A pragmatic checklist to identify pediatric ICU patients at risk for cardiac arrest or code bell activation



Background

In-hospital cardiac arrest is a rare event associated with significant morbidity and mortality. The ability to identify the ICU patients at risk for cardiac arrest could allow the clinical team to prepare staff and equipment in anticipation.

Methods

This pilot study was completed at a large tertiary care pediatric intensive care unit to determine the feasibility of a simple checklist of clinical variables to predict deterioration. The daily checklist assessed patient risk for critical deterioration defined as cardiac arrest or code bell activation within 24 h of the checklist screen. The Phase I checklist was developed by expert consensus and evaluated to determine standard diagnostic test performance. A modified Phase II checklist was developed to prospectively test the feasibility and bedside provider “number needed to train”.

Results

For identifying patients requiring code bell activation, both checklists demonstrated a sensitivity of 100% with specificity of 76.0% during Phase I and 97.7% during Phase II. The positive likelihood ratio improved from 4.2 to 43.7. For identifying patients that had a cardiac arrest within 24 h, the Phase I and II checklists demonstrated a sensitivity of 100% with specificity again improving from 75.7% to 97.6%. There was an improved positive likelihood ratio from 4.1 in Phase I to 41.9 in Phase II, with improvement of “number needed to train” from 149 to 7.4 providers.

Conclusions

A novel high-risk clinical indicators checklist is feasible and provides timely and accurate identification of the ICU patients at risk for cardiac arrest or code bell activation.

Keywords
Cardiopulmonary resuscitation; Intensive care units; Pediatric; Hospital rapid response team
Abbreviations
CA, cardiac arrest; CBA, code bell activation; CPR, cardiopulmonary resuscitation; NNT, number needed to train; PICU, pediatric intensive care unit

A Spanish translated version of the summary of this article appears as Appendix in the final online version at http://dx.doi.org/10.1016/j.resuscitation.2015.11.017.

Correspondence to: Department of Critical Care, The Children’s Hospital of Philadelphia, 3401 Civic Center Blvd Philadelphia, PA 19104.



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Dana E. Nilesa, Maya Dewana, , , Carleen Zebuhrb, Heather Wolfea, Christopher P. Bonafidea, Robert M. Suttona, Mary Ann DiLibertoa, Lori Boylea, Natalie Napolitanoa, Ryan W. Morgana, Hannah Stinsona, Jessica Leffelmana, Akira Nishisakia, Robert A. Berga, Vinay M. Nadkarnia
a The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
b Children’s Hospital Colorado, Denver, CO, USA
Received 3 August 2015, Revised 8 November 2015, Accepted 26 November 2015, Available online 17 December 2015


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Content from EBPI Course Population: Bariatric adolescents considering or undergoing gastric bypass surgery. Intervention: The nurse’s role as a primary member of the multidisciplinary team regarding perioperative care of the bariatric adolescent patient. Comparison: The nurse’s role as a secondary Continue reading PICO Question Examples

Proper Development of an Early PICU Readmission Risk Prediction Tool



Purpose: Few tools are available to reduce readmissions to PICU, a proposed quality measure reflecting outcomes important to the patient, family and health system. This retrospective case-control study was designed to a) assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer, SWIFT) and, b) create a pediatric version (Prevention Of PICU Early Readmissions, PROPER).
Methods: After IRB approval, 76 unplanned early ( Results: Readmitted patients were younger (33% of readmissions were less than 1 year of age, 24% of controls; p=0.05) and weighed less (45% readmissions had weight < 15 centile, 28% in controls; p=0.009). There were no differences with gender, race or admission Pediatric Index of Mortality (PIM) scores. Higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category (PCPC) in the moderate to severe disability category (42% vs. 25%, p<0.01). More of the readmitted patients were admitted from ED (50% vs. 29% p<0.001) and fewer were postsurgical (32% vs. 52%, p=0.003). No readmission differences were noted for patients admitted in busier PICU seasons or with day of week of discharge. Similarly no difference was observed with median PICU or floor census at the time of PICU discharge. There was a much higher proportion of patients on supplemental oxygen in the readmissions group compared to the non-readmissions group (14% vs 1%, p<0.01). Only 2/5 (Source of admission and GCS score, significant; while last measured P/F ratio and PaCo2, Total PICU LOS, insignificant)) of the categories in SWIFT model were significantly different and the median SWIFT score of 13, was identical in the two groups. A seven category PROPER score (Age 0.21% at discharge, Last GCS<15) was created based on multiple logistic regression model , with weights to the individual components assigned based on the odds ratio of the risk. The AUC for prediction of readmission of this model was 0.757 (0.690 to 0.824).
Conclusion: We have created a preliminary model for predicting patients at risk for early readmissions to the PICU from hospital floor. This score needs to be validated prospectively as well as externally. SWIFT score is not applicable for predicting risk for pediatric population.

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