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.

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.

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.

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.

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.

Characterization and Outcomes of Hospitalized Children With Coronavirus Disease 2019: A Report From a Multicenter, Viral Infection and Respiratory Illness Universal Study (Coronavirus Disease 2019) Registry.

OBJECTIVES: Multicenter data on the characteristics and outcomes of children hospitalized with coronavirus disease 2019 are limited. Our objective was to describe the characteristics, ICU admissions, and outcomes among children hospitalized with coronavirus disease 2019 using Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study: Coronavirus Disease 2019 registry. DESIGN: Retrospective study. SETTING: Society of Critical Care Medicine Viral Infection and Respiratory Illness Universal Study (Coronavirus Disease 2019) registry. PATIENTS: Children (< 18 yr) hospitalized with coronavirus disease 2019 at participating hospitals from February 2020 to January 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome was ICU admission. Secondary outcomes included hospital and ICU duration of stay and ICU, hospital, and 28-day mortality. A total of 874 children with coronavirus disease 2019 were reported to Viral Infection and Respiratory Illness Universal Study registry from 51 participating centers, majority in the United States. Median age was 8 years (interquartile range, 1.25-14 yr) with a male:female ratio of 1:2. A majority were non-Hispanic (492/874; 62.9%). Median body mass index (n = 817) was 19.4 kg/m2 (16-25.8 kg/m2), with 110 (13.4%) overweight and 300 (36.6%) obese. A majority (67%) presented with fever, and 43.2% had comorbidities. A total of 238 of 838 (28.2%) met the Centers for Disease Control and Prevention criteria for multisystem inflammatory syndrome in children, and 404 of 874 (46.2%) were admitted to the ICU. In multivariate logistic regression, age, fever, multisystem inflammatory syndrome in children, and pre-existing seizure disorder were independently associated with a greater odds of ICU admission. Hospital mortality was 16 of 874 (1.8%). Median (interquartile range) duration of ICU (n = 379) and hospital (n = 857) stay were 3.9 days (2-7.7 d) and 4 days (1.9-7.5 d), respectively. For patients with 28-day data, survival was 679 of 787, 86.3% with 13.4% lost to follow-up, and 0.3% deceased. CONCLUSIONS: In this observational, multicenter registry of children with coronavirus disease 2019, ICU admission was common. Older age, fever, multisystem inflammatory syndrome in children, and seizure disorder were independently associated with ICU admission, and mortality was lower among children than mortality reported in adults.

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.

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.

Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models.

OBJECTIVES: Assess a machine learning method of serially updated mortality risk. DESIGN: Retrospective analysis of a national database (Health Facts; Cerner Corporation, Kansas City, MO). SETTING: Hospitals caring for children in ICUs. PATIENTS: A total of 27,354 admissions cared for in ICUs from 2009 to 2018. INTERVENTIONS: None. MAIN OUTCOME: Hospital mortality risk estimates determined at 6-hour time periods during care in the ICU. Models were truncated at 180 hours due to decreased sample size secondary to discharges and deaths. MEASUREMENTS AND MAIN RESULTS: The Criticality Index, based on physiology, therapy, and care intensity, was computed for each admission for each time period and calibrated to hospital mortality risk (Criticality Index-Mortality [CI-M]) at each of 29 time periods (initial assessment: 6 hr; last assessment: 180 hr). Performance metrics and clinical validity were determined from the held-out test sample (n = 3,453, 13%). Discrimination assessed with the area under the receiver operating characteristic curve was 0.852 (95% CI, 0.843-0.861) overall and greater than or equal to 0.80 for all individual time periods. Calibration assessed by the Hosmer-Lemeshow goodness-of-fit test showed good fit overall (p = 0.196) and was statistically not significant for 28 of the 29 time periods. Calibration plots for all models revealed the intercept ranged from--0.002 to 0.009, the slope ranged from 0.867 to 1.415, and the R2 ranged from 0.862 to 0.989. Clinical validity assessed using population trajectories and changes in the risk status of admissions (clinical volatility) revealed clinical trajectories consistent with clinical expectations and greater clinical volatility in deaths than survivors (p < 0.001). CONCLUSIONS: Machine learning models incorporating physiology, therapy, and care intensity can track changes in hospital mortality risk during intensive care. The CI-M's framework and modeling method are potentially applicable to monitoring clinical improvement and deterioration in real time.

