Ventilator Weaning and Terminal Extubation: Withdrawal of Life-Sustaining Therapy in Children. Secondary Analysis of the Death One Hour After Terminal Extubation Study.

OBJECTIVE: Terminal extubation (TE) and terminal weaning (TW) during withdrawal of life-sustaining therapies (WLSTs) have been described and defined in adults. The recent Death One Hour After Terminal Extubation study aimed to validate a model developed to predict whether a child would die within 1 hour after discontinuation of mechanical ventilation for WLST. Although TW has not been described in children, pre-extubation weaning has been known to occur before WLST, though to what extent is unknown. In this preplanned secondary analysis, we aim to describe/define TE and pre-extubation weaning (PW) in children and compare characteristics of patients who had ventilatory support decreased before WLST with those who did not. DESIGN: Secondary analysis of multicenter retrospective cohort study. SETTING: Ten PICUs in the United States between 2009 and 2021. PATIENTS: Nine hundred thirteen patients 0-21 years old who died after WLST. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 71.4% ( n = 652) had TE without decrease in ventilatory support in the 6 hours prior. TE without decrease in ventilatory support in the 6 hours prior = 71.4% ( n = 652) of our sample. Clinically relevant decrease in ventilatory support before WLST = 11% ( n = 100), and 17.6% ( n = 161) had likely incidental decrease in ventilatory support before WLST. Relevant ventilator parameters decreased were F io2 and/or ventilator set rates. There were no significant differences in any of the other evaluated patient characteristics between groups (weight, body mass index, unit type, primary diagnostic category, presence of coma, time to death after WLST, analgosedative requirements, postextubation respiratory support modality). CONCLUSIONS: Decreasing ventilatory support before WLST with extubation in children does occur. This practice was not associated with significant differences in palliative analgosedation doses or time to death after extubation.

Prehospitalization Trauma and Physiologic Factors Associated with the Presence of Post-traumatic Stress 3 Months After PICU Discharge.

BACKGROUND: Children admitted to the pediatric intensive care unit (PICU) have post-traumatic stress (PTS) rates up to 64%, and up to 28% of them meet criteria for PTS disorder (PTSD). We aim to examine whether a prior trauma history and increased physiologic parameters due to a heightened sympathetic response are associated with later PTS. Our hypothesis was children with history of prehospitalization trauma, higher heart rates, blood pressures, cortisol, and extrinsic catecholamine administration during PICU admission are more likely to have PTS after discharge. METHODS: This is a prospective, observational study of children admitted to the PICU at an urban, quaternary, academic children's hospital. Children aged 8 to 17 years old without developmental delay, severe psychiatric disorder, or traumatic brain injury were included. Children's prehospitalization trauma history was assessed with a semistructured interview. All in-hospital variables were from the electronic medical record. PTS was present if children had 4 of the Diagnostic and Statistical Manual of Mental Disorders IV criteria for PTSD. Student's t- and chi-squared tests were used to compare the presence or absence of prior trauma and all of the PICU-associated variables. RESULTS: Of the 110 children at baseline, 67 had 3-month follow-up. In the latter group, 46% met the criteria for PTS, mean age of 13 years (SD 3), 57% male, a mean PRISM III score of 4.9 (SD 4.3), and intensive care unit length of stay 6.5 days (SD 7.8). There were no statistically significant differences in the demographics of the children with and without PTS. The only variable to show significance was trauma history; children with prehospitalization trauma were more likely to have PTS at 3-month follow-up (P = .02). CONCLUSIONS: Prehospitalization trauma history was associated with the presence of PTS after admission to the PICU. This study suggests future studies should shift to the potential predictive benefit of screening children for trauma history upon PICU admission.

Interpreting a recurrent neural network's predictions of ICU mortality risk.

Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions. Algorithms such as Recurrent Neural Networks (RNNs) when applied to Electronic Medical Records (EMR) introduce additional barriers to transparency because of the sequential processing of the RNN and the multi-modal nature of EMR data. This work seeks to improve transparency by: 1) introducing Learned Binary Masks (LBM) as a method for identifying which EMR variables contributed to an RNN model's risk of mortality (ROM) predictions for critically ill children; and 2) applying KernelSHAP for the same purpose. Given an individual patient, LBM and KernelSHAP both generate an attribution matrix that shows the contribution of each input feature to the RNN's sequence of predictions for that patient. Attribution matrices can be aggregated in many ways to facilitate different levels of analysis of the RNN model and its predictions. Presented are three methods of aggregations and analyses: 1) over volatile time periods within individual patient predictions, 2) over populations of ICU patients sharing specific diagnoses, and 3) across the general population of critically ill children.

Continuous Prediction of Mortality in the PICU: A Recurrent Neural Network Model in a Single-Center Dataset.

OBJECTIVES: Develop, as a proof of concept, a recurrent neural network model using electronic medical records data capable of continuously assessing an individual child's risk of mortality throughout their ICU stay as a proxy measure of severity of illness. DESIGN: Retrospective cohort study. SETTING: PICU in a tertiary care academic children's hospital. PATIENTS/SUBJECTS: Twelve thousand five hundred sixteen episodes (9,070 children) admitted to the PICU between January 2010 and February 2019, partitioned into training (50%), validation (25%), and test (25%) sets. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: On 2,475 test set episodes lasting greater than or equal to 24 hours in the PICU, the area under the receiver operating characteristic curve of the recurrent neural network's 12th hour predictions was 0.94 (CI, 0.93-0.95), higher than those of Pediatric Index of Mortality 2 (0.88; CI, [0.85-0.91]; p < 0.02), Pediatric Risk of Mortality III (12th hr) (0.89; CI, [0.86-0.92]; p < 0.05), and Pediatric Logistic Organ Dysfunction day 1 (0.85; [0.81-0.89]; p < 0.002). The recurrent neural network's discrimination increased with more acquired data and smaller lead time, achieving a 0.99 area under the receiver operating characteristic curve 24 hours prior to discharge. Despite not having diagnostic information, the recurrent neural network performed well across different primary diagnostic categories, generally achieving higher area under the receiver operating characteristic curve for these groups than the other three scores. On 692 test set episodes lasting greater than or equal to 5 days in the PICU, the recurrent neural network area under the receiver operating characteristic curves significantly outperformed their daily Pediatric Logistic Organ Dysfunction counterparts (p < 0.005). CONCLUSIONS: The recurrent neural network model can process hundreds of input variables contained in a patient's electronic medical record and integrate them dynamically as measurements become available. Its high discrimination suggests the recurrent neural network's potential to provide an accurate, continuous, and real-time assessment of a child in the ICU.

Machine Learning to Predict Cardiac Death Within 1 Hour After Terminal Extubation.

OBJECTIVES: Accurate prediction of time to death after withdrawal of life-sustaining therapies may improve counseling for families and help identify candidates for organ donation after cardiac death. The study objectives were to: 1) train a long short-term memory model to predict cardiac death within 1 hour after terminal extubation, 2) calculate the positive predictive value of the model and the number needed to alert among potential organ donors, and 3) examine associations between time to cardiac death and the patient's characteristics and physiologic variables using Cox regression. DESIGN: Retrospective cohort study. SETTING: PICU and cardiothoracic ICU in a tertiary-care academic children's hospital. PATIENTS: Patients 0-21 years old who died after terminal extubation from 2011 to 2018 (n = 237). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The median time to death for the cohort was 0.3 hours after terminal extubation (interquartile range, 0.16-1.6 hr); 70% of patients died within 1 hour. The long short-term memory model had an area under the receiver operating characteristic curve of 0.85 and a positive predictive value of 0.81 at a sensitivity of 94% when predicting death within 1 hour of terminal extubation. About 39% of patients who died within 1 hour met organ procurement and transplantation network criteria for liver and kidney donors. The long short-term memory identified 93% of potential organ donors with a number needed to alert of 1.08, meaning that 13 of 14 prepared operating rooms would have yielded a viable organ. A Cox proportional hazard model identified independent predictors of shorter time to death including low Glasgow Coma Score, high Pao2-to-Fio2 ratio, low-pulse oximetry, and low serum bicarbonate. CONCLUSIONS: Our long short-term memory model accurately predicted whether a child will die within 1 hour of terminal extubation and may improve counseling for families. Our model can identify potential candidates for donation after cardiac death while minimizing unnecessarily prepared operating rooms.

