method (n = 18). CONCLUSIONS: Pediatric CCI has been variably defined with regard to the concepts of patient complexity and chronicity of critical illness. A consensus definition is needed to advance this emerging and important area of pediatric critical care research.

Variable Identification of Children With Medical Complexity in United States PICUs.

OBJECTIVES: Children with medical complexity are at increased risk for critical illness and adverse outcomes. However, there is currently no consensus definition of medical complexity in pediatric critical care research. DESIGN: Retrospective, cross-sectional cohort study. SETTING: One hundred thirty-one U.S. PICUs participating in the Virtual Pediatric Systems Database. SUBJECTS: Children less than 21 years old admitted from 2017 to 2019. Multisystem complexity was identified on the basis of two common definitions of medical complexity, Pediatric Complex Chronic Conditions (CCC), greater than or equal to 2 qualifying diagnoses, and Pediatric Medical Complexity Algorithm (PMCA), complex chronic disease. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 291,583 index PICU admissions, 226,430 (77.7%) met at least one definition of multisystem complexity, including 168,332 patients identified by CCC and 201,537 by PMCA. Of these, 143,439 (63.3%) were identified by both definitions. Cohen kappa was 0.39, indicating only fair agreement between definitions. Children identified by CCC were younger and were less frequently scheduled admissions and discharged home from the ICU than PMCA. The most common reason for admission was respiratory in both groups, although this represented a larger proportion of CCC patients. ICU and hospital length of stay were longer for patients identified by CCC. No difference in median severity of illness scoring was identified between definitions, but CCC patients had higher inhospital mortality. Readmission to the ICU in the subsequent year was seen in approximately one-fifth of patients in either group. CONCLUSIONS: Commonly used definitions of medical complexity identified distinct populations of children with multisystem complexity in the PICU with only fair agreement.

Association of Pediatric Mortality With the Child Opportunity Index Among Children Presenting to the Emergency Department.

OBJECTIVE: Child health and development is influenced by neighborhood context. The Child Opportunity Index (COI) is a multidimensional measure of neighborhood conditions. We sought to evaluate the association of COI with mortality among children presenting to the emergency department (ED). METHODS: We performed a multicenter cross-sectional study of pediatric (<18 years) ED encounters from a statewide dataset from 2016 to 2020. We constructed a multivariable logistic regression model to evaluate the association between COI and in-hospital mortality after adjusting for sociodemographic characteristics and medical complexity. RESULTS: Among 4,653,070 included encounters, in-hospital mortality occurred in 1855 (0.04%). There was a higher proportion of encounters with mortality in the lower COI categories relative to the higher COI categories (0.053%, 0.038%, 0.031%, 0.034%, 0.034% ranging from Very Low to Very High, respectively). In adjusted models, child residence in Low (adjusted odds ratio 1.26; 95% confidence interval [CI], 1.04-1.53) and Very Low (adjusted odds ratio 1.58; 95% CI, 1.31-1.90) COI neighborhoods was associated with mortality relative to residence in Very High COI neighborhoods. This association was noted across all domains of COI (education, health and environment, and social and economic), using an expanded definition of mortality, using nationally normed COI, and excluding patients with complex chronic conditions. Other factors associated with increased odds of mortality included age, medical complexity, payor status, age, and race and ethnicity. CONCLUSIONS: Understanding the association of neighborhood context on child mortality can inform public health interventions to improve child mortality rates and reduce disparities.

Hospitalizations at United States Children's Hospitals and Severity of Illness by Neighborhood Child Opportunity Index.