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.

The Association of Laboratory Test Abnormalities With Mortality Risk in Pediatric Intensive Care.

OBJECTIVES: To determine the bivariable associations between abnormalities of 28 common laboratory tests and hospital mortality and determine how mortality risks changes when the ranges are evaluated in the context of commonly used laboratory test panels. DESIGN: A 2009-2016 cohort from the Health Facts (Cerner Corporation, Kansas City, MO) database. SETTING: Hospitals caring for children in ICUs. PATIENTS: Children cared for in ICUs with laboratory data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 2,987,515 laboratory measurements in 71,563 children. The distribution of laboratory test values in 10 groups defined by population percentiles demonstrated the midrange of tests was within the normal range except for those measured predominantly when significant abnormalities are suspected. Logistic regression analysis at the patient level combined the population-based groups into ranges with nonoverlapping mortality odds ratios. The most deviant test ranges associated with increased mortality risk (mortality odds ratios > 5.0) included variables associated with acidosis, coagulation abnormalities and blood loss, immune function, liver function, nutritional status, and the basic metabolic profile. The test ranges most associated with survival included normal values for chloride, pH, and bicarbonate/total Co2. When the significant test ranges from bivariable analyses were combined in commonly used test panels, they generally remained significant but were reduced as risk was distributed among the tests. CONCLUSIONS: The relative importance of laboratory test ranges vary widely, with some ranges strongly associated with mortality and others strongly associated with survival. When evaluated in the context of test panels rather than isolated tests, the mortality odds ratios for the test ranges decreased but generally remained significant as risk was distributed among the components of the test panels. These data are useful to develop critical values for children in ICUs, to identify risk factors previously underappreciated, for education and training, and for future risk score development.

Severity Trajectories of Pediatric Inpatients Using the Criticality Index.

OBJECTIVES: To assess severity of illness trajectories described by the Criticality Index for survivors and deaths in five patient groups defined by the sequence of patient care in ICU and routine patient care locations. DESIGN: The Criticality Index developed using a calibrated, deep neural network, measures severity of illness using physiology, therapies, and therapeutic intensity. Criticality Index values in sequential 6-hour time periods described severity trajectories. SETTING: Hospitals with pediatric inpatient and ICU care. PATIENTS: Pediatric patients never cared for in an ICU (n = 20,091), patients only cared for in the ICU (n = 2,096) and patients cared for in both ICU and non-ICU care locations (n = 17,023) from 2009 to 2016 Health Facts database (Cerner Corporation, Kansas City, MO). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Criticality Index values were consistent with clinical experience. The median (25-75th percentile) ICU Criticality Index values (0.878 [0.696-0.966]) were more than 80-fold higher than the non-ICU values (0.010 [0.002-0.099]). Non-ICU Criticality Index values for patients transferred to the ICU were 40-fold higher than those never transferred to the ICU (0.164 vs 0.004). The median for ICU deaths was higher than ICU survivors (0.983 vs 0.875) (p < 0.001). The severity trajectories for the five groups met expectations based on clinical experience. Survivors had increasing Criticality Index values in non-ICU locations prior to ICU admission, decreasing Criticality Index values in the ICU, and decreasing Criticality Index values until hospital discharge. Deaths had higher Criticality Index values than survivors, steeper increases prior to the ICU, and worsening values in the ICU. Deaths had a variable course, especially those who died in non-ICU care locations, consistent with deaths associated with both active therapies and withdrawals/limitations of care. CONCLUSIONS: Severity trajectories measured by the Criticality Index showed strong validity, reflecting the expected clinical course for five diverse patient groups.