The impact of extraneous features on the performance of recurrent neural network models in clinical tasks.

Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables or features are useful in predicting clinical outcomes can be challenging. Advanced algorithms, such as deep neural networks, were designed to process high-dimensional inputs containing variables in their measured form, thus bypass separate feature selection or engineering steps. We investigated the effect of extraneous input features on the predictive performance of Recurrent Neural Networks (RNN) by including in the input vector extraneous features that were randomly drawn from theoretical and empirical distributions. RNN models using different input vectors (EMR features only; EMR and extraneous features; extraneous features only) were trained to predict three clinical outcomes: in-ICU mortality, 72-h ICU re-admission, and 30-day ICU-free days. The measured degradations of the RNN's predictive performance with the inclusion of extraneous features to EMR variables were negligible.

The Impact of Coronavirus Disease 2019 Pandemic on U.S. and Canadian PICUs.

OBJECTIVES: There are limited reports of the impact of the coronavirus disease 2019 pandemic focused on U.S. and Canadian PICUs. This hypothesis-generating report aims to identify the United States and Canadian trends of coronavirus disease 2019 in PICUs. DESIGN AND SETTING: To better understand how the coronavirus disease 2019 pandemic was affecting U.S. and Canadian PICUs, an open voluntary daily data collection process of Canadian and U.S. PICUs was initiated by Virtual Pediatric Systems, LLC (Los Angeles, CA; http://www.myvps.org) in mid-March 2020. Information was made available online to all PICUs wishing to participate. A secondary data collection was performed to follow-up on patients discharged from those PICUs reporting coronavirus disease 2019 positive patients. MEASUREMENTS AND MAIN RESULTS: To date, over 180 PICUs have responded detailing 530 PICU admissions requiring over 3,467 days of PICU care with 30 deaths. The preponderance of cases was in the eastern regions. Twenty-four percent of the patients admitted to the PICUs were over 18 years old. Fourteen percent of admissions were under 2 years old. Nearly 60% of children had comorbidities at admission with the average length of stay increasing by age and by severity of comorbidity. Advanced respiratory support was necessary during 67% of the current days of care, with 69% being conventional mechanical ventilation. CONCLUSIONS: PICUs have been significantly impacted by the pandemic. They have provided care not only for children but also adults. Patients with coronavirus disease 2019 have a high frequency of comorbidities, require longer stays, more ventilatory support than usual PICU admissions. These data suggest several avenues for further exploration.

Development and Validation of an Empiric Tool to Predict Favorable Neurologic Outcomes Among PICU Patients.

OBJECTIVES: To create a novel tool to predict favorable neurologic outcomes during ICU stay among children with critical illness. DESIGN: Logistic regression models using adaptive lasso methodology were used to identify independent factors associated with favorable neurologic outcomes. A mixed effects logistic regression model was used to create the final prediction model including all predictors selected from the lasso model. Model validation was performed using a 10-fold internal cross-validation approach. SETTING: Virtual Pediatric Systems (VPS, LLC, Los Angeles, CA) database. PATIENTS: Patients less than 18 years old admitted to one of the participating ICUs in the Virtual Pediatric Systems database were included (2009-2015). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 160,570 patients from 90 hospitals qualified for inclusion. Of these, 1,675 patients (1.04%) were associated with a decline in Pediatric Cerebral Performance Category scale by at least 2 between ICU admission and ICU discharge (unfavorable neurologic outcome). The independent factors associated with unfavorable neurologic outcome included higher weight at ICU admission, higher Pediatric Index of Morality-2 score at ICU admission, cardiac arrest, stroke, seizures, head/nonhead trauma, use of conventional mechanical ventilation and high-frequency oscillatory ventilation, prolonged hospital length of ICU stay, and prolonged use of mechanical ventilation. The presence of chromosomal anomaly, cardiac surgery, and utilization of nitric oxide were associated with favorable neurologic outcome. The final online prediction tool can be accessed at https://soipredictiontool.shinyapps.io/GNOScore/. Our model predicted 139,688 patients with favorable neurologic outcomes in an internal validation sample when the observed number of patients with favorable neurologic outcomes was among 139,591 patients. The area under the receiver operating curve for the validation model was 0.90. CONCLUSIONS: This proposed prediction tool encompasses 20 risk factors into one probability to predict favorable neurologic outcome during ICU stay among children with critical illness. Future studies should seek external validation and improved discrimination of this prediction tool.

