External evaluation of the Dynamic Criticality Index: A machine learning model to predict future need for ICU care in hospitalized pediatric patients.

OBJECTIVE: To assess the single site performance of the Dynamic Criticality Index (CI-D) models developed from a multi-institutional database to predict future care. Secondarily, to assess future care-location predictions in a single institution when CI-D models are re-developed using single-site data with identical variables and modeling methods. Four CI-D models were assessed for predicting care locations >6-12 hours, >12-18 hours, >18-24 hours, and >24-30 hours in the future. DESIGN: Prognostic study comparing multi-institutional CI-D models' performance in a single-site electronic health record dataset to an institution-specific CI-D model developed using identical variables and modelling methods. The institution did not participate in the multi-institutional dataset. PARTICIPANTS: All pediatric inpatients admitted from January 1st 2018 -February 29th 2020 through the emergency department. MAIN OUTCOME(S) AND MEASURE(S): The main outcome was inpatient care in routine or ICU care locations. RESULTS: A total of 29,037 pediatric hospital admissions were included, with 5,563 (19.2%) admitted directly to the ICU, 869 (3.0%) transferred from routine to ICU care, and 5,023 (17.3%) transferred from ICU to routine care. Patients had a median [IQR] age 68 months (15-157), 47.5% were female and 43.4% were black. The area under the receiver operating characteristic curve (AUROC) for the multi-institutional CI-D models applied to a single-site test dataset was 0.493-0.545 and area under the precision-recall curve (AUPRC) was 0.262-0.299. The single-site CI-D models applied to an independent single-site test dataset had an AUROC 0.906-0.944 and AUPRC range from 0.754-0.824. Accuracy at 0.95 sensitivity for those transferred from routine to ICU care was 72.6%-81.0%. Accuracy at 0.95 specificity was 58.2%-76.4% for patients who transferred from ICU to routine care. CONCLUSION AND RELEVANCE: Models developed from multi-institutional datasets and intended for application to individual institutions should be assessed locally and may benefit from re-development with site-specific data prior to deployment.

Hospital survival following pediatric HSCT: changes in complications, ICU therapies and outcomes over 10 years.

Introduction: Hematopoietic stem cell transplantation (HSCT) is an increasingly utilized therapy for malignant and non-malignant pediatric diseases. HSCT complications, including infection, organ dysfunction, and graft-versus-host-disease (GVHD) often require intensive care unit (ICU) therapies and are associated with mortality. Our aims were to identify the HSCT characteristics, complications and ICU therapies associated with (1) survival, and (2) survival changes over a ten-year period in a national dataset. Methods: A national sample from the Health Facts (Cerner Corporation, Kansas City, MO) database from 2009 to 2018 was utilized. Inclusion criteria were age 30 days to <22 years and HSCT procedure code. For patients with >1 HSCT, the first was analyzed. Data included demographics, hospital length of stay (LOS), hospital outcome, transplant type and indication. HSCT complications included GVHD and infections. ICU therapies were positive pressure ventilation (PPV), vasoactive infusion, and dialysis. Primary outcome was survival to discharge. Statistical methods included bivariate analyses and multivariate logistic regression. Results: 473 patients underwent HSCT with 93% survival. 62% were allogeneic (89% survival) and 38% were autologous (98% survival). GVHD occurred in 33% of allogeneic HSCT. Infections occurred in 26% of all HSCT. ICU therapies included PPV (11% of patients), vasoactive (25%), and dialysis (3%). Decreased survival was associated with allogeneic HSCT (p < 0.01), GVHD (p = 0.02), infection (p < 0.01), and ICU therapies (p < 0.01). Survival improved from 89% (2009-2013) to 96% (2014-2018) (p < 0.01). Allogeneic survival improved (82%-94%, p < 0.01) while autologous survival was unchanged. Survival improvement over time was associated with decreasing infections (33%-21%, p < 0.01) and increasing vasoactive infusions (20%-28%, p = 0.05). On multivariate analysis, later time period was associated with improved survival (p < 0.01, adjusted OR 4.28). Discussion: Hospital survival for HSCT improved from 89% to 96% from 2009 to 2018. Factors associated with mortality included allogeneic HSCT, GVHD, infections and ICU therapies. Improving survival coincided with decreasing infections and increasing vasoactive use.

