Incidence of cardiac arrest following implementation of a predictive analytics display in a pediatric intensive care unit.

BACKGROUND

More than 90% of in-hospital cardiac arrests involving children occur in an intensive care unit (ICU) with less than half surviving to discharge. We sought to assess the association of the display of risk scores of cardiovascular and respiratory instability with the incidence of cardiac arrest in a pediatric ICU.

METHODS

Employing supervised machine learning, we previously developed predictive models of cardiovascular and respiratory instability, incorporating real-time physiologic and laboratory data, to display risk scores for potentially catastrophic clinical events in the subsequent 12 h. Clinical implementation with risk scores displayed on large screen monitors in multiple areas throughout the ICU was finalized in July 2022. We compared the incidence of cardiac arrest events in the 18-months pre- and post-implementation.

RESULTS

The cardiac arrest incidence rate dropped from 3.0 events (95% CI 2.0-4.4) to 2.4 events (95% CI 1.6-3.5) per 1000 patient days following implementation. We observed a 50% increase in the rate of cardiac arrest events where return of spontaneous circulation (ROSC) was achieved ( = 0.025). The incidence rate of cardiac arrest without ROSC dropped from 1.4 events (95% CI 0.7-2.4) to 0.4 events (95% CI 0.1-0.9) per 1000 patient days (incidence rate difference = 1.0 (95% CI 0.13-1.87),  = 0.01).

CONCLUSIONS

We observed a non-significant decrease in the rates of cardiac arrest events and an increase in the rate of cardiac arrests events where ROSC was achieved following the implementation of a predictive analytics display of risk scores.