OBJECTIVE: To describe the association between neighborhood opportunity measured by the Child Opportunity Index 2.0 (COI) and patterns of hospital admissions and disease severity among children admitted to US pediatric hospitals. STUDY DESIGN: Retrospective, cross-sectional study of 773 743 encounters for children <18 years of age admitted to US children's hospitals participating in the Pediatric Health Information System database 7/2020-12/2021. RESULTS: The proportion of children from each COI quintile was inversely related to the degree of neighborhood opportunity. The difference between the proportion of patients from Very Low COI and Very High COI ranged from +32.0% (type 2 diabetes mellitus with complications) to -14.1% (mood disorders). The most common principal diagnoses were acute bronchiolitis, respiratory failure/insufficiency, chemotherapy, and asthma. Of the 45 diagnoses which occurred in ≥0.5% of the cohort, 22, including type 2 diabetes mellitus, asthma, and sleep apnea had higher odds of occurring in lower COI tiers in multivariable analysis. Ten diagnoses, including mood disorders, neutropenia, and suicide and intentional self-inflicted injury had lower odds of occurring in the lower COI tiers. The proportion of patients needing critical care and who died increased, as neighborhood opportunity decreased. CONCLUSIONS: Pediatric hospital admission diagnoses and severity of illness are disproportionately distributed across the range of neighborhood opportunity, and these differences persist after adjustment for factors including race/ethnicity and payor status, suggesting that these patterns in admissions reflect disparities in neighborhood resources and differential access to care.

Scoping Review: Neurocognitive Outcome Assessments After Critical Illness in Children.

OBJECTIVES: To map the literature regarding assessment of neurocognitive outcomes in PICU survivors. Secondary objectives were to identify literature gaps and to provide data for development of a Core Outcome Measures Set in the domain. METHODS: Planned, a priori analysis was performed of data from an over-all scoping review of Post-Intensive Care Syndrome-pediatrics (PICS-p) functional outcomes. English-language databases and registries from 1970 to 2017 were searched by a medical librarian to identify manuscripts reporting on Post Intensive Care Syndrome-pediatrics (PICS-p). Further, detailed data extraction for neurocognitive outcomes was performed focusing on study characteristics, instruments used, and populations. RESULTS: 114 instruments evaluated neurocognitive function in 183 manuscripts. 83% of manuscripts were published after 2000. Median of 3 (IQR 2-5) neurocognitive instruments per manuscript were reported. Wechsler Scales (45%), clinical neurologic evaluations (21%), Pediatric Cerebral Performance Category (20%), Bayley Scales of Infant Development (16%), and Vineland Adaptive Behavior Scales (11%) were the most commonly used instruments. Median sample size was 65 (IQR 32-129) subjects. Most (63%) assessments were conducted in-person and parents/guardians (40%) provided the information. Patients with congenital heart disease and traumatic brain injury were most commonly evaluated (31% and 24% of manuscripts, respectively). Adolescents were the most commonly studied age group (34%). Baseline function was infrequently assessed (11% of manuscripts); most studies assessed patients at only one time point after PICU discharge. Within studies, neurocognitive assessments were often combined with others - especially social (18%) and physical (8%). CONCLUSIONS: 183 manuscripts studied the neurocognitive domain of PICS-p. Studies were quantitative and tended to focus on populations with anticipated cognitive impairment. Considerable variability exists among the chosen 114 instruments used; however, 4 instruments were frequently chosen with focus on intelligence, cerebral functioning, and developmental and adaptive behavior. The literature is marked by lack of agreement on methodologies but reflects the burgeoning interest in studying PICS-p neurocognitive outcomes.

A Core Outcome Measurement Set for Pediatric Critical Care.