The Association of the Lactate-Albumin Ratio With Mortality and Multiple Organ Dysfunction in PICU Patients.

OBJECTIVES: To compare the relative associations of lactate, albumin, and the lactate-albumin ratio (LAR) measured early in disease course against mortality and prevalence of multiple organ dysfunction syndrome (MODS) in a general sample of critically ill pediatric patients. DESIGN: Retrospective analysis of the Health Facts (Cerner Corporation, Kansas City, MO) national database. SETTING: U.S. hospitals with PICUs. PATIENTS: Children admitted to the ICU ( n = 648) from 2009 to 2018 who had lactate and albumin measured within 6 hours of admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 648 admissions were included, with an overall mortality rate of 10.8% ( n = 70) and a MODS prevalence of 29.3% ( n = 190). Compared with survivors, deaths had higher initial lactates (7.3 mmol/L [2.6-11.7 mmol/L] vs 1.9 mmol/L [1.2-3.1 mmol/L]; p < 0.01), lower initial albumins (3.3 g/dL [2.7-3.8 g/dL] vs 4.2 g/dL [3.7-4.7 g/dL]; p < 0.01), and higher LARs (2.2 [1.0-4.2] vs 0.5 [0.3-0.8]; p < 0.01), with similar trends in patients with MODS versus those without MODS. LAR demonstrated a higher odds ratio (OR) for death than initial lactate alone (2.34 [1.93-2.85] vs 1.29 [1.22-1.38]) and a higher OR for MODS than initial lactate alone (2.10 [1.73-2.56] vs 1.22 [1.16-1.29]). Area under the receiver operating characteristic (AUROC) curve of LAR for mortality was greater than initial lactate (0.86 vs 0.82; p < 0.01). The LAR AUROC for MODS was greater than the lactate AUROC (0.71 vs 0.66; p < 0.01). Trends of lactate, albumin, and LAR for mortality were consistent across several diagnostic subgroups (trauma, primary respiratory failure, toxicology), but not all. CONCLUSIONS: LAR measured early in the course of critical illness is significantly associated with mortality and development of MODS when compared with initial lactate or initial albumin alone in critically ill pediatric patients.

Clinical Instability Is a Sign of Severity of Illness: A Cohort Study.

OBJECTIVES: Test the hypothesis that within patient clinical instability measured by deterioration and improvement in mortality risk over 3-, 6-, 9-, and 12-hour time intervals is indicative of increasing severity of illness. DESIGN: Analysis of electronic health data from January 1, 2018, to February 29, 2020. SETTING: PICU and cardiac ICU at an academic children's hospital. PATIENTS: All PICU patients. Data included descriptive information, outcome, and independent variables used in the Criticality Index-Mortality. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 8,399 admissions with 312 deaths (3.7%). Mortality risk determined every three hours using the Criticality Index-Mortality, a machine learning algorithm calibrated to this hospital. Since the sample sizes were sufficiently large to expect statical differences, we also used two measures of effect size, the proportion of time deaths had greater instability than survivors, and the rank-biserial correlation, to assess the magnitude of the effect and complement our hypothesis tests. Within patient changes were compared for survivors and deaths. All comparisons of survivors versus deaths were less than 0.001. For all time intervals, two measures of effect size indicated that the differences between deaths and survivors were not clinically important. However, the within-patient maximum risk increase (clinical deterioration) and maximum risk decrease (clinical improvement) were both substantially greater in deaths than survivors for all time intervals. For deaths, the maximum risk increase ranged from 11.1% to 16.1% and the maximum decrease ranged from -7.3% to -10.0%, while the median maximum increases and decreases for survivors were all less than ± 0.1%. Both measures of effect size indicated moderate to high clinical importance. The within-patient volatility was greater than 4.5-fold greater in deaths than survivors during the first ICU day, plateauing at ICU days 4-5 at 2.5 greater volatility. CONCLUSIONS: Episodic clinical instability measured with mortality risk is a reliable sign of increasing severity of illness. Mortality risk changes during four time intervals demonstrated deaths have greater maximum and within-patient clinical instability than survivors. This observation confirms the clinical teaching that clinical instability is a sign of severity of illness.

