Publications by authors named "Randall C Wetzel"

41 Publications

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

Pediatr Crit Care Med 2021 Feb;22(2):161-171

Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA.

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.
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http://dx.doi.org/10.1097/PCC.0000000000002612DOI Listing
February 2021

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

Pediatr Crit Care Med 2020 09;21(9):e643-e650

Virtual Pediatric Systems (VPS), LLC, Los Angeles, CA.

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.
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http://dx.doi.org/10.1097/PCC.0000000000002510DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340137PMC
September 2020

The authors reply.

Pediatr Crit Care Med 2019 04;20(4):399-400

The Laura P and Leland K. Virtual Pediatric Intensive Care Unit, Children's Hospital Los Angeles, Los Angeles, CA Department of Anesthesiology Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA.

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http://dx.doi.org/10.1097/PCC.0000000000001900DOI Listing
April 2019

Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.

J Am Med Inform Assoc 2018 12;25(12):1600-1607

Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit, Children's Hospital Los Angeles, Los Angeles, CA, USA.

Objective: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.

Methods: EMR data from 7256 survivor PICU episodes (5632 patients) collected between 2009 and 2017 at Children's Hospital Los Angeles was analyzed. Each episode contained 375 variables representing physiology, labs, interventions, and drugs. Between medical and physical discharge, when clinicians determined the patient was ready for ICU discharge, they were assumed to be in a physiologically acceptable state space (PASS) for discharge. Each patient's heart rate, systolic blood pressure, diastolic blood pressure in the PASS window were measured and compared to age-normal values, regression-quantified PASS predictions, and recurrent neural network (RNN) PASS predictions made 12 hours after PICU admission.

Results: Mean absolute errors (MAEs) between individual PASS values and age-normal values (HR: 21.0 bpm; SBP: 10.8 mm Hg; DBP: 10.6 mm Hg) were greater (p < .05) than regression prediction MAEs (HR: 15.4 bpm; SBP: 9.9 mm Hg; DBP: 8.6 mm Hg). The RNN models best approximated individual PASS values (HR: 12.3 bpm; SBP: 7.6 mm Hg; DBP: 7.0 mm Hg).

Conclusions: The RNN model predictions better approximate patient-specific PASS values than regression and age-normal values.
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http://dx.doi.org/10.1093/jamia/ocy122DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647156PMC
December 2018

Artificial Intelligence: An Inkling of Caution.

Pediatr Crit Care Med 2018 10;19(10):1004-1005

Department of Anesthesiology Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA The Laura P and Leland K. Virtual Pediatric Intensive Care Unit Children's Hospital Los Angeles Los Angeles, CA.

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http://dx.doi.org/10.1097/PCC.0000000000001700DOI Listing
October 2018

Applying Machine Learning to Pediatric Critical Care Data.

Pediatr Crit Care Med 2018 07;19(7):599-608

All authors: Children's Hospital Los Angeles, Los Angeles, CA.

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.
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http://dx.doi.org/10.1097/PCC.0000000000001567DOI Listing
July 2018

First Get the Data, Then Do the Science!

Authors:
Randall C Wetzel

Pediatr Crit Care Med 2018 04;19(4):382-383

Department of Anesthesiology Critical Care Medicine, Children's Hospital of Los Angeles, Los Angeles, CA.

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http://dx.doi.org/10.1097/PCC.0000000000001482DOI Listing
April 2018

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

Crit Care Med 2018 01;46(1):108-115

Virtual Pediatric Systems, LLC, Los Angeles, CA.

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.
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http://dx.doi.org/10.1097/CCM.0000000000002753DOI Listing
January 2018

Pediatric Intensive Care Databases for Quality Improvement.

Authors:
Randall C Wetzel

J Pediatr Intensive Care 2016 Sep 30;5(3):81-88. Epub 2015 Nov 30.

Department of Anesthesiology Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, California, United States.