OBJECTIVES: To identify a PICU Core Outcome Measurement Set (PICU COMS), a set of measures that can be used to evaluate the PICU Core Outcome Set (PICU COS) domains in PICU patients and their families. DESIGN: A modified Delphi consensus process. SETTING: Four webinars attended by PICU physicians and nurses, pediatric surgeons, rehabilitation physicians, and scientists with expertise in PICU clinical care or research ( n = 35). Attendees were from eight countries and convened from the Pediatric Acute Lung Injury and Sepsis Investigators Pediatric Outcomes STudies after PICU Investigators and the Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network PICU COS Investigators. SUBJECTS: Measures to assess outcome domains of the PICU COS are as follows: cognitive, emotional, overall (including health-related quality of life), physical, and family health. Measures evaluating social health were also considered. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Measures were classified as general or additional based on generalizability across PICU populations, feasibility, and relevance to specific COS domains. Measures with high consensus, defined as 80% agreement for inclusion, were selected for the PICU COMS. Among 140 candidate measures, 24 were delineated as general (broadly applicable) and, of these, 10 achieved consensus for inclusion in the COMS (7 patient-oriented and 3 family-oriented). Six of the seven patient measures were applicable to the broadest range of patients, diagnoses, and developmental abilities. All were validated in pediatric populations and have normative pediatric data. Twenty additional measures focusing on specific populations or in-depth evaluation of a COS subdomain also met consensus for inclusion as COMS additional measures. CONCLUSIONS: The PICU COMS delineates measures to evaluate domains in the PICU COS and facilitates comparability across future research studies to characterize PICU survivorship and enable interventional studies to target long-term outcomes after critical illness.

SARS-CoV-2 With Concurrent Respiratory Viral Infection as a Risk Factor for a Higher Level of Care in Hospitalized Pediatric Patients.

OBJECTIVE: As of early 2021, there have been over 3.5 million pediatric cases of SARS-CoV-2, including 292 pediatric deaths in the United States. Although most pediatric patients present with mild disease, they are still at risk for developing significant morbidity requiring hospitalization and intensive care unit (ICU) level of care. This study was performed to evaluate if the presence of concurrent respiratory viral infections in pediatric patients admitted to the hospital with SARS-CoV-2 was associated with an increased rate of ICU level of care. DESIGN: A multicenter, international, noninterventional, cross-sectional study using data provided through The Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study database. SETTING: The medical ward and ICU of 67 participating hospitals. PATIENTS: Pediatric patients younger than 18 years hospitalized with SARS-CoV-2. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 922 patients were included. Among these patients, 391 required ICU level care and 31 had concurrent non-SARS-CoV-2 viral coinfection. In a multivariate analysis, after accounting for age, positive blood culture, positive sputum culture, preexisting chronic medical conditions, the presence of a viral respiratory coinfection was associated with need for ICU care (odds ratio, 3.6; 95% confidence interval, 1.6-9.4; P < 0.01). CONCLUSIONS: This study demonstrates an association between concurrent SARS-CoV-2 infection with viral respiratory coinfection and the need for ICU care. Further research is needed to identify other risk factors that can be used to derive and validate a risk-stratification tool for disease severity in pediatric patients with SARS-CoV-2.

A Narrative Review of Postoperative Anticoagulation Therapy for Congenital Cardiac Disease.

Congenital heart disease encompasses a range of cardiac birth defects. Some defects require early and complex surgical intervention and post-operative thromboprophylaxis primarily for valve, conduit, and shunt patency. Antiplatelet and anticoagulant management strategies vary considerably and may or may not align with recognized consensus practice guidelines. In addition, newer anticoagulant agents are being increasingly used in children, but these medications are not addressed in most consensus statements. This narrative review evaluated the literature from 2011 through 2021 on the topic of postoperative thromboprophylaxis after congenital heart disease operations. The search was focused on the descriptions and results of pediatric studies for replacement and/or repair of heart valves, shunts, conduits, and other congenital heart disease operations. Wide variability in practice exists and, as was true a decade ago, few randomized controlled trials have been conducted. Aspirin, warfarin, and perioperative heparin remain the most commonly used agents with varying dosing, duration, and monitoring strategies, making comparisons difficult. Only recently have data on direct oral anticoagulants been published in children, suggesting evolving paradigms of care. Our findings highlight the need for more research to strengthen the evidence for standardized thromboprophylaxis strategies.

Distribution of Emergency Department Encounters and Subsequent Hospital Admissions for Children by Child Opportunity Index.