The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU.

Background: The Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change. Objectives: To assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions. Population: There were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing. Model: A neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression. Results: Discrimination assessed across all time periods found an AUROC of 0.851 (0.841-0.862) and an AUPRC was 0.443 (0.417-0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689-0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058-0.328) and a maximum value of 0.499 (0.229-0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the R2 was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed a priori clinical expectations about the trajectories of death and survivors. Conclusions: The Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.

Pediatric Mortality and Acute Kidney Injury Are Associated with Chloride Abnormalities in Intensive Care Units in the United States: A Multicenter Observational Study.

Our objective was to determine in children in the intensive care unit (ICU) the incidence of hyperchloremia (>110 mmol/L) and hypochloremia (<98 mmol/L), the association of diagnoses with chloride abnormalities, and the associations of mortality and acute kidney injury (AKI) with chloride abnormalities. We analyzed the initial, maximum, and minimum chloride measurements of 14,684 children in the ICU with ≥1 chloride measurement in the Health Facts database between 2009 and 2016. For hyperchloremia and hypochloremia compared with normochloremia, mortality rates increased three to fivefold and AKI rates increased 1.5 to threefold. The highest mortality rate (7.7%; n = 95/1,234) occurred with hyperchloremia in the minimum chloride measurement group and the highest AKI rate (7.7%; n = 72/930) occurred with hypochloremia in the initial chloride measurement group. The most common diagnostic categories associated with chloride abnormalities were injury and poisoning; respiratory; central nervous system; infectious and parasitic diseases; and endocrine, nutritional, metabolic, and immunity disorders. Controlled for race, gender, age, and diagnostic categories, mortality odds ratios, and AKI odds ratios were significantly higher for hyperchloremia and hypochloremia compared with normochloremia. In conclusion, hyperchloremia and hypochloremia are independently associated with mortality and AKI in children in the ICU.

National Intravenous Acetaminophen Use in Pediatric Inpatients From 2011-2016.

OBJECTIVE: To 1) determine current intravenous (IV) acetaminophen use in pediatric inpatients; and 2) determine the association between opioid medication duration when used with or without IV acetaminophen. METHODS: A retrospective analysis of pediatric inpatients exposed to IV acetaminophen from January 2011 to June 2016, using the national database Health Facts. RESULTS: Eighteen thousand one hundred ninety-seven (2.0%) of 893,293 pediatric inpatients received IV acetaminophen for a median of 14 doses per patient (IQR, 8-56). A greater proportion of IV acetaminophen patients were admitted to the intensive care unit (ICU) (14.8% vs 5.1%, p < 0.0001), received positive pressure ventilation (2.0% vs 1.5%, p < 0.0001), had a higher hospital mortality rate (0.9% vs 0.3%, p < 0.0001), and were operative (35.5% vs 12.8%, p < 0.001) than those not receiving IV acetaminophen. The most common operations associated with IV acetaminophen use were musculoskeletal and digestive system operations. Prescription of IV acetaminophen increased over time, both in prescription rates and number of per patient doses. Of the 18,197 patients prescribed IV acetaminophen, 16,241 (89.2%) also were prescribed opioids during their hospitalization. A multivariate analysis revealed patients prescribed both IV acetaminophen and opioids had a 54.8% increase in opioid duration as compared with patients who received opioids alone. CONCLUSIONS: This is the first study to assess IV acetaminophen prescription practices for pediatric inpatients. Intravenous acetaminophen prescription was greater in the non-operative pediatric inpatient population than operative patients. Intravenous acetaminophen prescription was associated with an increase in opioid duration as compared with patients who received opioids alone, suggesting that it is commonly used to supplement opioids for pain relief.