Applying Machine Learning to Pediatric Critical Care Data.

OBJECTIVES: To explore whether machine learning applied to pediatric critical care data could discover medically pertinent information, we analyzed clinically collected electronic medical record data, after data extraction and preparation, using k-means clustering. DESIGN: Retrospective analysis of electronic medical record ICU data. SETTING: Tertiary Children's Hospital PICU. PATIENTS: Anonymized electronic medical record data from PICU admissions over 10 years. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Data from 11,384 PICU episodes were cleaned, and specific features were generated. A k-means clustering algorithm was applied, and the stability and medical validity of the resulting 10 clusters were determined. The distribution of mortality, length of stay, use of ventilation and pressors, and diagnostic categories among resulting clusters was analyzed. Clusters had significant prognostic information (p < 0.0001). Cluster membership predicted mortality (area under the curve of the receiver operating characteristic = 0.77). Length of stay, the use of inotropes and intubation, and diagnostic categories were nonrandomly distributed among the clusters (p < 0.0001). CONCLUSIONS: A standard machine learning methodology was able to determine significant medically relevant information from PICU electronic medical record data which included prognosis, diagnosis, and therapy in an unsupervised approach. Further development and application of machine learning to critical care data may provide insights into how critical illness happens to children.

Low-dose dexmedetomidine as an adjuvant to propofol infusion for children in MRI: A double-cohort study.

INTRODUCTION: Propofol is an effective sedative for magnetic resonance imaging. Nevertheless, it may cause hemodynamic and respiratory complications in a dose dependent fashion. We investigated the role of low-dose dexmedetomidine (0.5 µg/kg) as an adjuvant to propofol sedation for children undergoing magnetic resonance imaging. We hypothesized that dexmedetomidine would decrease the propofol dose required, airway complications, and hemodynamic instability. METHODS: We performed a retrospective chart review of patients' age of 1 month to 20 years. Children were divided into 2 groups; group P received only propofol; group D + P received intravenous bolus of dexmedetomidine (0.5 µg/kg) and propofol. RESULTS: We reviewed 172 children in P and 129 in D + P (dexmedetomidine dose, median: 0.50 µg/kg (IQR: 0.45-0.62). An additional dexmedetomidine bolus was given to 17 children for sedation lasting longer than 2 hours. Total propofol dose (µg/kg/min) was significantly higher in group P than D + P; 215.0 (182.6-253.8) vs 147.6 (127.5-180.9); Median Diff = -67.8; 95%CI = -80.6, -54.9; P < .0001. There was no difference in recovery time (minutes); P: 28 (17-39) vs D + P: 27 (18-41); Median Diff = -1; 95%CI = -6.0, 4.0; P = .694. The need for airway support was significantly greater in P compared to D + P; 15/172 vs 3/129; OR = 0.25; 95%CI = 0.07 to 0.90; P = .02 (2-sample proportions test). Mean arterial pressure was significantly lower in P compared to D + P across time over 60 minutes after induction (coef = -0.06, 95%CI = -0.11, -0.02, P = .004). DISCUSSION & CONCLUSION: A low-dose bolus of dexmedetomidine (0.5 µg/kg) used as an adjuvant can decrease the propofol requirement for children undergoing sedation for magnetic resonance imaging. This may decrease the need for airway support and contribute to improved hemodynamic stability without prolonging recovery time.

Impact of 24/7 In-Hospital Intensivist Coverage on Outcomes in Pediatric Intensive Care. A Multicenter Study.