The availability and breadth of collected data has grown exponentially in pediatric critical care medicine. This growth is driven by the practitioners' desire to understand and improve practice. In this manuscript, the author details the registry design factors that must be considered to meet quality improvement and safety needs in pediatric critical care units. The challenges to maintain a high standard database and data on health care delivery performances using the VPS registry data are provided.
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http://dx.doi.org/10.1055/s-0035-1568146DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512415PMC
September 2016

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

Crit Care Med 2016 Dec;44(12):2131-2138

1Division of Pediatric Cardiology, University of Arkansas for Medical Sciences, Little Rock, AR.2Section of Biostatistics, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR.3Department of Statistics, LSU Health Sciences Center, New Orleans, LA.4Division of Pediatric Cardiology, Department of Pediatrics, Children's Hospital of Orange County, Orange, CA.5Medical Intelligence and Innovation Institute (MI3), Children's Hospital of Orange County, Orange, CA.6Virtual PICU Systems, LLC, Los Angeles, CA.7Division of Pediatric Critical Care, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI.8Division of Critical Care Medicine, Department of Pediatrics and Anesthesiology, Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA.

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.
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http://dx.doi.org/10.1097/CCM.0000000000001961DOI Listing
December 2016

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

Am J Respir Crit Care Med 2016 12;194(12):1506-1513

4 Virtual Pediatric Systems, LLC, Los Angeles, California.

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.
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http://dx.doi.org/10.1164/rccm.201512-2456OCDOI Listing
December 2016

Risk factors and outcomes of in-hospital cardiac arrest following pediatric heart operations of varying complexity.

Resuscitation 2016 08 13;105:1-7. Epub 2016 May 13.

Virtual PICU Systems, LLC, Los Angeles, CA, United States; Division of Critical Care Medicine, Department of Pediatrics and Anesthesiology, Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA, United States.

Background: Multi center data regarding cardiac arrest in children undergoing heart operations of varying complexity are limited.

Methods: Children <18 years undergoing heart surgery (with or without cardiopulmonary bypass) in the Virtual Pediatric Systems (VPS, LLC) Database (2009-2014) were included. Multivariable mixed logistic regression models were adjusted for patient's characteristics, surgical risk category (STS-EACTS Categories 1, 2, and 3 classified as "low" complexity and Categories 4 and 5 classified as "high" complexity), and hospital characteristics.

Results: Overall, 26,909 patients (62 centers) were included. Of these, 2.7% had cardiac arrest after cardiac surgery with an associated mortality of 31%. The prevalence of cardiac arrest was lower among patients undergoing low complexity operations (low complexity vs. high complexity: 1.7% vs. 5.9%). Unadjusted outcomes after cardiac arrest were significantly better among patients undergoing low complexity operations (mortality: 21.6% vs. 39.1%, good neurological outcomes: 78.7% vs. 71.6%). In adjusted models, odds of cardiac arrest were significantly lower among patients undergoing low complexity operations (OR: 0.55, 95% CI: 0.46-0.66). Adjusted models, however, showed no difference in mortality or neurological outcomes after cardiac arrest regardless of surgical complexity. Further, our results suggest that incidence of cardiac arrest and mortality after cardiac arrest are a function of patient characteristics, surgical risk category, and hospital characteristics. Presence of around the clock in-house attending level pediatric intensivist coverage was associated with lower incidence of post-operative cardiac arrest, and presence of a dedicated cardiac ICU was associated with lower mortality after cardiac arrest.

Conclusions: This study suggests that the patients undergoing high complexity operations are a higher risk group with increased prevalence of post-operative cardiac arrest. These data further suggest that patients undergoing high complexity operations can be rescued after cardiac arrest with a high survival rate.
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http://dx.doi.org/10.1016/j.resuscitation.2016.04.022DOI Listing
August 2016

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

Crit Care Med 2016 Oct;44(10):1901-9

1Division of Pediatric Cardiology, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR.2Children's Hospital Association, Overland Park, KS.3Division of Pediatric Critical Care Medicine, Department of Pediatrics, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA.4Virtual PICU Systems, LLC, Los Angeles, CA.5Division of Critical Care Medicine, Department of Pediatrics and Anesthesiology, Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA.

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.
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http://dx.doi.org/10.1097/CCM.0000000000001837DOI Listing
October 2016

Obesity and Mortality Risk in Critically Ill Children.

Pediatrics 2016 Mar 16;137(3):e20152035. Epub 2016 Feb 16.

Department of Anesthesiology Critical Care Medicine, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, California.

Background And Objectives: Childhood obesity is epidemic and may be associated with PICU mortality. Using a large multicenter PICU database, we investigated the association between obesity and PICU mortality, adjusting for initial severity of illness. We further investigated whether height- and weight-based classifications of obesity compared with a weight-based classification alone alter the mortality distribution.

