The incidences of bleeding and thrombosis are high during ECMO support. Laboratory sampling is a major contributor to transfusion during ECMO. Strategies to reduce the daily risk of bleeding and thrombosis, and different thresholds for transfusion, may be appropriate subjects of future trials to improve outcomes of children requiring this supportive therapy.

Objectives Severity of illness measures have long been used in pediatric critical care. The Pediatric Risk of Mortality is a physiologically based score used to quantify physiologic status, and when combined with other independent variables, it can compute expected mortality risk and expected morbidity risk. Although the physiologic ranges for the Pediatric Risk of Mortality variables have not changed, recent Pediatric Risk of Mortality data collection improvements have been made to adapt to new practice patterns, minimize bias, and reduce potential sources of error. These include changing the outcome to hospital survival/death for the first PICU admission only, shortening the data collection period and altering the Pediatric Risk of Mortality data collection period for patients admitted for “optimizing” care before cardiac surgery or interventional catheterization. This analysis incorporates those changes, assesses the potential for Pediatric Risk of Mortality physiologic variable subcategories to improve score performance, and recalibrates the Pediatric Risk of Mortality score, placing the algorithms (Pediatric Risk of Mortality IV) in the public domain. Design Prospective cohort study from December 4, 2011, to April 7, 2013. Measurements and Main Results Among 10,078 admissions, the unadjusted mortality rate was 2.7% (site range, 1.3–5.0%). Data were divided into derivation (75%) and validation (25%) sets. The new Pediatric Risk of Mortality prediction algorithm (Pediatric Risk of Mortality IV) includes the same Pediatric Risk of Mortality physiologic variable ranges with the subcategories of neurologic and nonneurologic Pediatric Risk of Mortality scores, age, admission source, cardiopulmonary arrest within 24 hours before admission, cancer, and low-risk systems of primary dysfunction. The area under the receiver operating characteristic curve for the development and validation sets was 0.88 ± 0.013 and 0.90 ± 0.018, respectively. The Hosmer-Lemeshow goodness of fit statistics indicated adequate model fit for both the development (p = 0.39) and validation (p = 0.50) sets. Conclusions The new Pediatric Risk of Mortality data collection methods include significant improvements that minimize the potential for bias and errors, and the new Pediatric Risk of Mortality IV algorithm for survival and death has excellent prediction performance.

Objective Assessments of care including quality assessments adjusted for physiological status should include the development of new morbidities as well as mortalities. We hypothesized that morbidity, like mortality, is associated with physiological dysfunction and could be predicted simultaneously with mortality. Design Prospective cohort study from December 4, 2011 to April 7, 2013. Setting and Patients General and cardiac/cardiovascular pediatric intensive care units at 7 sites. Measurements and Main Results Among 10,078 admissions, the unadjusted morbidity rates (measured with the Functional Status Scale (FSS), and defined as an increase of ≥ 3 from pre-illness to hospital discharge) were 4.6% (site range 2.6% to 7.7%) and unadjusted mortality rates were 2.7% (site range 1.3% – 5.0%). Morbidity and mortality were significantly (p<0.001) associated with physiological instability (measured with the PRISM III score) in dichotomous (survival, death) and trichotomous (survival without new morbidity, survival with new morbidity, death) models without covariate adjustments. Morbidity risk increased with increasing PRISM III scores and then decreased at the highest PRISM III values as potential morbidities became mortalities. The trichotomous model with covariate adjustments included age, admission source, diagnostic factors, baseline FSS and the PRISM III score. The three-level goodness of fit test indicated satisfactory performance for the derivation and validation sets (p>0.20). Predictive ability assessed with the volume under the surface (VUS) was 0.50 ± 0.019 (derivation) and 0.50 ± 0.034 (validation) (versus chance performance = 0.17). Site-level standardized morbidity ratios were more variable than standardized mortality ratios. Conclusions New morbidities were associated with physiological status and can be modeled simultaneously with mortality. Trichotomous outcome models including both morbidity and mortality based on physiological status are suitable for research studies, and quality and other outcome assessments. This approach may be applicable to other assessments presently based only on mortality.

Difficult TI was reported in 9% of all TIs and was associated with increased adverse TI events. History of difficult airway and sign of upper airway obstruction were associated with difficult TIs.

Objective To determine the incidence of cardiopulmonary resuscitation (CPR) in pediatric intensive care units (PICU) and subsequent outcomes. Design, Setting, and Patients Multi-center prospective observational study of children 30 minutes, p30 minutes of CPR. Conclusions These data establish that contemporary PICU CPR, including long durations of CPR, results in high rates of survival to hospital discharge (45%) and favorable neurologic outcomes among survivors (89%). Rates of survival with favorable neurologic outcomes were similar among cardiac and non-cardiac patients. The rigorous prospective, observational study design avoided the limitations of missing data and potential selection biases inherent in registry and administrative data.