RATIONALE: The around-the-clock presence of an in-house attending critical care physician (24/7 coverage) is purported to be associated with improved outcomes among high-risk children with critical illness. OBJECTIVES: To evaluate the association of 24/7 in-house coverage with outcomes in children with critical illness. METHODS: Patients younger than 18 years of age in the Virtual Pediatric Systems Database (2009-2014) were included. The main analysis was performed using generalized linear mixed effects multivariable regression models. In addition, multiple sensitivity analyses were performed to test the robustness of our findings. MEASUREMENTS AND MAIN RESULTS: A total of 455,607 patients from 125 hospitals were included (24/7 group: 266,319 patients; no 24/7 group: 189,288 patients). After adjusting for patient and center characteristics, the 24/7 group was associated with lower mortality in the intensive care unit (ICU) (24/7 vs. no 24/7; odds ratio [OR], 0.52; 95% confidence interval [CI], 0.33-0.80; P = 0.002), a lower incidence of cardiac arrest (OR, 0.73; 95% CI, 0.54-0.99; P = 0.04), lower mortality after cardiac arrest (OR, 0.56; 95% CI, 0.340-0.93; P = 0.02), a shorter ICU stay (mean difference, -0.51 d; 95% CI, -0.93 to -0.09), and shorter duration of mechanical ventilation (mean difference, -0.68 d; 95% CI, -1.23 to -0.14). CONCLUSIONS: In this large observational study, we demonstrated that pediatric critical care provided in the ICUs staffed with a 24/7 intensivist presence is associated with improved overall patient survival and survival after cardiac arrest compared with patients treated in ICUs staffed with discretionary attending coverage. However, results from a few sensitivity analyses leave some ambiguity in these results.

Pediatric Triage in a Severe Pandemic: Maximizing Survival by Establishing Triage Thresholds.

OBJECTIVES: To develop and validate an algorithm to guide selection of patients for pediatric critical care admission during a severe pandemic when Crisis Standards of Care are implemented. DESIGN: Retrospective observational study using secondary data. PATIENTS: Children admitted to VPS-participating PICUs between 2009-2012. INTERVENTIONS: A total of 111,174 randomly selected nonelective cases from the Virtual PICU Systems database were used to estimate each patient's probability of death and duration of ventilation employing previously derived predictive equations. Using real and projected statistics for the State of Ohio as an example, triage thresholds were established for casualty volumes ranging from 5,000 to 10,000 for a modeled pandemic with peak duration of 6 weeks and 280 pediatric intensive care beds. The goal was to simultaneously maximize casualty survival and bed occupancy. Discrete Event Simulation was used to determine triage thresholds for probability of death and duration of ventilation as a function of casualty volume and the total number of available beds. Simulation was employed to compare survival between the proposed triage algorithm and a first come first served distribution of scarce resources. MEASUREMENTS AND MAIN RESULTS: Population survival was greater using the triage thresholds compared with a first come first served strategy. In this model, for five, six, seven, eight, and 10 thousand casualties, the triage algorithm increased the number of lives saved by 284, 386, 547, 746, and 1,089, respectively, compared with first come first served (all p < 0.001). CONCLUSIONS: Use of triage thresholds based on probability of death and duration of mechanical ventilation determined from actual critically ill children's data demonstrated superior population survival during a simulated overwhelming pandemic.

Effect of Inhaled Nitric Oxide on Outcomes in Children With Acute Lung Injury: Propensity Matched Analysis From a Linked Database.