Methods: This retrospective analysis used prospectively collected data from the Virtual PICU Systems database. Height, weight, age, and gender were used to calculate z score groups based on Centers for Disease Control and Prevention and World Health Organization growth curves. A random effects mixed logistic regression model was used to evaluate the association between obesity and PICU mortality, controlling for hospital, initial severity of illness, and comorbidities.

Results: A total of 127,607 patients were included; the mortality rate was 2.48%. Being overweight was independently associated with increased PICU mortality after controlling for severity of illness with the Pediatric Index of Mortality 2 score and preexisting comorbidities. Mortality had a U-shaped distribution when classified according to weight-for-age or weight-for-height/BMI. When classifying patients using weight-for-age without respect to height, the nadir of the mortality curve was shifted, potentially falsely implying a benefit to mild obesity.

Conclusions: Risk-adjusted PICU mortality significantly increases as weight-for-height/BMI increases into the overweight and obese ranges. We believe that height data are necessary to correctly classify body habitus; without such information, a protective benefit from mild obesity may be incorrectly concluded.
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http://dx.doi.org/10.1542/peds.2015-2035DOI Listing
March 2016

Risk factors for mechanical ventilation and reintubation after pediatric heart surgery.

J Thorac Cardiovasc Surg 2016 Feb 28;151(2):451-8.e3. Epub 2015 Sep 28.

Virtual PICU Systems, LLC, Los Angeles, Calif; Division of Critical Care Medicine, Department of Pediatrics and Anesthesiology, Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, Calif.

Objective: To determine the prevalence of and risk factors associated with the need for mechanical ventilation in children following cardiac surgery and the need for subsequent reintubation after the initial extubation attempt.

Methods: Patients younger than 18 years who underwent cardiac operations for congenital heart disease at one of the participating pediatric intensive care units (ICUs) in the Virtual PICU Systems (VPS), LLC, database were included (2009-2014). Multivariable logistic regression models were fitted to identify factors likely associated with mechanical ventilation and reintubation.

Results: A total of 27,398 patients from 62 centers were included. Of these, 6810 patients (25%) were extubated in the operating room (OR), whereas 20,588 patients (75%) arrived intubated in the ICU. Of the patients who were extubated in the OR, 395 patients (6%) required reintubation. In contrast, 2054 patients (10%) required reintubation among the patients arriving intubated postoperatively in the ICU. In adjusted models, patient characteristics, patients undergoing high-complexity operations, and patients undergoing operations in lower-volume centers were associated with higher likelihood for the need for postoperative mechanical ventilation and need for reintubation. Furthermore, the prevalence of mechanical ventilation and reintubation was lower among the centers with a dedicated cardiac ICU in propensity-matched analysis among centers with and without a dedicated cardiac ICU.

Conclusions: This multicenter study suggests that proportion of patients extubated in the OR after heart operation is low. These data further suggest that extubation in the OR can be done successfully with a low complication rate.
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http://dx.doi.org/10.1016/j.jtcvs.2015.09.080DOI Listing
February 2016

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

Acta Paediatr 2016 Feb 13;105(2):e60-6. Epub 2015 Nov 13.

Virtual PICU Systems, LLC, Los Angeles, CA, USA.

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.
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http://dx.doi.org/10.1111/apa.13223DOI Listing
February 2016

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

Pediatr Crit Care Med 2015 Sep;16(7):e207-16

1Division of Critical Care, Department of Pediatrics, Rainbow Babies and Children's Hospital, Cleveland, OH. 2Virtual PICU Systems LLC, Los Angeles, CA. 3National Outcomes Center, Children's Hospital of Wisconsin, Milwaukee, Wisconsin. 4Pediatric Critical Care Medicine, Department of Pediatrics, Virginia Tech Carilion School of Medicine, Roanoke, VA. 5National Center for Disaster Preparedness, Columbia University, New York, NY. 6Department of Anesthesiology Critical Care Medicine, Children's Hospital of Los Angeles, Los Angeles, CA.

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.
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http://dx.doi.org/10.1097/PCC.0000000000000481DOI Listing
September 2015

Critical care for rare diseases (and procedures): redux.

