Pediatr Crit Care Me
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To estimate the organ donation potential of patients dying at a children's hospital. ⋯ The number of pediatric potential candidates for donation after circulatory determination of death was significantly larger than potential candidates for donation after neurologic determination of death at our hospital, but the actual donation rate was significantly lower. Increasing acceptance of donation after circulatory determination of death could increase organ donation. Among all children having withdrawal of life-sustaining therapies, donation after circulatory determination of death potential is less for infants.
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Pediatr Crit Care Me · Nov 2015
Multicenter StudyAssociation Between Extracorporeal Membrane Oxygenation Center Volume and Mortality Among Children With Heart Disease: Propensity and Risk Modeling.
To evaluate the relationship between extracorporeal membrane oxygenation center volume and mortality in children undergoing heart operations using propensity score matching in a multiinstitutional cohort. ⋯ We demonstrated no relationship between extracorporeal membrane oxygenation center volume and mortality. Further analyses are needed to evaluate this relationship.
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Pediatr Crit Care Me · Nov 2015
A Novel Method to Identify the Start and End of the Winter Surge in Demand for Pediatric Intensive Care in Real Time.
Implementation of winter surge management in intensive care is hampered by the annual variability in the start and duration of the winter surge. We aimed to develop a real-time monitoring system that could identify the start promptly and accurately predict the end of the winter surge in a pediatric intensive care setting. ⋯ We have developed and tested a novel method to identify the start and predict the end of the winter surge in emergency demand for pediatric intensive care.
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Pediatr Crit Care Me · Nov 2015
Randomized Controlled TrialThe Value of Screening Parents for Their Risk of Developing Psychological Symptoms After PICU: A Feasibility Study Evaluating a Pediatric Intensive Care Follow-Up Clinic.
This study aimed to assess whether prospectively screening parents for psychological vulnerability would enable beneficial targeting of a subsequent follow-up clinic. ⋯ All parents completed Impact of Event Scale-Revised and Hospital Anxiety and Depression Scale at 6 months. Of the 209 parents of 145 children recruited to the study, 78 (37%) were identified, on the basis of their Posttraumatic Adjustment Scale score at baseline, as being at risk of developing posttraumatic stress disorder, and randomized to the control or intervention condition. Follow-up data were provided by 157 of 209 parents (75%). Logistic regression analyses controlling for parent gender and child length of stay showed that high-risk control parents (n = 32) were significantly more likely to score above the clinical cutoff for all three psychological outcomes than parents deemed low risk at baseline (n = 89) (posttraumatic stress: odds ratio = 3.39; 95% CI, 1.28-8.92; p = 0.014; anxiety: odds ratio = 6.34; 95% CI, 2.55-15.76; p < 0.001; depression: odds ratio = 4.13; 95% CI, 1.47-11.61; p = 0.007). Only 14 of 38 (37%) high-risk intervention parents attended the follow-up clinic appointment they were offered. At follow-up, there were no statistically significant differences between the intervention and control groups, but there were small effect sizes in favor of the intervention for anxiety scores (Cohen d = 0.209) and depression scores (Cohen d = 0.254) CONCLUSIONS:: Screening parents for psychological vulnerability using measures such as the Posttraumatic Adjustment Scale may enable more efficient targeting of support. However, further research is needed on how best to provide effective follow-up intervention for families.
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To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. ⋯ Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.