Implementation of a Pediatric Delirium Screening Program in a Pediatric Intensive Care Unit.

BACKGROUND: Knowledge of and screening for delirium are important to patient care. As bedside caregivers, nurses are in a strategic position to observe changes that may indicate delirium. OBJECTIVE: To institute a delirium screening protocol in a pediatric intensive care unit using the Cornell Assessment of Pediatric Delirium. METHODS: Implementation strategies included cycles of education and repeated, timed compliance and accuracy assessments. Surveys administered before and 6 months after implementation were used to identify nurses' perceptions of screening. Compliance with and accuracy of screening were measured over 3 years. During the second and third years, text messages to bedside nurses' telephones served as screening reminders. RESULTS: Responses on the nursing surveys before (n = 89) and after (n = 74) implementation were compared. After implementation, time to complete the assessment, medical providers, and procedures were less likely to be perceived as obstacles to screening. Nurses' confidence in delirium screening improved over time. Accuracy increased from 60% during the first year to 100% during the second and third years (P < .05). Unit-wide compliance with delirium screening was 68% in the first year, 51% in the second year, and 72% in the third year, a trend toward improvement that was statistically significant (P < .05). CONCLUSIONS: Although unit-wide compliance with the screening protocol decreased without regular monitoring, it subsequently improved after the institution of reminders and reeducation of nursing staff. Nursing perceptions evolved to view screening as an important component of patient care that did not disrupt daily workflow.

Association of Congenital and Acquired Cardiovascular Conditions With COVID-19 Severity Among Pediatric Patients in the US.

Importance: Identifying the associations between severe COVID-19 and individual cardiovascular conditions in pediatric patients may inform treatment. Objective: To assess the association between previous or preexisting cardiovascular conditions and severity of COVID-19 in pediatric patients. Design, Setting, and Participants: This retrospective cohort study used data from a large, multicenter, electronic health records database in the US. The cohort included patients aged 2 months to 17 years with a laboratory-confirmed diagnosis of COVID-19 or a diagnosis code indicating infection or exposure to SARS-CoV-2 at 85 health systems between March 1, 2020, and January 31, 2021. Exposures: Diagnoses for 26 cardiovascular conditions between January 1, 2015, and December 31, 2019 (before infection with SARS-CoV-2). Main Outcomes and Measures: The main outcome was severe COVID-19, defined as need for supplemental oxygen or in-hospital death. Mixed-effects, random intercept logistic regression modeling assessed the significance and magnitude of associations between 26 cardiovascular conditions and COVID-19 severity. Multiple comparison adjustment was performed using the Benjamini-Hochberg false discovery rate procedure. Results: The study comprised 171 416 pediatric patients; the median age was 8 years (IQR, 2-14 years), and 50.28% were male. Of these patients, 17 065 (9.96%) had severe COVID-19. The random intercept model showed that the following cardiovascular conditions were associated with severe COVID-19: cardiac arrest (odds ratio [OR], 9.92; 95% CI, 6.93-14.20), cardiogenic shock (OR, 3.07; 95% CI, 1.90-4.96), heart surgery (OR, 3.04; 95% CI, 2.26-4.08), cardiopulmonary disease (OR, 1.91; 95% CI, 1.56-2.34), heart failure (OR, 1.82; 95% CI, 1.46-2.26), hypotension (OR, 1.57; 95% CI, 1.38-1.79), nontraumatic cerebral hemorrhage (OR, 1.54; 95% CI, 1.24-1.91), pericarditis (OR, 1.50; 95% CI, 1.17-1.94), simple biventricular defects (OR, 1.45; 95% CI, 1.29-1.62), venous embolism and thrombosis (OR, 1.39; 95% CI, 1.11-1.73), other hypertensive disorders (OR, 1.34; 95% CI, 1.09-1.63), complex biventricular defects (OR, 1.33; 95% CI, 1.14-1.54), and essential primary hypertension (OR, 1.22; 95% CI, 1.08-1.38). Furthermore, 194 of 258 patients (75.19%) with a history of cardiac arrest were younger than 12 years. Conclusions and Relevance: The findings suggest that some previous or preexisting cardiovascular conditions are associated with increased severity of COVID-19 among pediatric patients in the US and that morbidity may be increased among individuals children younger than 12 years with previous cardiac arrest.