Pediatr Crit Care Med 2015 Mar;16(3):297-9

Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD Division of Health Science Informatics, Johns Hopkins University School of Medicine, Baltimore, MD Department of Anesthesiology Critical Care Medicine, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA.

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http://dx.doi.org/10.1097/PCC.0000000000000360DOI Listing
March 2015

Variation of ventilation practices with center volume after pediatric heart surgery.

Clin Cardiol 2015 Mar 23;38(3):178-84. Epub 2015 Feb 23.

Division of Pediatric Critical Care, Department of Pediatrics, University of Arkansas Medical Center, Little Rock, Arkansas; Division of Pediatric Cardiology, Department of Pediatrics, University of Arkansas Medical Center, Little Rock, Arkansas.

Background: This study was designed to evaluate the odds of mechanical ventilation and duration of mechanical ventilation after pediatric cardiac surgery across centers of varying center volume using the Virtual PICU Systems database.

Hypothesis: Children receiving cardiac surgery at high-volume centers will be associated with lower odds of mechanical ventilation and shorter duration of mechanical ventilation, compared with low-volume centers.

Methods: Patients age <18 years undergoing operations (with or without cardiopulmonary bypass) for congenital heart disease at one of the participating intensive care units in the Virtual PICU Systems database were included (2009-2013). Logistic regression models and Cox proportional hazards models were fitted for the probability of conventional mechanical ventilation and duration of mechanical ventilation, respectively, to investigate the difference in the outcomes between different center volume groups with/without adjustment for other risk factors.

Results: A total of 10 378 patients from 43 centers qualified for inclusion. Of these, 7648 (74%) patients received conventional mechanical ventilation after cardiac surgery. Higher center volume was significantly associated with lower odds of mechanical ventilation after cardiac surgery (odds ratio: 2.68, 95% confidence interval: 2.15-3.35). However, patients receiving mechanical ventilation in these centers were associated with longer duration of mechanical ventilation, compared with lower-volume centers (hazard ratio: 1.26, 95% confidence interval: 1.16-1.37). This association was most prominent in the lower surgical-risk categories.

Conclusions: Large clinical practice variations were demonstrated for mechanical ventilation following pediatric cardiac surgery among intensive care units of varied center volumes.
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http://dx.doi.org/10.1002/clc.22374DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6710997PMC
March 2015

It is too early to declare early or late rescue high-frequency oscillatory ventilation dead--reply.

JAMA Pediatr 2014 Sep;168(9):863

Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles.

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http://dx.doi.org/10.1001/jamapediatrics.2014.934DOI Listing
September 2014

Epidemiology and outcomes of in-hospital cardiac arrest in critically ill children across hospitals of varied center volume: a multi-center analysis.

Resuscitation 2014 Nov 7;85(11):1473-9. Epub 2014 Aug 7.

Virtual PICU Systems, LLC, Los Angeles, CA, United States; Division of Critical Care Medicine, Department of Pediatrics and Anesthesiology, Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA, United States.

Objective: To describe epidemiology and outcomes associated with cardiac arrest among critically ill children across hospitals of varying center volumes.

Methods: Patients <18 years of age in the Virtual PICU Systems (VPS, LLC) Database (2009-2013) were included. Patients with both cardiac and non-cardiac diagnoses were included. Data on demographics, patient diagnosis, cardiac arrest, severity of illness and outcomes were collected. Hierarchical cluster analysis was performed to categorize all the participating centers into low, low-medium, high-medium, and high volume groups using the center volume characteristics (annual hospital discharges per center, annual extracorporeal membrane oxygenation per center, and annual mechanical ventilators per center). Multivariable models were used to evaluate association of center volume with incidence of cardiac arrest, and mortality after cardiac arrest, adjusting for patient and center characteristics.

Results: Of 329,982 patients (108 centers), 2.2% (n=7390) patients had cardiac arrest with an associated mortality of 35% (n=2586). In multivariable models controlling for patient and center characteristics, center volume was not associated with either the incidence of cardiac arrest (OR: 1.00; 95% CI: 0.95-1.06; p=0.98), or mortality in those with cardiac arrest (OR: 0.93; 95% CI: 0.82-1.06; p=0.27). These associations were similar across cardiac and non-cardiac disease categories. Furthermore, we demonstrated that there was no correlation between incidence of cardiac arrest and mortality in those with cardiac arrest across different study hospitals in adjusted models.