OBJECTIVES: To evaluate the effect of inhaled nitric oxide on outcomes in children with acute lung injury. DESIGN: Retrospective study with a secondary data analysis of linked data from two national databases. Propensity score matching was performed to adjust for potential confounding variables between patients who received at least 24 hours of inhaled nitric oxide (inhaled nitric oxide group) and those who did not receive inhaled nitric oxide (no inhaled nitric oxide group). SETTING: Linked data from Virtual Pediatric Systems (LLC) database and Pediatric Health Information System. PATIENTS: Patients less than 18 years old receiving mechanical ventilation for acute lung injury at nine participating hospitals were included (2009-2014). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 20,106 patients from nine hospitals were included. Of these, 859 patients (4.3%) received inhaled nitric oxide for at least 24 hours during their hospital stay. Prior to matching, patients in the inhaled nitric oxide group were younger, with more comorbidities, greater severity of illness scores, higher prevalence of cardiopulmonary resuscitation, and greater resource utilization. Before matching, unadjusted outcomes, including mortality, were worse in the inhaled nitric oxide group (inhaled nitric oxide vs no inhaled nitric oxide; 25.7% vs 7.9%; p < 0.001; standardized mortality ratio, 2.6 [2.3-3.1] vs 1.1 [1.0-1.2]; p < 0.001). Propensity score matching of 521 patient pairs revealed no difference in mortality in the two groups (22.3% vs 20.2%; p = 0.40; standardized mortality ratio, 2.5 [2.1-3.0] vs 2.3 [1.9-2.8]; p = 0.53). However, the other outcomes such as ventilation free days (10.1 vs 13.6 d; p < 0.001), duration of mechanical ventilation (13.8 vs 10.1 d; p < 0.001), duration of ICU and hospital stay (15.5 vs 12.2 d; p < 0.001 and 28.0 vs 24.1 d; p < 0.001), and hospital costs ($150,569 vs $102,823; p < 0.001) were significantly worse in the inhaled nitric oxide group. CONCLUSIONS: This large observational study demonstrated that inhaled nitric oxide administration in children with acute lung injury was not associated with improved mortality. Rather, it was associated with increased hospital utilization and hospital costs.

Association of Freestanding Children's Hospitals With Outcomes in Children With Critical Illness.

OBJECTIVES: Little is known about the relationship between freestanding children's hospitals and outcomes in children with critical illness. The purpose of this study was to evaluate the association of freestanding children's hospitals with outcomes in children with critical illness. DESIGN: Propensity score matching was performed to adjust for potential confounding variables between patients cared for in freestanding or nonfreestanding children's hospitals. We tested the sensitivity of our findings by repeating the primary analyses using inverse probability of treatment weighting method and regression adjustment using the propensity score. SETTING: Retrospective study from an existing national database, Virtual PICU Systems (LLC) database. PATIENTS: Patients less than 18 years old admitted to one of the participating PICUs in the Virtual PICU Systems, LLC database were included (2009-2014). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 538,967 patients from 140 centers were included. Of these, 323,319 patients were treated in 60 freestanding hospitals. In contrast, 215,648 patients were cared for in 80 nonfreestanding hospitals. By propensity matching, 134,656 patients were matched 1:1 in the two groups (67,328 in each group). Prior to matching, patients in the freestanding hospitals were younger, had greater comorbidities, had higher severity of illness scores, had higher incidence of cardiac arrest, had higher resource utilization, and had higher proportion of patients undergoing complex procedures such as cardiac surgery. Before matching, the outcomes including mortality were worse among the patients cared for in the freestanding hospitals (freestanding vs nonfreestanding, 2.5% vs 2.3%; p < 0.001). After matching, the majority of the study outcomes were better in freestanding hospitals (freestanding vs nonfreestanding, mortality: 2.1% vs 2.8%, p < 0.001; standardized mortality ratio: 0.77 [0.73-0.82] vs 0.99 [0.87-0.96], p < 0.001; reintubation: 3.4% vs 3.8%, p < 0.001; good neurologic outcome: 97.7% vs 97.1%, p = 0.001). CONCLUSIONS: In this large observational study, we demonstrated that ICU care provided in freestanding children's hospitals is associated with improved risk-adjusted survival chances compared to nonfreestanding children's hospitals. However, the clinical significance of this change in mortality should be interpreted with caution. It is also possible that the hospital structure may be a surrogate of other factors that may bias the results.

Association of house staff training with mortality in children with critical illness.

AIM: To evaluate the association of house staff training with mortality in children with critical illness. METHODS: Patients <18 years of age in the Virtual PICU Systems (VPS, LLC) Database (2009-2013) were included. The study population was divided in two study groups: hospitals with residency programme only and hospitals with both residency and fellowship programme. Control group constituted hospitals with no residency or fellowship programme. The primary study outcome was mortality before intensive care unit (ICU) discharge. Multivariable logistic regression models were fitted to evaluate association of training programmes with ICU mortality. RESULTS: A total of 336 335 patients from 108 centres were included. Case-mix of patients among the hospitals with training programmes was complex; patients cared for in the hospitals with training programmes had greater severity of illness, had higher resource utilisation and had higher overall admission risk of death compared to patients cared for in the control hospitals. Despite caring for more complex and sicker patients, the hospitals with training programmes were associated with lower odds of ICU mortality. CONCLUSION: Our study establishes that ICU care provided in hospitals with training programmes is associated with improved adjusted survival rates among the Virtual PICU database hospitals in the United States.

