Predicting the length of stay of patients admitted for intensive care using a first step analysis

For patients admitted to intensive care units (ICU), the length of stay in different destinations after the first day of ICU admission, has not been systematically studied. We aimed to estimate the average length of stay (LOS) of such patients in Colombia, using a discrete time Markov process. We used the maximum likelihood method and Markov chain modeling to estimate the average LOS in the ICU and at each destination after discharge from intensive care. Six Markov models were estimated, describing the LOS in each one of the Cardiovascular, Neurological, Respiratory, Gastrointestinal, Trauma and Other diagnostic groups from the ultimate primary reason for admission to ICU. Possible destinations were: the intensive care unit, ward in the same hospital, the high dependency unit/intermediate care area in the same hospital, ward in other hospital, intensive care unit in other hospital, other hospital, other location same hospital, discharge from same hospital and death. The stationary property was tested and using a split-sample analysis, we provide indirect evidence about the appropriateness of the Markov property. It is not possible to use a unique Markov chain model for each diagnostic group. The length of stay varies across the ultimate primary reason for admission to intensive care. Although our Markov models shown to be predictive, the fact that current available statistical methods do not allow us to verify the Markov property test is a limitation. Clinicians may be able to provide information about the hospital LOS by diagnostic groups for different hospital destinations.