Conclusions: Both incidence of cardiac arrest, and mortality in those with cardiac arrest vary substantially across hospitals. However, center volume is not associated with either of these outcomes, after adjusting for patient and center characteristics.
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http://dx.doi.org/10.1016/j.resuscitation.2014.07.016DOI Listing
November 2014

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

Ann Allergy Asthma Immunol 2014 Jul 14;113(1):42-7. Epub 2014 May 14.

Virtual PICU Systems, LLC, Los Angeles, California; Division of Critical Care Medicine, Department of Pediatrics and Anesthesiology, Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, California.

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.
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http://dx.doi.org/10.1016/j.anai.2014.04.020DOI Listing
July 2014

Comparison of high-frequency oscillatory ventilation and conventional mechanical ventilation in pediatric respiratory failure.

JAMA Pediatr 2014 Mar;168(3):243-9

Virtual PICU Systems LLC, Los Angeles, California8Department of Anesthesiology, Keck School of Medicine, University of Southern California, Los Angeles9Division of Pediatric Critical Care, Department of Pediatrics, Keck School of Medicine, University of S.

Importance: Outcomes associated with use of high-frequency oscillatory ventilation (HFOV) in children with acute respiratory failure have not been established.

Objective: To compare the outcomes of HFOV with those of conventional mechanical ventilation (CMV) in children with acute respiratory failure.

Design, Setting, And Participants: We performed a retrospective, observational study using deidentified data obtained from all consecutive patients receiving mechanical ventilation aged 1 month to 18 years in the Virtual PICU System database from January 1, 2009, through December 31, 2011. The study population was divided into 2 groups: HFOV and CMV. The HFOV group was further divided into early and late HFOV. Propensity score matching was performed as a 1-to-1 match of HFOV and CMV patients. A similar matching process was performed for early HFOV and CMV patients.

Exposure: High-frequency oscillatory ventilation.

Main Outcomes And Measures: Length of mechanical ventilation, intensive care unit (ICU) length of stay, ICU mortality, and standardized mortality ratio (SMR).

Results: A total of 9177 patients from 98 hospitals qualified for inclusion. Of these, 902 (9.8%) received HFOV, whereas 8275 (90.2%) received CMV. A total of 1764 patients were matched to compare HFOV and CMV, whereas 942 patients were matched to compare early HFOV and CMV. Length of mechanical ventilation (CMV vs HFOV: 14.6 vs 20.3 days, P < .001; CMV vs early HFOV: 14.6 vs 15.9 days, P < .001), ICU length of stay (19.1 vs 24.9 days, P < .001; 19.3 vs 19.5 days, P = .03), and mortality (8.4% vs 17.3%, P < .001; 8.3% vs 18.1%, P < .001) were significantly higher in HFOV and early HFOV patients compared with CMV patients. The SMR in the HFOV group was 2.00 (95% CI, 1.71-2.35) compared with an SMR in the CMV group of 0.85 (95% CI, 0.68-1.07). The SMR in the early HFOV group was 1.62 (95% CI, 1.31-2.01) compared with an SMR in the CMV group of 0.76 (95% CI, 0.62-1.16).

Conclusions And Relevance: Application of HFOV and early HFOV compared with CMV in children with acute respiratory failure is associated with worse outcomes. The results of our study are similar to recently published studies in adults comparing these 2 modalities of ventilation for acute respiratory distress syndrome.
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http://dx.doi.org/10.1001/jamapediatrics.2013.4463DOI Listing
March 2014

Algorithms to estimate PaCO2 and pH using noninvasive parameters for children with hypoxemic respiratory failure.

Respir Care 2014 Aug;59(8):1248-57

Background: Ventilator management for children with hypoxemic respiratory failure may benefit from ventilator protocols, which rely on blood gases. Accurate noninvasive estimates for pH or P(aCO2) could allow frequent ventilator changes to optimize lung-protective ventilation strategies. If these models are highly accurate, they can facilitate the development of closed-loop ventilator systems. We sought to develop and test algorithms for estimating pH and P(aCO2) from measures of ventilator support, pulse oximetry, and end-tidal carbon dioxide pressure (P(ETCO2)). We also sought to determine whether surrogates for changes in dead space can improve prediction.

