Modeling the occurrence of cardiac arrest as a poisson process

A statistical model for the occurrence of cardiac arrest has not been described in the literature. Independent events occurring along the time axis may constitute a Poisson process, described by the Poisson and exponential probability distributions. This statistical model defines the probability distribution of events occurring within time intervals and enables construction of confidence intervals for the mean rate. Moreover, the probability that 2 or more events will occur close in time can be estimated from knowledge of the mean rate. We investigated whether the occurrence of cardiac arrests constitutes a Poisson process.

Time and date for cardiac arrests requiring CPR out of hospital (county population, 155,000) or in hospital (850 beds) during 5 years were analyzed. Goodness of fit was assessed by comparing the observed weekly counts of cardiac arrests and the observed time intervals between such events with the values predicted from the model.

The Poisson parameter estimates (mean weekly rates) for out-of-hospital and in-hospital cardiac arrest were 2.02 and 1.09 events per week, respectively. There was close agreement between observed and predicted values, indicating an adequate model fit.

Occurrence of cardiac arrest along the time axis constitutes a Poisson process and may be adequately modeled by the Poisson and exponential distributions. The model provides information about the nature of these events and allows for probability calculations based on the mean rate of events. Examples of such calculations are given.