Fryer, Karen

OBJECTIVES: With increasing complexity of critical care medicine comes an increasing need for multidisciplinary involvement in care. In many institutions, pharmacists are an integral part of this team, but long-term data on the interventions performed by pharmacists and their effects on patient care and outcomes are limited. We aimed to describe the role of pediatric clinical pharmacists in pediatric intensive care unit (PICU) practice. METHODS: We retrospectively reviewed the records of pharmacy interventions in the PICU at the Mayo Clinic in Rochester, Minnesota, from 2003-2013, with a distinct period of increased pharmacist presence in the PICU from 2008 onward. We compared demographic and outcome data on patients who did and who did not have pharmacy interventions during 2 periods (2003–2007 and 2008–2013). RESULTS: We identified 27,773 total interventions by pharmacists during the 11-year period, of which 79.8% were accepted by the clinical team. These interventions were made on 10,963 unique PICU admissions and prevented 5867 order entry errors. Pharmacists' interventions increased year over year, including a significant change in 2008. Patients who required pharmacy involvement were younger, sicker, and had longer intensive care unit, hospital, and ventilator duration. Average central line infections and central line entry rates decreased significantly over the study period. CONCLUSIONS: Increased pharmacist presence in the PICU is associated with increased interventions and prevention of adverse drug events. Pharmacist participation during rounds and order entry substantially improved the care of critically sick children and should be encouraged.

BACKGROUNDDelirium is associated with poor outcomes in adults but is less extensively studied in children.OBJECTIVESTo describe a quality improvement initiative to implement delirium assessment in a pediatric intensive care unit and to identify barriers to delirium screening completion.METHODSA survey identified perceived barriers to delirium assessment. Failure modes and effects analysis characterized factors likely to impede assessment. A randomized case-control study evaluated factors affecting assessment by comparing patients always assessed with patients never assessed.RESULTSDelirium assessment was completed in 57% of opportunities over 1 year, with 2% positive screen results. Education improved screening completion by 20%. Barriers to assessment identified by survey (n = 25) included remembering to complete assessments, documentation outside workflow, and “busy patient.” Factors with high risk prediction numbers were lack of time and paper charting. Patients always assessed had more severe illness (median Pediatric Index of Mortality 2 score, 0.90 vs 0.36; P < .001), more developmental disabilities (moderate to severe pediatric cerebral performance category score, 54% vs 32%; P = .007), and admission during lower pediatric intensive care unit census (median [interquartile range], 10 [9–12] vs 12 [10–13]; P < .001) than did those never assessed (each group, n = 80). Patients receiving mechanical ventilation were less likely to be assessed (41.0% vs 51.2%, P < .001).CONCLUSIONSSuccessful implementation of pediatric delirium screening may be associated with early use of quality improvement tools to identify assessment barriers, comprehensive education, monitoring system with feedback, multidisciplinary team involvement, and incorporation into nursing workflow models.

Objective: No risk prediction model is currently available to measure patient’s probability for readmission to the pediatric intensive care unit (PICU). This retrospective case–control study was designed to assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer [SWIFT]) and to create a pediatric version (PRediction Of PICU Early Readmissions [PROPER]). Design: Eighty-six unplanned early (<48 hours) PICU readmissions from January 07, 2007, to June 30, 2014, were compared with 170 random controls. Patient- and disease-specific data and PICU workload factors were compared across the 2 groups. Factors statistically significant on multivariate analysis were included in the creation of the risk prediction model. The SWIFT scores were calculated for cases and controls and compared for validation. Results: Readmitted patients were younger, weighed less, and were more likely to be admitted from the emergency department. There were no differences in gender, race, or admission Pediatric Index of Mortality scores. A higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category in the moderate to severe disability category. Cases and controls did not differ with respect to staff workload at discharge or discharge day of the week; there was a much higher proportion of patients on supplemental oxygen in the readmission group. Only 2 of 5 categories in the SWIFT model were significantly different, and although the median SWIFT score was significantly higher in the readmissions group, the model discriminated poorly between cases and controls (area under the curve: 0.613). A 7-category PROPER score was created based on a multiple logistic regression model. Sensitivity of this model (score ≥12) for the detection of readmission was 81% with a positive predictive value of 0.50. Conclusion: We have created a preliminary model for predicting patients at risk of early readmissions to the PICU from the hospital floor. The SWIFT score is not applicable for predicting the risk for pediatric population.