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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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Percentage of model variance attributable to factors used to predict intensive care unit length of stay remaining after day 5. Physiology refers to the acute physiology score (APS) and rescaled Glasgow Coma Score. Rescaled PaO2/FiO2 ratio is defined in Appendix Table 1. GCS unable refers to inability to assess Glasgow Coma score due to sedation or paralysis. Diagnostic category includes 57 mutually exclusive diagnostic groups on day 5. Miscellaneous includes age, prior length of stay, emergency surgery, and ICU readmission.
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Figure 4: Percentage of model variance attributable to factors used to predict intensive care unit length of stay remaining after day 5. Physiology refers to the acute physiology score (APS) and rescaled Glasgow Coma Score. Rescaled PaO2/FiO2 ratio is defined in Appendix Table 1. GCS unable refers to inability to assess Glasgow Coma score due to sedation or paralysis. Diagnostic category includes 57 mutually exclusive diagnostic groups on day 5. Miscellaneous includes age, prior length of stay, emergency surgery, and ICU readmission.

Mentions: For the 12,640 patients in the development data set mean predicted remaining ICU stay after day 5 was 6.87 days and mean observed ICU stay remaining after day 5 was also 6.87 days. The model had an r2 = 20.2% across individuals and 44.3% across units. Spearman's rho was = 0.494 across individuals, analogous to an r2 = 24.4%. The relative contribution of the variables used to predict ICU stay remaining after ICU day 5 is shown in Figure 4. ICU day 5 data accounted for 81.6% of the models overall explanatory power; day 5 mechanical ventilation for 41.4%, PaO2: FiO2 for 17%, other day 5 physiologic variables included in the APS score for 15.8%, and inability to assess Glasgow coma score due to sedation or paralysis for 7.4%. The remaining variables combined contributed 18.4% of the model's variation.


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)

Percentage of model variance attributable to factors used to predict intensive care unit length of stay remaining after day 5. Physiology refers to the acute physiology score (APS) and rescaled Glasgow Coma Score. Rescaled PaO2/FiO2 ratio is defined in Appendix Table 1. GCS unable refers to inability to assess Glasgow Coma score due to sedation or paralysis. Diagnostic category includes 57 mutually exclusive diagnostic groups on day 5. Miscellaneous includes age, prior length of stay, emergency surgery, and ICU readmission.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Percentage of model variance attributable to factors used to predict intensive care unit length of stay remaining after day 5. Physiology refers to the acute physiology score (APS) and rescaled Glasgow Coma Score. Rescaled PaO2/FiO2 ratio is defined in Appendix Table 1. GCS unable refers to inability to assess Glasgow Coma score due to sedation or paralysis. Diagnostic category includes 57 mutually exclusive diagnostic groups on day 5. Miscellaneous includes age, prior length of stay, emergency surgery, and ICU readmission.
Mentions: For the 12,640 patients in the development data set mean predicted remaining ICU stay after day 5 was 6.87 days and mean observed ICU stay remaining after day 5 was also 6.87 days. The model had an r2 = 20.2% across individuals and 44.3% across units. Spearman's rho was = 0.494 across individuals, analogous to an r2 = 24.4%. The relative contribution of the variables used to predict ICU stay remaining after ICU day 5 is shown in Figure 4. ICU day 5 data accounted for 81.6% of the models overall explanatory power; day 5 mechanical ventilation for 41.4%, PaO2: FiO2 for 17%, other day 5 physiologic variables included in the APS score for 15.8%, and inability to assess Glasgow coma score due to sedation or paralysis for 7.4%. The remaining variables combined contributed 18.4% of the model's variation.

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