Abstract

BackgroundThere are not enough clinical data from rare critical events to calculate statistics to decide if the management of actual events might be below what could reasonably be expected (i.e. was an outlier).ObjectivesIn this project we used simulation to describe the distribution of management times as an approach to decide if the management of a simulated obstetrical crisis scenario could be considered an outlier.DesignTwelve obstetrical teams managed 4 scenarios that were previously developed. Relevant outcome variables were defined by expert consensus. The distribution of the response times from the teams who performed the respective intervention was graphically displayed and median and quartiles calculated using rank order statistics.ResultsOnly 7 of the 12 teams performed chest compressions during the arrest following the ‘cannot intubate/cannot ventilate’ scenario. All other outcome measures were performed by at least 11 of the 12 teams. Calculation of medians and quartiles with 95% CI was possible for all outcomes. Confidence intervals, given the small sample size, were large.ConclusionWe demonstrated the use of simulation to calculate quantiles for management times of critical event. This approach could assist in deciding if a given performance could be considered normal and also point to aspects of care that seem to pose particular challenges as evidenced by a large number of teams not performing the expected maneuver. However sufficiently large sample sizes (i.e. from a national data base) will be required to calculate acceptable confidence intervals and to establish actual tolerance limits.

Highlights

  • We demonstrated the use of simulation to calculate quantiles for management times of critical event

  • This approach could assist in deciding if a given performance could be considered normal and point to aspects of care that seem to pose particular challenges as evidenced by a large number of teams not performing the expected maneuver

  • Due to the rare occurrence of many critical events, it is usually difficult to gather real life data of sufficient quality and quantity to decide if the management of an actual event fell below what could reasonably be expected of a peer

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Summary

Introduction

Due to the rare occurrence of many critical events, it is usually difficult to gather real life data of sufficient quality and quantity to decide if the management of an actual event fell below what could reasonably be expected of a peer. One approach could be to collect data from realistic crisis simulations and try to define what constitutes an outlier among various performances by plotting the outcome variable in question (for example the time from cardiac arrest to delivery of the neonate, or the time from asystole to chest compression) from a number of different teams. There are not enough clinical data from rare critical events to calculate statistics to decide if the management of actual events might be below what could reasonably be expected (i.e. was an outlier)

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