Abstract
BackgroundPatients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay.MethodsWe performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model.ResultsThe variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO2: FiO2 ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r2 was 20.2% across individuals and 44.3% across units.ConclusionsA model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.
Highlights
Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use
Length of stay is frequently used as a measure of ICU resource use, but there is no uniform definition of what constitutes a prolonged ICU stay [1,2]
Because models that include daily physiologic measures during therapy have improved the accuracy of daily mortality predictions [21,22,23,24], we examined whether a similar approach might improve predictions of ICU stay, when length of stay is prolonged
Summary
Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. A simplistic method uses the mean along with the standard deviation of length of stay for a population to assign a boundary of two standard deviations above the mean This is unsatisfactory, because the distribution of ICU length of stay is left-censored at zero and is heavily skewed to the Despite differences in definition, studies have repeatedly shown that a small percentage (7% to 11%) of lengthy ICU admissions account for a large proportion (40% to 50%) of resource use [8,9,10]. Resources, their early identification can assist in improving unit efficiency This is because identifying these individuals early can improve patient throughput by signalling a need for discharge planning or exploration of care alternatives. These alternatives might include palliative care consultation [11], early mobility therapy [12,13], transfer to an in-hospital chronic ventilator unit [14], or discharge to a long-term acute care facility [15,16]
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