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.

Codesign of remote data collection for chronic management of pediatric home mechanical ventilation.

INTRODUCTION: Outpatient monitoring of children using invasive home mechanical ventilation (IHMV) is recommended, but access to care can be difficult. This study tested if remote (home-based) data collection was feasible and acceptable in chronic IHMV management. METHODS: A codesign study was conducted with an IHMV program, home nurses, and English- and Spanish-speaking parent-guardians of children using IHMV (0-17 years; n = 19). After prototyping, parents used a remote patient monitoring (RPM) bundle to collect patient heart rate, respiratory rate (RR), oxygen saturation, end-tidal carbon dioxide (EtCO2 ), and ventilator pressure/volume over 8 weeks. User feedback was analyzed using qualitative methods and the System Usability Scale (SUS). Expected marginal mean differences within patient measures when awake, asleep, or after a break were calculated using mixed effects models. RESULTS: Patients were a median 2.9 years old and 11 (58%) took breaks off the ventilator. RPM data were entered on a mean of 83.7% (SD ± 29.1%) weeks. SUS scores were 84.8 (SD ± 10.5) for nurses and 91.8 (SD ± 10.1) for parents. Over 90% of parents agreed/strongly agreed that RPM data collection was feasible and relevant to their child's care. Within-patient comparisons revealed that EtCO2 (break-vs-asleep 2.55 mmHg, d = 0.79 [0.42-1.15], p < .001; awake-vs-break 1.48, d = -0.49 [0.13-0.84], p = .02) and RR (break-vs-asleep 16.14, d = 2.12 [1.71-2.53], p < .001; awake-vs-break 3.44, d = 0.45 [0.10-0.04], p = .03) were significantly higher during ventilator breaks. CONCLUSIONS: RPM data collection in children with IHMV was feasible, acceptable, and captured clinically meaningful vital sign changes during ventilator breaks, supporting the clinical utility of RPM in IHMV management.

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.

Paying Family Medical Caregivers for Children's Home Healthcare in Colorado: A Working Medicaid Model.

OBJECTIVE: To compare the characteristics and healthcare use of children with medical complexity who receive paid certified nursing assistant (CNA) care by a family member (family CNA) and by a traditional nonfamily member (nonfamily CNA). STUDY DESIGN: This was retrospective cohort study of children who received CNA care through Colorado's Medicaid paid family caregiving program between 2017 and 2019 by a home healthcare agency. We compared patient characteristics between the family CNA and nonfamily CNA groups. A multivariable Poisson regression model was used to compare hospitalization rates (days in the hospital per year), adjusting for patient age patient sex, nursing care, and complex chronic condition. RESULTS: Of 861 patients, 79% (n = 680) received family CNA care and 21% (n = 181) received nonfamily CNA care. Patient demographics and hospitalization did not differ between the groups, although patients who had family CNAs were less likely to receive additional nursing-level care (42% vs 60%, P < .01). Family and nonfamily CNA caregivers had similar characteristics, except that family CNA caregivers had substantially better 3-year retention (82% vs 9%, P < .01) despite lower average hourly pay ($14.60 vs $17.60 per hour, P < .01). Hospitalizations were rare (<10% of patients). In the adjusted model, patients who received family CNA care experienced 1 more hospitalized day per year, compared with patients who received nonfamily CNA care (P < .001). CONCLUSIONS: Paid family caregivers provided CAN-level care to children with medical complexity with a greater employee retention compared with nonfamily CNA caregivers, with marginally different hospitalization rates using a family-centered approach. This model may help address workforce shortages while also providing income to family caregivers.

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.

Hyperinflation and its association with successful transition to home ventilator devices in infants with chronic respiratory failure and severe bronchopulmonary dysplasia.

OBJECTIVE: To estimate the association between lung hyperinflation and the time to successful transition to home ventilators in infants with sBPD and chronic respiratory failure. DESIGN/METHODS: Infants with sBPD <32 weeks' gestation who received tracheostomies were identified. Hyperinflation was the main exposure. Time from tracheostomy to successful transition to the home ventilator was the main outcome. Kaplan-Meier and multivariable Cox proportional hazards were used to estimate the relationships between hyperinflation and the main outcome. RESULTS: Sixty-two infants were included; 26 (42%) were hyperinflated. Eleven died before transition, and 51 successfully transitioned. Hyperinflation was associated with both mortality (31% vs 8.3%, p = 0.02) and an increased duration (72 vs. 56 days) to successful transition (hazard ratio (HR) = 0.38, 95% CI: 0.19, 0.76, p = 0.006). Growth velocity was similar after tracheostomy placement. CONCLUSIONS: In infants with chronic respiratory failure and sBPD <32 weeks' gestation, hyperinflation is related to mortality and inpatient morbidities.

Children With Medical Complexity and Mental and Behavioral Disorders in the Emergency Department.