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.

Association of Intravenous Acetaminophen Administration With the Duration of Intravenous Opioid Use Among Hospitalized Pediatric Patients.

Importance: Adoption of multimodal pain regimens that incorporate nonopioid analgesic medications to reduce inpatient opioid administration can prevent serious opioid-related adverse effects in children, including tolerance, withdrawal, delirium, and respiratory depression. Intravenous (IV) acetaminophen is in widespread pediatric use; however, its effectiveness as an opioid-sparing agent has not been evaluated in general pediatric inpatients. Objective: To determine if IV acetaminophen administered prior to IV opioids is associated with a reduction in the total duration of IV opioids administered compared with IV opioids administered without IV acetaminophen in general pediatric inpatients. Design, Setting, and Participants: This comparative effectiveness research study included data on pediatric inpatients from 274 US hospitals between January 2011 and June 2016 collected from a national database. Outcomes were compared with a propensity score-matched analysis of pediatric inpatients administered IV opioids without IV acetaminophen (control) and those administered IV acetaminophen prior to IV opioids (intervention). Data were analyzed from January 2020 through October 2021. Exposures: Patients in the intervention group received IV acetaminophen prior to IV opioids. Patients in the control group received IV opioids without IV acetaminophen. Main Outcomes and Measures: Total duration of all IV opioids administered during a patient's hospitalization. Results: Of 893 293 pediatric inpatients, a total of 104 579 were included in analysis (median [IQR] age, 1.3 [0-14.7] years; 59 806 [57.2%] female; 21 485 [21.5%] African American, 56 309 [53.8%] White), of whom 18 197 (2.0%) received IV acetaminophen, and 287 504 (34.0%) received IV opioids. After applying exclusion criteria, among patients who received IV acetaminophen, 1739 (10.8%) received IV acetaminophen prior to IV opioids within a median (IQR) treatment time of 1.5 (0.02-7.3) hours. After propensity score matching produced comparable groups in the control and intervention groups (with 839 patients in each group), the multivariable model estimated a 15.5% shorter duration of IV opioid use in the intervention group, with an absolute IV opioid reduction of 7.5 hours (95% CI, 0.7-15.8 hours). Conclusions and Relevance: In this comparative effectiveness study, IV acetaminophen administered prior to IV opioids was associated with a reduction in IV opioid duration by 15.5%. Multimodal pain regimens that use IV acetaminophen prior to IV opioids could reduce IV opioid duration.

Predicting Future Care Requirements Using Machine Learning for Pediatric Intensive and Routine Care Inpatients.

Develop and compare separate prediction models for ICU and non-ICU care for hospitalized children in four future time periods (6-12, 12-18, 18-24, and 24-30 hr) and assess these models in an independent cohort and simulated children's hospital. DESIGN: Predictive modeling used cohorts from the Health Facts database (Cerner Corporation, Kansas City, MO). SETTING: Children hospitalized in ICUs. PATIENTS: Children with greater than or equal to one ICU admission (n = 20,014) and randomly selected routine care children without ICU admission (n = 20,130) from 2009 to 2016 were used for model development and validation. An independent 2017-2018 cohort consisted of 80,089 children. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Initially, we undersampled non-ICU patients for development and comparison of the models. We randomly assigned 64% of patients for training, 8% for validation, and 28% for testing in both clinical groups. Two additional validation cohorts were tested: a simulated children's hospitals and the 2017-2018 cohort. The main outcome was ICU care or non-ICU care in four future time periods based on physiology, therapy, and care intensity. Four independent, sequential, and fully connected neural networks were calibrated to risk of ICU care at each time period. Performance for all models in the test sample were comparable including sensitivity greater than or equal to 0.727, specificity greater than or equal to 0.885, accuracy greater than 0.850, area under the receiver operating characteristic curves greater than or equal to 0.917, and all had excellent calibration (all R2 s > 0.98). Model performance in the 2017-2018 cohort was sensitivity greater than or equal to 0.545, specificity greater than or equal to 0.972, accuracy greater than or equal to 0.921, area under the receiver operating characteristic curves greater than or equal to 0.946, and R2 s greater than or equal to 0.979. Performance metrics were comparable for the simulated children's hospital and for hospitals stratified by teaching status, bed numbers, and geographic location. CONCLUSIONS: Machine learning models using physiology, therapy, and care intensity predicting future care needs had promising performance metrics. Notably, performance metrics were similar as the prediction time periods increased from 6-12 hours to 24-30 hours.