Criticality: A New Concept of Severity of Illness for Hospitalized Children.

OBJECTIVES: To validate the conceptual framework of "criticality," a new pediatric inpatient severity measure based on physiology, therapy, and therapeutic intensity calibrated to care intensity, operationalized as ICU care. DESIGN: Deep neural network analysis of a pediatric cohort from the Health Facts (Cerner Corporation, Kansas City, MO) national database. SETTING: Hospitals with pediatric routine inpatient and ICU care. PATIENTS: Children cared for in the ICU (n = 20,014) and in routine care units without an ICU admission (n = 20,130) from 2009 to 2016. All patients had laboratory, vital sign, and medication data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A calibrated, deep neural network used physiology (laboratory tests and vital signs), therapy (medications), and therapeutic intensity (number of physiology tests and medications) to model care intensity, operationalized as ICU (versus routine) care every 6 hours of a patient's hospital course. The probability of ICU care is termed the Criticality Index. First, the model demonstrated excellent separation of criticality distributions from a severity hierarchy of five patient groups: routine care, routine care for those who also received ICU care, transition from routine to ICU care, ICU care, and high-intensity ICU care. Second, model performance assessed with statistical metrics was excellent with an area under the curve for the receiver operating characteristic of 0.95 for 327,189 6-hour time periods, excellent calibration, sensitivity of 0.817, specificity of 0.892, accuracy of 0.866, and precision of 0.799. Third, the performance in individual patients with greater than one care designation indicated as 88.03% (95% CI, 87.72-88.34) of the Criticality Indices in the more intensive locations was higher than the less intense locations. CONCLUSIONS: The Criticality Index is a quantification of severity of illness for hospitalized children using physiology, therapy, and care intensity. This new conceptual model is applicable to clinical investigations and predicting future care needs.

Coronavirus Disease 2019-Associated PICU Admissions: A Report From the Society of Critical Care Medicine Discovery Network Viral Infection and Respiratory Illness Universal Study Registry.

OBJECTIVES: To compare clinical characteristics and outcomes of children admitted to the PICU for severe acute respiratory syndrome coronavirus 2-related illness with or without multisystem inflammatory syndrome in children. The secondary objective was to identify explanatory factors associated with outcome of critical illness defined by a composite index of in-hospital mortality and organ system support requirement. DESIGN: Retrospective cohort study. SETTING: Thirty-eight PICUs within the Viral Infection and Respiratory Illness Universal Study registry from March 2020 to January 2021. PATIENTS: Children less than 18 years with severe acute respiratory syndrome coronavirus 2-related illness with or without multisystem inflammatory syndrome in children. MEASUREMENTS AND MAIN RESULTS: Of 394 patients, 171 (43.4%) had multisystem inflammatory syndrome in children. Children with multisystem inflammatory syndrome in children were more likely younger (2-12 yr vs adolescents; p < 0.01), Black (35.6% vs 21.9%; p < 0.01), present with fever/abdominal pain than cough/dyspnea (p < 0.01), and less likely to have comorbidities (33.3% vs 61.9%; p < 0.01) compared with those without multisystem inflammatory syndrome in children. Inflammatory marker levels, use of inotropes/vasopressors, corticosteroids, and anticoagulants were higher in multisystem inflammatory syndrome in children patients (p < 0.01). Overall mortality was 3.8% (15/394), with no difference in the two groups. Diagnosis of multisystem inflammatory syndrome in children was associated with longer duration of hospitalization as compared to nonmultisystem inflammatory syndrome in children (7.5 d[interquartile range, 5-11] vs 5.3 d [interquartile range, 3-11 d]; p < 0.01). Critical illness occurred in 164 patients (41.6%) and was more common in patients with multisystem inflammatory syndrome in children compared with those without (55.6% vs 30.9%; p < 0.01). Multivariable analysis failed to show an association between critical illness and age, race, sex, greater than or equal to three signs and symptoms, or greater than or equal to two comorbidities among the multisystem inflammatory syndrome in children cohort. Among nonmultisystem inflammatory syndrome in children patients, the presence of greater than or equal to two comorbidities was associated with greater odds of critical illness (odds ratio 2.95 [95% CI, 1.61-5.40]; p < 0.01). CONCLUSIONS: This study delineates significant clinically relevant differences in presentation, explanatory factors, and outcomes among children admitted to PICU with severe acute respiratory syndrome coronavirus 2-related illness stratified by multisystem inflammatory syndrome in children.