Mainstream capnography system for nonintubated children in the postanesthesia care unit: Performance with changing flow rates, and a comparison to side stream capnography.

INTRODUCTION: Monitoring of exhaled carbon dioxide (CO2 ) in nonintubated patients is challenging. We compared the precision of a mainstream mask capnography to side stream sampling nasal cannula capnography. In addition, we compared the effect of gas flow rates on the measured exhaled CO2 between mainstream mask and side stream nasal cannula capnography. METHODS: A mainstream mask capnography system (cap-ONE) was tested. Children (weight of 7-40 kg, ASA 1-2) following anesthesia for minor procedures were assigned randomly to side stream or mainstream sampling groups. The side stream group wore a nasal cannula with CO2 side port (NC). In the postanesthesia care unit, O2 flow was started at 5 l·min-1 , reduced to 2 and then 0.25 l·min-1 every 3 min. Capnogram analysis measuring heights of all the waveforms was performed for continuous 120 s from the end of recording at each O2 flow rate for each group. RESULTS: Fifty-eight children were enrolled and 39 were analyzed (18 side stream NC and 21 mainstream mask). There were two mouth breathing children excluded from study in side stream NC group due to failure to capture measurable CO2 waveforms. Peak CO2 values measured by mainstream mask system were normally (Gaussian) distributed with smaller standard deviation (sd) at each O2 flow than were those measured by side stream NC system which demonstrated irregular distributions with larger sd. Peak CO2 values measurement was less affected by a change in flow rate in mainstream mask group than in side stream NC group (P = 0.04 in 5-0.25 l·min-1 O2 flow change). CONCLUSION: A new mainstream mask system (cap-ONE) performed with greater precision than side stream NC monitoring regardless of mouth breathing. Measurement of peak CO2 values by mainstream mask system showed normal distribution with smaller standard deviation (sd) and was less affected by O2 flow change in predictable fashion.

Pediatric Index of Cardiac Surgical Intensive Care Mortality Risk Score for Pediatric Cardiac Critical Care.

OBJECTIVE: Comparison of clinical outcomes is imperative in the evaluation of healthcare quality. Risk adjustment for children undergoing cardiac surgery poses unique challenges, due to its distinct nature. We developed a risk-adjustment tool specifically focused on critical care mortality for the pediatric cardiac surgical population: the Pediatric Index of Cardiac Surgical Intensive care Mortality score. DESIGN: Retrospective analysis of prospectively collected pediatric critical care data. SETTING: Pediatric critical care units in the United States. PATIENTS: Pediatric cardiac intensive care surgical patients. INTERVENTIONS: Prospectively collected data from consecutive patients admitted to ICUs were obtained from The Virtual PICU System (VPS, LLC, Los Angeles, CA), a national pediatric critical care database. Thirty-two candidate physiologic, demographic, and diagnostic variables were analyzed for inclusion in the development of the Pediatric Index of Cardiac Surgical Intensive care Mortality model. Multivariate logistic regression with stepwise selection was used to develop the model. MEASUREMENTS AND MAIN RESULTS: A total of 16,574 cardiac surgical patients from the 55 PICUs across the United States were included in the analysis. Thirteen variables remained in the final model, including the validated Society of Thoracic Surgeons-European Association of Cardio-Thoracic Surgery Congenital Heart Surgery Mortality (STAT) score and admission time with respect to cardiac surgery, which identifies whether the patient underwent the index surgical procedure before or after admission to the ICU. Pediatric Index of Cardiac Surgical Intensive Care Mortality (PICSIM) performance was compared with the performance of Pediatric Risk of Mortality-3 and Pediatric Index of Mortality-2 risk of mortality scores, as well as the STAT score and STAT categories by calculating the area under the curve of the receiver operating characteristic from a validation dataset: PICSIM (area under the curve = 0.87) performed better than Pediatric Index of Mortality-2 (area under the curve = 0.81), Pediatric Risk of Mortality-3 (area under the curve = 0.82), STAT score (area under the curve = 0.77), STAT category (area under the curve = 0.75), and Risk Adjustment for Congenital Heart Surgery-1 (area under the curve = 0.74). CONCLUSIONS: This newly developed mortality score, PICSIM, consisting of 13 risk variables encompassing physiology, cardiovascular condition, and time of admission to the ICU showed better discrimination than Pediatric Index of Mortality-2, Pediatric Risk of Mortality-3, and STAT score and category for mortality in a multisite cohort of pediatric cardiac surgical patients. The introduction of the variable "admission time with respect to cardiac surgery" allowed prediction of mortality when patients are admitted to the ICU either before or after the index surgical procedure.