BACKGROUND: To assess the overlap and admission or transfer rate of children with chronic complex conditions (CCC) and with mental or behavioral health (MBH) disorders among children presenting to the emergency department (ED). METHODS: We performed a cross-sectional analysis from 2 data sources: hospitals in the Pediatric Health Information System (PHIS) and from a statewide sample (Illinois COMPdata). We included ED encounters 2 to 21 years and compared differences in admission and/or transfer between subgroups. Among patients with both a CCC and MBH, we evaluated if a primary MBH diagnosis was associated with admission or transfer. RESULTS: There were 11 880 930 encounters in the PHIS dataset; 0.7% had an MBH and CCC, 2.2% had an MBH, and 8.0% had a CCC. Patients with an MBH and CCC had a greater need for admission or transfer (86.5%) compared with patients with an MBH alone (57.7%) or CCC alone (52.0%). Among 5 362 701 patients in the COMPdata set, 0.2% had an MBH and CCC, 2.1% had an MBH, and 3.2% had a CCC, with similar admission or transfer needs between groups (61.8% admission or transfer with CCC and MBH; 42.8% MBH alone, and 27.3% with CCC alone). Within both datasets, patients with both a MBH and CCC had a higher odds of admission or transfer when their primary diagnosis was an MBH disorder. CONCLUSIONS: While accounting for a small proportion of ED patients, CCC with concomitant MBH have a higher need for admission or transfer relative to other patients.

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.

Measuring Social Health Following Pediatric Critical Illness: A Scoping Review and Conceptual Framework.

OBJECTIVE: Social health is an important component of recovery following critical illness as modeled in the pediatric Post-Intensive Care Syndrome framework. We conducted a scoping review of studies measuring social outcomes (measurable components of social health) following pediatric critical illness and propose a conceptual framework of the social outcomes measured in these studies. DATA SOURCES: PubMed, EMBASE, PsycINFO, CINAHL, and the Cochrane Registry. STUDY SELECTION: We identified studies evaluating social outcomes in pediatric intensive care unit (PICU) survivors or their families from 1970-2017 as part of a broader scoping review of outcomes after pediatric critical illness. DATA EXTRACTION: We identified articles by dual review and dual-extracted study characteristics, instruments, and instrument validation and administration information. For instruments used in studies evaluating a social outcome, we collected instrument content and described it using qualitative methods adapted to a scoping review. DATA SYNTHESIS: Of 407 articles identified in the scoping review, 223 (55%) evaluated a social outcome. The majority were conducted in North America and the United Kingdom, with wide variation in methodology and population. Among these studies, 38 unique instruments were used to evaluate a social outcome. Specific social outcomes measured included individual (independence, attachment, empathy, social behaviors, social cognition, and social interest), environmental (community perceptions and environment), and network (activities and relationships) characteristics, together with school and family outcomes. While many instruments assessed more than one social outcome, no instrument evaluated all areas of social outcome. CONCLUSIONS: The full range of social outcomes reported following pediatric critical illness were not captured by any single instrument. The lack of a comprehensive instrument focused on social outcomes may contribute to under-appreciation of the importance of social outcomes and their under-representation in PICU outcomes research. A more comprehensive evaluation of social outcomes will improve understanding of overall recovery following pediatric critical illness.

Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years.

Importance: Readmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children. Objective: To develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages. Design, Setting, and Participants: This population-based prognostic analysis used electronic health record-derived data from a freestanding children's hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021. Main Outcomes and Measures: Three models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges. Results: The derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort. Conclusions and Relevance: In this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.

The increasing proportion of adult discharges at children's hospitals, 2004-2019.

Significant medical advances now enable individuals with pediatric illnesses to survive into adulthood. Finding medical homes for these individuals often remains challenging. We utilized the Pediatric Health Information System to measure the variation in and growth of admissions to children's hospitals, stratified by age and payor from 2004 to 2019. We identified 8,097,081 patient encounters from 30 hospitals. Compared to children, adults discharged at children's hospitals are more likely to have a complex chronic condition, have a higher median cost, and have a longer median length of stay. Hospital-level adult discharges ranged from 1.9% to 10.1% (median 4.1%; interquartile range: 2.8%-5.4%). Significantly higher increases were seen in each adult age subgroup (18-20, 21-25, and >25 years old) compared to the pediatric age group (p < .001). The number of adults discharged from children's hospitals is increasing faster than children, impacting children's hospitals and the populations they serve.

At-home end-tidal carbon dioxide measurement in children with invasive home mechanical ventilation.