Medications for Children Receiving Intensive Care: A National Sample.

OBJECTIVE: To examine medication administration records through electronic health record data to provide a broad description of the pharmaceutical exposure of critically ill children. DESIGN: Retrospective cohort study using the Cerner Health Facts database. SETTING: United States. PATIENTS: A total of 43,374 children 7 days old to less than 22 years old receiving intensive care with available pharmacy data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 907,440 courses of 1,080 unique medications were prescribed with a median of nine medications (range, 1-99; 25-75th percentile, 5-16) per patient. The most common medications were acetaminophen, ondansetron, and morphine. Only 45 medications (4.2%) were prescribed to more than 5% of patients, and these accounted for 442,067 (48.7%) of the total courses of medications. Each additional medication was associated with increased univariate risk of mortality (odds ratio, 1.05; 95% CI, 1.05-1.06; p < 0.001). CONCLUSIONS: Children receiving intensive care receive a median of nine medications per patient and one quarter are prescribed at least than 16 medications. Only 45 medications were prescribed to more than 5% of patients, but these accounted for almost half of all medication courses.

Sedation, Analgesia, and Neuromuscular Blockade: An Assessment of Practices From 2009 to 2016 in a National Sample of 66,443 Pediatric Patients Cared for in the ICU.

OBJECTIVES: To describe the pharmaceutical management of sedation, analgesia, and neuromuscular blockade medications administered to children in ICUs. DESIGN: A retrospective analysis using data extracted from the national database Health Facts. SETTING: One hundred sixty-one ICUs in the United States with pediatric admissions. PATIENTS: Children in ICUs receiving medications from 2009 to 2016. EXPOSURE/INTERVENTION: Frequency and duration of administration of sedation, analgesia, and neuromuscular blockade medications. MEASUREMENTS AND MAIN RESULTS: Of 66,443 patients with a median age of 1.3 years (interquartile range, 0-14.5), 63.3% (n = 42,070) received nonopioid analgesic, opioid analgesic, sedative, and/or neuromuscular blockade medications consisting of 83 different agents. Opioid and nonopioid analgesics were dispensed to 58.4% (n = 38,776), of which nonopioid analgesics were prescribed to 67.4% (n = 26,149). Median duration of opioid analgesic administration was 32 hours (interquartile range, 7-92). Sedatives were dispensed to 39.8% (n = 26,441) for a median duration of 23 hours (interquartile range, 3-84), of which benzodiazepines were most common (73.4%; n = 19,426). Neuromuscular-blocking agents were dispensed to 17.3% (n = 11,517) for a median duration of 2 hours (interquartile range, 1-15). Younger age was associated with longer durations in all medication classes. A greater proportion of operative patients received these medication classes for a longer duration than nonoperative patients. A greater proportion of patients with musculoskeletal and hematologic/oncologic diseases received these medication classes. CONCLUSIONS: Analgesic, sedative, and neuromuscular-blocking medications were prescribed to 63.3% of children in ICUs. The durations of opioid analgesic and sedative medication administration found in this study can be associated with known complications, including tolerance and withdrawal. Several medications dispensed to pediatric patients in this analysis are in conflict with Food and Drug Administration warnings, suggesting that there is potential risk in current sedation and analgesia practice that could be reduced with practice changes to improve efficacy and minimize risks.