Rationale: High mortality and resource use burden are associated with hospitalization of critically ill children transferred from level II pediatric intensive care units (PICUs) to level I PICUs for escalated care. Guidelines urge transfer of the most severely ill children to level I PICUs without specification of either the criteria or the best timing of transfer to achieve good outcomes.
Objectives: To identify factors associated with transfer, develop a modeling framework that uses those factors to determine thresholds to guide transfer decisions, and test these thresholds against actual patient transfer data to determine if delay in transfer could be reduced.
Methods: A multistep approach was adopted, with initial identification of factors associated with transfer status using data from a prior case-control study conducted with children with respiratory failure admitted to six level II PICUs between January 1, 1997, and December 31, 2007. To identify when to transfer a patient, thresholds for transfer were created using generalized estimating equations and discrete event simulation. The transfer policies were then tested against actual transfer data.
Measurements and main results: Multivariate logistic regression revealed that the absolute difference of a patient's pediatric logistic organ dysfunction score from the admission value, high-frequency oscillatory ventilation use, antibiotic use, and blood transfusions were all significantly associated with transfer status. The resulting threshold policies led to average transfer delay reduction ranging from 0.5 to 2.3 days in the testing dataset.
Conclusions: Current transfer guidelines are devoid of criteria to identify critically ill children who might benefit from transfer and when the best time to transfer might be. In this study, we used innovative methods to create thresholds of transfer that might reduce delay in transfer.
Keywords: children; computer simulation; critical illness; intensive care units; patient transfer.