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.

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

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.