Methods: Algorithms were developed and tested using 2 data sets from previously published investigations. A baseline model estimated pH and P(aCO2) from P(ETCO2) using the previously observed relationship between P(ETCO2) and P(aCO2) or pH (using the Henderson-Hasselbalch equation). We developed a multivariate gaussian process (MGP) model incorporating other available noninvasive measurements.

Results: The training data set had 2,386 observations from 274 children, and the testing data set had 658 observations from 83 children. The baseline model predicted P(aCO2) within ± 7 mm Hg of the observed P(aCO2) 80% of the time. The MGP model improved this to ± 6 mm Hg. When the MGP model predicted P(aCO2) between 35 and 60 mm Hg, the 80% prediction interval narrowed to ± 5 mm Hg. The baseline model predicted pH within ± 0.07 of the observed pH 80% of the time. The MGP model improved this to ± 0.05.

Conclusions: We have demonstrated a conceptual first step for predictive models that estimate pH and P(aCO2) to facilitate clinical decision making for children with lung injury. These models may have some applicability when incorporated in ventilator protocols to encourage practitioners to maintain permissive hypercapnia when using high ventilator support. Refinement with additional data may improve model accuracy.
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http://dx.doi.org/10.4187/respcare.02806DOI Listing
August 2014

The parsimonious pediatric index of mortality*.

Pediatr Crit Care Med 2013 Sep;14(7):718-9

Department of Pediatrics, UC Davis Children's Hospital, Sacramento, CA Department of Anesthesia and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA.

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http://dx.doi.org/10.1097/PCC.0b013e3182a1251bDOI Listing
September 2013

Are all ICUs the same?

Paediatr Anaesth 2011 Jul 9;21(7):787-93. Epub 2011 May 9.

Department of Anesthesiology, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

The ability to compare intensive care units (ICUs) and determine whether they provide the same level of care with regard to efficacy, efficiency, and quality is a cornerstone of understanding critical care and improving the quality of that care. Without collecting high-quality data, adjusted for severity of illness and analyzed in a comparative fashion, it would not be possible to describe best practices objectively, to identify which ICUs are doing a good job or to learn from those units that are. This review article discusses how and why ICUs are compared. Particular attention is focused on the severity of illness scores, standardized mortality, and comparative reporting. A data collecting network, Virtual Pediatric Systems, limited liability corporation (VPS, LLC), designed for the purposes of determining where differences in critical care can be identified and the value that this adds in improving quality is discussed. Finally, results from this large data sharing collaborative describing the practice of pediatric critical care are included for the purpose of pediatric intensive care units practice benchmarks.
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http://dx.doi.org/10.1111/j.1460-9592.2011.03595.xDOI Listing
July 2011

Who is doing what to whom: a large prospective study of propofol anesthesia in children.

Authors:
Randall C Wetzel

Anesth Analg 2009 Mar;108(3):695-8

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http://dx.doi.org/10.1213/ane.0b013e31819614c8DOI Listing
March 2009

Databases for assessing the outcomes of the treatment of patients with congenital and paediatric cardiac disease--the perspective of critical care.

Cardiol Young 2008 Dec;18 Suppl 2:130-6

Department of Paediatric Intensive Care, The Royal Brompton Hospital, London, United Kingdom.

