Early prediction of delirium in a pediatric cardiac intensive care unit: A pilot study. (September 2021) - Reiter, Pamela D.; Wright, Garth; Good, Ryan; Payan, Marisa; Lieb, Ann; Kaufman, Jon
Abstract: Background: Pediatric delirium is associated with increased risk of mortality, prolonged time to extubation, and increased length of stay. If the risk of delirium could be identified early, then preventative strategies could be targeted. Objectives: To study the performance of an early delirium prediction model in children admitted to a pediatric cardiac intensive care unit using variables extracted from the electronic medical record 24 hours after admission. Methods: This was a single center, IRB approved, retrospective study of children between 31 days and 18 years old who were admitted to the cardiac ICU for at least three consecutive days during a 1-year study period (January 1, 2018 to December 31, 2018). Using fifteen previously defined variables associated with delirium in children, we determined their presence at 24 hours after admission and created a model to predict the risk of delirium anytime during a child's stay. Delirium was considered present if a patient had at least one Cornell Assessment of Pediatric Delirium score of 9 or greater, required an antipsychotic or had an ICD-10 diagnosis of delirium. Repeated measures logistic regression between the preselected variables associated with delirium and the clinical diagnosis of delirium was performed. Variables significant in the unadjusted univariate analyses were entered into a multivariable model for adjustment. Results: A total of 97 patients (113 admits) were included. The presence of four covariates at 24 hours following admit (serum albumin below 3 g/dl, blood transfusion, dexmedetomidine, and mechanical ventilation) were identified as significant predictors of delirium. The model demonstrated good discriminative ability with an area under the receiver operating characteristics curve of 0.74. Conclusion: Early prediction of delirium may be possible, but this model requires additional study and validation in a larger subset of subjects. Identification of patients at high-risk for delirium may facilitate targeted delirium prevention strategies. Highlights: Mortality, prolonged intubation, and increased length of hospital stay are associated with delirium. Application of forecasting tools can calculate risk and target early prevention strategies. Variables present at 24 h after admit can predict delirium with good discriminative ability. Leveraging the electronic medical record to alert providers of high-risk patients may reduce disease burden.