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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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Comparison of observed and predicted intensive care unit (ICU) length of stay. Mean observed (ICU) length of stay (white bar), mean predicted length of stay based on the day 5 model [5 days + predicted remaining length of stay after day 5] (gray bar), and mean predicted length of stay based on day 1 model (black bar).
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Figure 6: Comparison of observed and predicted intensive care unit (ICU) length of stay. Mean observed (ICU) length of stay (white bar), mean predicted length of stay based on the day 5 model [5 days + predicted remaining length of stay after day 5] (gray bar), and mean predicted length of stay based on day 1 model (black bar).

Mentions: Comparison of mean observed and mean predicted total ICU stay using the APACHE IV ICU day 1 model versus the predicted remaining ICU stay added to the threshold number of days (5 days + predicted remaining ICU stay) demonstrates the usefulness of the model for predicting lengthy ICU stays. Figure 6 shows the mean ICU length of stay values for the development, internal validation, and external validation data sets, respectively. In each data set the sum of the day 5 prediction + 5 days was much closer to the observed ICU stay than the day 1 prediction. For the external validation data set, mean predicted total ICU stay was 11.58 days and mean observed ICU stay was 11.99 days, a difference of 9.7 hours (p < 0.001); using the day 1 prediction the difference between observed and predicted ICU stay was 149.3 hours (p < 0.001).


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)

Comparison of observed and predicted intensive care unit (ICU) length of stay. Mean observed (ICU) length of stay (white bar), mean predicted length of stay based on the day 5 model [5 days + predicted remaining length of stay after day 5] (gray bar), and mean predicted length of stay based on day 1 model (black bar).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Comparison of observed and predicted intensive care unit (ICU) length of stay. Mean observed (ICU) length of stay (white bar), mean predicted length of stay based on the day 5 model [5 days + predicted remaining length of stay after day 5] (gray bar), and mean predicted length of stay based on day 1 model (black bar).
Mentions: Comparison of mean observed and mean predicted total ICU stay using the APACHE IV ICU day 1 model versus the predicted remaining ICU stay added to the threshold number of days (5 days + predicted remaining ICU stay) demonstrates the usefulness of the model for predicting lengthy ICU stays. Figure 6 shows the mean ICU length of stay values for the development, internal validation, and external validation data sets, respectively. In each data set the sum of the day 5 prediction + 5 days was much closer to the observed ICU stay than the day 1 prediction. For the external validation data set, mean predicted total ICU stay was 11.58 days and mean observed ICU stay was 11.99 days, a difference of 9.7 hours (p < 0.001); using the day 1 prediction the difference between observed and predicted ICU stay was 149.3 hours (p < 0.001).

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