Intensive care unit occupancy and patient outcomes

Crit Care Med. 2009 May;37(5):1545-57. doi: 10.1097/CCM.0b013e31819fe8f8.

Abstract

Principle: Although intensive care units (ICUs) with higher overall patient volume may achieve better outcomes than lower volume ICUs, there are few data on the effects of increasing patient loads on patients within the ICU.

Objectives: To examine the association of ICU occupancy with the patient outcomes within the same ICU.

Methods: We examined 200,499 patients in 108 ICUs using the Acute Physiology and Chronic Health Evaluation IV database in 2002-2005. Daily census on the day of admission was determined for each patient and defined in relation to the mean census. We used conditional logistic regression to compare inpatient outcomes of patients admitted on high census days to those admitted in the same ICU on low census days. We controlled for severity of illness at the patient level using data on clinical, demographic, and physiologic variables on admission to the ICU.

Measurements and main results: Patients admitted on high census days had the same odds of inpatient mortality or transfer to another hospital as patients admitted on average or on low census days. These findings were robust to multiple alternative definitions of day of admission census and were confirmed in several subgroup analyses.

Conclusions: The ICUs in this data are able to function as high reliability organizations. They are able to scale up their operations to meet the needs of a wide range of operating conditions while maintaining consistent patient mortality outcomes.

Publication types

  • Multicenter Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • APACHE
  • Aged
  • Aged, 80 and over
  • Bed Occupancy*
  • Critical Care / standards
  • Critical Care / trends
  • Critical Illness / mortality*
  • Critical Illness / therapy
  • Female
  • Health Care Surveys
  • Hospital Mortality / trends*
  • Humans
  • Intensive Care Units / statistics & numerical data*
  • Length of Stay
  • Linear Models
  • Logistic Models
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Patient Admission / statistics & numerical data
  • Patient Discharge / statistics & numerical data
  • Probability
  • Quality of Health Care
  • Risk Management
  • United States
  • Workload / statistics & numerical data*