The development of databases to track the outcomes of children with cardiovascular disease has been ongoing for much of the last two decades, paralleled by the rise of databases in the intensive care unit. While the breadth of data available in national, regional and local databases has grown exponentially, the ability to identify meaningful measurements of outcomes for patients with cardiovascular disease is still in its early stages. In the United States of America, the Virtual Pediatric Intensive Care Unit Performance System (VPS) is a clinically based database system for the paediatric intensive care unit that provides standardized high quality, comparative data to its participants [https://portal.myvps.org/]. All participants collect information on multiple parameters: (1) patients and their stay in the hospital, (2) diagnoses, (3) interventions, (4) discharge, (5) various measures of outcome, (6) organ donation, and (7) paediatric severity of illness scores. Because of the standards of quality within the database, through customizable interfaces, the database can also be used for several applications: (1) administrative purposes, such as assessing the utilization of resources and strategic planning, (2) multi-institutional research studies, and (3) additional internal projects of quality improvement or research.In the United Kingdom, The Paediatric Intensive Care Audit Network is a database established in 2002 to record details of the treatment of all critically ill children in paediatric intensive care units of the National Health Service in England, Wales and Scotland. The Paediatric Intensive Care Audit Network was designed to develop and maintain a secure and confidential high quality clinical database of pediatric intensive care activity in order to meet the following objectives: (1) identify best clinical practice, (2) monitor supply and demand, (3) monitor and review outcomes of treatment episodes, (4) facilitate strategic healthcare planning, (5) quantify resource requirements, and (6) study the epidemiology of critical illness in children.Two distinct physiologic risk adjustment methodologies are the Pediatric Risk of Mortality Scoring System (PRISM), and the Paediatric Index of Mortality Scoring System 2 (PIM 2). Both Pediatric Risk of Mortality (PRISM 2) and Pediatric Risk of Mortality (PRISM 3) are comprised of clinical variables that include physiological and laboratory measurements that are weighted on a logistic scale. The raw Pediatric Risk of Mortality (PRISM) score provides quantitative measures of severity of illness. The Pediatric Risk of Mortality (PRISM) score when used in a logistic regression model provides a probability of the predicted risk of mortality. This predicted risk of mortality can then be used along with the rates of observed mortality to provide a quantitative measurement of the Standardized Mortality Ratio (SMR). Similar to the Pediatric Risk of Mortality (PRISM) scoring system, the Paediatric Index of Mortality (PIM) score is comprised of physiological and laboratory values and provides a quantitative measurement to estimate the probability of death using a logistic regression model.The primary use of national and international databases of patients with congenital cardiac disease should be to improve the quality of care for these patients. The utilization of common nomenclature and datasets by the various regional subspecialty databases will facilitate the eventual linking of these databases and the creation of a comprehensive database that spans conventional geographic and subspecialty boundaries.
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http://dx.doi.org/10.1017/S1047951108002886DOI Listing
December 2008

Calcium: a double-edged sword.

Authors:
Randall C Wetzel

Pediatr Crit Care Med 2007 May;8(3):300-1

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http://dx.doi.org/10.1097/01.PCC.0000264315.37398.0BDOI Listing
May 2007

Positive end-expiratory pressure and pressure support in peripheral airways obstruction : work of breathing in intubated children.

Intensive Care Med 2007 Jan 17;33(1):120-7. Epub 2006 Nov 17.

Department of Pediatrics, Division of Pediatric Critical Care, Oregon Health and Science University, 707 S.W. Gaines Street, Portland, OR 97239-2901, USA.

Objectives: Children with peripheral airways obstruction suffer the negative effects of intrinsic positive end-expiratory pressure: increased work of breathing and difficulty triggering assisted ventilatory support. We examined whether external positive end-expiratory pressure to offset intrinsic positive end-expiratory pressure decreases work of breathing in children with peripheral airways obstruction. The change in work of breathing with incremental pressure support was also tested.

Design And Setting: Prospective clinical trial in a pediatric intensive care unit.

Patients: Eleven mechanically ventilated, spontaneously breathing children with peripheral airways obstruction.

Interventions: Work of breathing (using pressure-rate product as a surrogate) was measured in three tiers: (a) Increasing pressure support over zero end-expiratory pressure. (b) Increasing applied positive end-expiratory pressure and fixed pressure support. The level of applied positive end-expiratory pressure at which pressure-rate product was least determined the compensatory positive end-expiratory pressure. (c) Increasing pressure support over compensatory (fixed) positive end-expiratory pressure.

Measurements And Results: Increases in pressure support alone decreased pressure-rate product from mean 724+/-311 to 403+/-192 cmH2O/min. Applied positive end-expiratory pressure alone decreased pressure-rate product from mean 608+/-301 to 250+/-169 cmH2O/min. The lowest pressure-rate product (136+/-128 cmH2O/min) was achieved using compensatory positive end-expiratory pressure (12+/-4 cmH2O) with pressure support 16 cmH2O.

Conclusions: For children with peripheral airways obstruction who require assisted ventilation, work of breathing during spontaneous breaths is decreased by the application of either compensatory positive end-expiratory pressure or pressure support.
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http://dx.doi.org/10.1007/s00134-006-0445-6DOI Listing
January 2007