OBJECTIVE: To evaluate differences in emergency department (ED) utilization and subsequent admission among children by Child Opportunity Index (COI). METHODS: We performed a cross-sectional study of pediatric (<18 years) encounters to 194 EDs in Illinois from 2016 to 2020. Each encounter was assigned to quntiles of COI 2.0 by postal code. We described the difference in the percent of encounters between lower (Very Low and Low) and higher (Very High and High) COI overall and among diagnoses with overrepresentation from lower COI groups. We evaluated the association of diagnosis with COI in ordinal models adjusted for demographics. RESULTS: There were 4,653,026 eligible ED encounters classified by COI as Very Low (28.6%), Low (24.8%), Moderate (20.3%), High (15.6%), and Very High (10.8%) (difference between low and high COI encounters 27.0%). Diagnoses with the greatest difference between low and high COI were eye infection, upper respiratory tract infections, and cough. The COI distribution for children admitted from the ED (n = 140,298) was 29.1% Very Low, 19.3% Low, 18.2% Moderate, 17.7% High, and 15.7% Very High (percent difference 15.1%). Diagnoses with the greatest differences between low and high COI among admitted patients were sickle cell crisis, asthma, and influenza. All ED diagnoses and 7/12 admission diagnoses were associated with lower COI in multivariable ordinal models. CONCLUSIONS: Children from lower COI areas are overrepresented in ED and inpatient encounters overall and within certain diagnosis groups. Further research is required to examine how health outcomes may be influenced by the structural and contextual characteristics of a child's neighborhood.

Demographic and Clinical Differences Between Applied Definitions of Medical Complexity.

OBJECTIVES: To identify the degree of concordance and characterize demographic and clinical differences between commonly used definitions of multisystem medical complexity in children hospitalized in children's hospitals. METHODS: We conducted a retrospective, cross-sectional cohort study of children <21 years of age hospitalized at 47 US Pediatric Health Information System-participating children's hospitals between January 2017 to December 2019. We classified patients as having multisystem complexity when using 3 definitions of medical complexity (pediatric complex chronic conditions, pediatric medical complexity algorithm, and pediatric chronic critical illness) and assessed their overlap. We compared demographic, clinical, outcome, cost characteristics, and longitudinal healthcare utilization for each grouping. RESULTS: Nearly one-fourth (23.5%) of children hospitalized at Pediatric Health Information System-participating institutions were identified as meeting at least 1 definition of multisystem complexity. Children with multisystem complexity ranged from 1.0% to 22.1% of hospitalized children, depending on the definition, with 31.2% to 95.9% requiring an ICU stay during their index admission. Differences were seen in demographic, clinical, and resource utilization patterns across the definitions. Definitions of multisystem complexity demonstrated poor agreement (Fleiss' κ 0.21), with 3.5% of identified children meeting all 3. CONCLUSIONS: Three definitions of multisystem complexity identified varied populations of children with complex medical needs, with poor overall agreement. Careful consideration is required when applying definitions of medical complexity in health services research, and their lack of concordance should result in caution in the interpretation of research using differing definitions of medical complexity.

Gastrointestinal Manifestations in Hospitalized Children With Acute SARS-CoV-2 Infection and Multisystem Inflammatory Condition: An Analysis of the VIRUS COVID-19 Registry.

