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A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay.

Kramer AA, Zimmerman JE - BMC Med Inform Decis Mak (2010)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cerner Corporation, Suite 500, Vienna, Virginia 22182, USA. akramer@cerner.com

ABSTRACT

Background: Patients 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.

Methods: We 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.

Results: The 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.

Conclusions: A 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.

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Related in: MedlinePlus

Duration of intensive care unit (ICU) stay and its association with total ICU bed days. Grey bars = % of ICU admissions. White bars = % of overall ICU days.
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Figure 2: Duration of intensive care unit (ICU) stay and its association with total ICU bed days. Grey bars = % of ICU admissions. White bars = % of overall ICU days.

Mentions: Figure 2 displays the percentage of ICU admissions and total ICU days respectively by ICU stay ranges. Admissions staying ≤ 5 days contributed 79% of all admissions but only 37% of all ICU days. Conversely, ICU stays > 30 days occurred 1% of the time, but resulted in 12.5% of all ICU days. Ranges of ICU stay between these two boundaries show that with an increasing ICU stay the percentages of total ICU days increase.


A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay.

Kramer AA, Zimmerman JE - BMC Med Inform Decis Mak (2010)

Duration of intensive care unit (ICU) stay and its association with total ICU bed days. Grey bars = % of ICU admissions. White bars = % of overall ICU days.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2876991&req=5

Figure 2: Duration of intensive care unit (ICU) stay and its association with total ICU bed days. Grey bars = % of ICU admissions. White bars = % of overall ICU days.
Mentions: Figure 2 displays the percentage of ICU admissions and total ICU days respectively by ICU stay ranges. Admissions staying ≤ 5 days contributed 79% of all admissions but only 37% of all ICU days. Conversely, ICU stays > 30 days occurred 1% of the time, but resulted in 12.5% of all ICU days. Ranges of ICU stay between these two boundaries show that with an increasing ICU stay the percentages of total ICU days increase.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cerner Corporation, Suite 500, Vienna, Virginia 22182, USA. akramer@cerner.com

ABSTRACT

Background: Patients 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.

Methods: We 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.

Results: The 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.

Conclusions: A 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.

Show MeSH
Related in: MedlinePlus