Evidence-Based Pediatric Outcome Predictors to Guide the Allocation of Critical Care Resources in a Mass Casualty Event.

OBJECTIVE: ICU resources may be overwhelmed by a mass casualty event, triggering a conversion to Crisis Standards of Care in which critical care support is diverted away from patients least likely to benefit, with the goal of improving population survival. We aimed to devise a Crisis Standards of Care triage allocation scheme specifically for children. DESIGN: A triage scheme is proposed in which patients would be divided into those requiring mechanical ventilation at PICU presentation and those not, and then each group would be evaluated for probability of death and for predicted duration of resource consumption, specifically, duration of PICU length of stay and mechanical ventilation. Children will be excluded from PICU admission if their mortality or resource utilization is predicted to exceed predetermined levels ("high risk"), or if they have a low likelihood of requiring ICU support ("low risk"). Children entered into the Virtual PICU Performance Systems database were employed to develop prediction equations to assign children to the exclusion categories using logistic and linear regression. Machine Learning provided an alternative strategy to develop a triage scheme independent from this process. SETTING: One hundred ten American PICUs SUBJECTS: : One hundred fifty thousand records from the Virtual PICU database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The prediction equations for probability of death had an area under the receiver operating characteristic curve more than 0.87. The prediction equation for belonging to the low-risk category had lower discrimination. R for the prediction equations for PICU length of stay and days of mechanical ventilation ranged from 0.10 to 0.18. Machine learning recommended initially dividing children into those mechanically ventilated versus those not and had strong predictive power for mortality, thus independently verifying the triage sequence and broadly verifying the algorithm. CONCLUSION: An evidence-based predictive tool for children is presented to guide resource allocation during Crisis Standards of Care, potentially improving population outcomes by selecting patients likely to benefit from short-duration ICU interventions.

Association of center volume with outcomes in critically ill children with acute asthma.

BACKGROUND: Little is known about the relation between center volume and outcomes in children requiring intensive care unit (ICU) admission for acute asthma. OBJECTIVE: To evaluate the association of center volume with the odds of receiving positive pressure ventilation and length of ICU stay. METHODS: Patients 2 to 18 years of age with the primary diagnosis of asthma were included (2009-2012). Center volume was defined as the average number of mechanical ventilator cases per year for any diagnoses during the study period. In multivariable analysis, the odds of receiving positive pressure ventilation (invasive and noninvasive ventilation) and ICU length of stay were evaluated as a function of center volume. RESULTS: Fifteen thousand eighty-three patients from 103 pediatric ICUs with the primary diagnosis of acute asthma met the inclusion criteria. Seven hundred fifty-two patients (5%) received conventional mechanical ventilation and 964 patients (6%) received noninvasive ventilation. In multivariable analysis, center volume was not associated with the odds of receiving any form of positive pressure ventilation in children with acute asthma, with the exception of high- to medium-volume centers. However, ICU length of stay varied with center volume and was noted to be longer in low-volume centers compared with medium- and high-volume centers. CONCLUSION: In children with acute asthma, this study establishes a relation between center volume and ICU length of stay. However, this study fails to show any significant relation between center volume and the odds of receiving positive pressure ventilation; further analyses are needed to evaluate this relation in more detail.