BACKGROUND: Describe the incidence and associated outcomes of gastrointestinal (GI) manifestations of acute coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in hospitalized children (MIS-C). METHODS: Retrospective review of the Viral Infection and Respiratory Illness Universal Study registry, a prospective observational, multicenter international cohort study of hospitalized children with acute COVID-19 or MIS-C from March 2020 to November 2020. The primary outcome measure was critical COVID-19 illness. Multivariable models were performed to assess for associations of GI involvement with the primary composite outcome in the entire cohort and a subpopulation of patients with MIS-C. Secondary outcomes included prolonged hospital length of stay defined as being >75th percentile and mortality. RESULTS: Of the 789 patients, GI involvement was present in 500 (63.3%). Critical illness occurred in 392 (49.6%), and 18 (2.3%) died. Those with GI involvement were older (median age of 8 yr), and 18.2% had an underlying GI comorbidity. GI symptoms and liver derangements were more common among patients with MIS-C. In the adjusted multivariable models, acute COVID-19 was no associated with the primary or secondary outcomes. Similarly, despite the preponderance of GI involvement in patients with MIS-C, it was also not associated with the primary or secondary outcomes. CONCLUSIONS: GI involvement is common in hospitalized children with acute COVID-19 and MIS-C. GI involvement is not associated with critical illness, hospital length of stay or mortality in acute COVID-19 or MIS-C.

Epidemiology of Pediatric Critical Care Admissions in 43 United States Children's Hospitals, 2014-2019.

OBJECTIVES: To identify trends in the population of patients in PICUs over time. DESIGN: Cross-sectional, retrospective cohort study using the Pediatric Health Information System database. SETTING: Forty-three U.S. children's hospitals. PATIENTS: All patients admitted to Pediatric Health Information System-participating hospitals from January 2014 to December 2019. Individuals greater than 65 years old and normal newborns were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: PICU care occurred in 13.8% of all pediatric hospital encounters and increased over the study period from 13.3% to 14.3%. Resource intensity, based on average Hospitalization Resource Intensity Scores for Kids score, increased significantly across epochs (6.5 in 2014-2015 vs 6.9 in 2018-2019; p < 0.001), although this was not consistently manifested as additional procedural exposure. Geometric mean PICU cost per patient encounter was stable. The two most common disease categories in PICU patients were respiratory failure and cardiac and circulatory congenital anomalies. Of all PICU encounters, 35.5% involved mechanical ventilation, and 25.9% involved vasoactive infusions. Hospital-level variation in the percentage of days spent in the PICU ranged from 15.1% to 63.5% across the participating sites. Of the total hospital costs for patients admitted to the PICU, 41.7% of costs were accrued during the patients' PICU stay. CONCLUSIONS: The proportional use of PICU beds is increasing over time, although was variable across centers. Case-based resource use and complexity of pediatric patients are also increasing. Despite the higher use of PICU resources, the standardized costs of PICU care per patient encounter have remained stable. These data may help to inform current PICU resource allocation and future PICU capacity planning.

Clinical Decision Support in the PICU: Implications for Design and Evaluation.

OBJECTIVES: To assess the current landscape of clinical decision support (CDS) tools in PICUs in order to identify priority areas of focus in this field. DESIGN: International, quantitative, cross-sectional survey. SETTING: Role-specific, web-based survey administered in November and December 2020. SUBJECTS: Medical directors, bedside nurses, attending physicians, and residents/advanced practice providers at Pediatric Acute Lung Injury and Sepsis Network-affiliated PICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The survey was completed by 109 respondents from 45 institutions, primarily attending physicians from university-affiliated PICUs in the United States. The most commonly used CDS tools were people-based resources (93% used always or most of the time) and laboratory result highlighting (86%), with order sets, order-based alerts, and other electronic CDS tools also used frequently. The most important goal providers endorsed for CDS tools were a proven impact on patient safety and an evidence base for their use. Negative perceptions of CDS included concerns about diminished critical thinking and the burden of intrusive processes on providers. Routine assessment of existing CDS was rare, with infrequent reported use of observation to assess CDS impact on workflows or measures of individual alert burden. CONCLUSIONS: Although providers share some consensus over CDS utility, we identified specific priority areas of research focus. Consensus across practitioners exists around the importance of evidence-based CDS tools having a proven impact on patient safety. Despite broad presence of CDS tools in PICUs, practitioners continue to view them as intrusive and with concern for diminished critical thinking. Deimplementing ineffective CDS may mitigate this burden, though postimplementation evaluation of CDS is rare.