To create a novel tool to predict favorable neurologic outcomes during ICU stay among children with critical illness.
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.
Virtual Pediatric Systems (VPS, LLC, Los Angeles, CA) database.
Patients less than 18 years old admitted to one of the participating ICUs in the Virtual Pediatric Systems database were included (2009–2015).
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.
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.
1Division of Pediatric Cardiology, Department of Pediatrics, University of Arkansas for Medical Sciences, Arkansas Children's Research Institute, Little Rock, AR.
2Section of Biostatistics, Department of Pediatrics, University of Arkansas for Medical Sciences, Arkansas Children's Research Institute, Little Rock, AR.
3Virtual Pediatric Systems, LLC, Los Angeles, CA.
4Division of Critical Care, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, WI.
5Department of Critical Care and Anesthesiology, Children’s Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA.
*See also p. 167
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Virtual Pediatric Systems data were provided by VPS, LLC. No endorsement or editorial restriction of the interpretation of these data or opinions of the authors has been implied or stated.
Dr. Rice received funding from Virtual Pediatric Systems, LLC (employer), and American Academy of Pediatrics (consultant, expired March 2016). The remaining authors have disclosed that they do not have any potential conflicts of interest
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