BACKGROUND: Carbon dioxide concentration trending is used in chronic management of children with invasive home mechanical ventilation (HMV) in clinical settings, but options for end-tidal carbon dioxide (EtCO2 ) monitoring at home are limited. We hypothesized that a palm-sized, portable endotracheal capnograph (PEC) that measures EtCO2 could be adapted for in-home use in children with HMV. METHODS: We evaluated the internal consistency of the PEC by calculating an intraclass correlation coefficient of three back-to-back breaths by children (0-17 years) at baseline health in the clinic. Pearson's correlation was calculated for PEC EtCO2 values with concurrent mean values of in-clinic EtCO2 and transcutaneous CO2 (TCM) capnometers. The Bland-Altman test determined their level of agreement. Qualitative interviews and surveys assessed usability and acceptability by family-caregivers at home. RESULTS: CO2 values were collected in awake children in varied activity levels and positions (N = 30). The intraclass correlation coefficient for the PEC was 0.95 (p < 0.05). The correlation between the PEC and in-clinic EtCO2 device was 0.85 with a mean difference of -3.8 mmHg and precision of ±1.1 mmHg. The correlation between the PEC and the clinic TCM device was 0.92 with a mean difference of 0.2 mmHg and precision of ±1.0. Family-caregivers (N = 10) trialed the PEC at home; all were able to obtain measurements at home while children were awake and sometimes asleep. CONCLUSIONS: A portable, noninvasive device for measuring EtCO2 was feasible and acceptable, with values that trend similarly to currently in-practice, outpatient models. These devices may facilitate monitoring of EtCO2 at home in children with invasive HMV.

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.

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.

High-Frequency Utilization of the PICU.

OBJECTIVES: To describe the demographic, clinical, outcome, and cost differences between children with high-frequency PICU admission and those without. DESIGN: Retrospective, cross-sectional cohort study. SETTING: United States. PATIENTS: Children less than or equal to 18 years old admitted to PICUs participating in the Pediatric Health Information System database in 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We assessed survivors of PICU admissions for repeat PICU admissions within a year of their index visit. Children with greater than or equal to 3 PICU admissions within a year were classified as high-frequency PICU utilization (HFPICU). We compared demographic, clinical, outcome, and cost characteristics between children with HFPICU and those with only an index or two admissions per year (nHFPICU). Of 95,465 children who survived an index admission, 5,880 (6.2%) met HFPICU criteria. HFPICU patients were more frequently younger, technology dependent, and publicly insured. HFPICU patients had longer lengths of stay and were more frequently discharged to a rehabilitation facility or with home nursing services. HFPICU patients accounted for 24.8% of annual hospital utilization costs among patients requiring PICU admission. Time to readmission for children with HFPICU was 58% sooner (95% CI, 56-59%) than in those with nHFPICU with two admissions using an accelerated failure time model. Among demographic and clinical factors that were associated with development of HFPICU status calculated from a multivariable analysis, the greatest effect size was for time to first readmission within 82 days. CONCLUSIONS: Children identified as having HFPICU account for 6.2% of children surviving an index ICU admission. They are a high-risk patient population with increased medical resource utilization during index and subsequent ICU admissions. Patients readmitted within 82 days of discharge should be considered at higher risk of HFPICU status. Further research, including validation and exploration of interventions that may be of use in this patient population, are necessary.

The Child With Severe Chronic Illness in the ICU: A Concise Review.

OBJECTIVES: Children with severe chronic illness are a prevalent, impactful, vulnerable group in PICUs, whose needs are insufficiently met by transitory care models and a narrow focus on acute care needs. Thus, we sought to provide a concise synthetic review of published literature relevant to them and a compilation of strategies to address their distinctive needs. DATA SOURCES: English language articles were identified in MEDLINE using a variety of phrases related to children with chronic conditions, prolonged admissions, resource utilization, mortality, morbidity, continuity of care, palliative care, and other critical care topics. Bibliographies were also reviewed. STUDY SELECTION: Original articles, review articles, and commentaries were considered. DATA EXTRACTION: Data from relevant articles were reviewed, summarized, and integrated into a narrative synthetic review. DATA SYNTHESIS: Children with serious chronic conditions are a heterogeneous group who are growing in numbers and complexity, partly due to successes of critical care. Because of their prevalence, prolonged stays, readmissions, and other resource use, they disproportionately impact PICUs. Often more than other patients, critical illness can substantially negatively affect these children and their families, physically and psychosocially. Critical care approaches narrowly focused on acute care and transitory/rotating care models exacerbate these problems and contribute to ineffective communication and information sharing, impaired relationships, subpar and untimely decision-making, patient/family dissatisfaction, and moral distress in providers. Strategies to mitigate these effects and address these patients' distinctive needs include improving continuity and communication, primary and secondary palliative care, and involvement of families. However, there are limited outcome data for most of these strategies and little consensus on which outcomes should be measured. CONCLUSIONS: The future of pediatric critical care medicine is intertwined with that of children with serious chronic illness. More concerted efforts are needed to address their distinctive needs and study the effectiveness of strategies to do so.