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.

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.

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.

Emphysematous pyelonephritis in renal allograft related to antibody-mediated rejection: A case report and literature review.

Emphysematous pyelonephritis (EPN) is a rare condition which can rapidly progress to sepsis and multiple organ failure with high mortality. We experienced a rare case of EPN in a renal allograft related to antibody-mediated rejection (AMR). The patient received a deceased donor kidney transplant due to end-stage renal disease secondary to diabetes mellitus. Cross-match test was negative but she had remote history of anti-HLA-A2 antibody corresponding with the donor HLA. Surgery concluded without any major events. Anti-thymoglobulin was given perioperatively for induction. She was compliant with her immunosuppressive medications making urine of 2 L/d with serum creatinine of 1.9 mg/dL at discharge on post-operative day (POD) 6. She did well until POD 14 when she presented to the clinic with features of sepsis, pain over the transplanted kidney area and decline in urine volume with elevated serum creatinine. CT revealed extensive gas throughout the transplanted kidney. Renal scan revealed non-functional transplant kidney with no arterial flow. Based on these findings, a decision to perform transplant nephrectomy was made. At laparotomy, the kidney was completely necrotic. Pathology showed non-viable kidney parenchyma with the tubules lacking neutrophilic casts suggestive of ischemic necrosis. Donor-specific antibody (DSA) returned positive with high intensity anti-HLA-A2 antibody. This is the first case of early EPN in allograft considered to have occurred as a result of thrombotic ischemia secondary to AMR. This case suggests consideration of perioperative anti-B-cell and/or anti-plasma cell therapies for historical DSA and strict post-operative follow-up in immunologically high-risk recipients to detect early signs of rejection and avoid deleterious outcomes.

Delirium in the Pediatric Cardiac Extracorporeal Membrane Oxygenation Patient Population: A Case Series.

OBJECTIVE: To determine the prevalence of delirium in children who require extracorporeal membrane oxygenation. DESIGN: Prospective observational longitudinal cohort study. SETTING: Urban academic cardiothoracic ICU. PATIENTS: All consecutive admissions to the cardiothoracic ICU who required venoarterial extracorporeal membrane oxygenation support. INTERVENTIONS: Daily delirium screening with the Cornell Assessment for Pediatric Delirium. MEASUREMENTS AND MAIN RESULTS: Eight children required extracorporeal membrane oxygenation during the study period, with a median extracorporeal membrane oxygenation duration of 202 hours (interquartile range, 99-302). All eight children developed delirium during their cardiothoracic ICU stay. Seventy-two days on extracorporeal membrane oxygenation were included in the analysis. A majority of patient days on extracorporeal membrane oxygenation were spent in coma (65%). Delirium was diagnosed during 21% of extracorporeal membrane oxygenation days. Only 13% of extracorporeal membrane oxygenation days were categorized as delirium free and coma free. Delirium screening was successfully completed on 70/72 days on extracorporeal membrane oxygenation (97%). CONCLUSIONS: In this cohort, delirium occurred in all children who required venoarterial extracorporeal membrane oxygenation. It is likely that this patient population has an extremely high risk for delirium and will benefit from routine screening in order to detect and treat delirium sooner. This has potential to improve both short- and long-term outcomes.