title: Mortality among patients admitted to strained intensive care units.
abstract: BACKGROUND
  The aging population may strain intensive care unit (ICU) capacity and adversely affect patient outcomes. Existing fluctuations in demand for ICU care offer an opportunity to explore such relationships.
  
  
  OBJECTIVE
  To determine whether transient increases in ICU strain influence patient mortality, and to identify characteristics of ICUs that are resilient to surges in capacity strain.
  
  
  METHODS
  Retrospective cohort study of 264,401 patients admitted to 155 U.S. ICUs from 2001 to 2008. We used logistic regression to examine relationships of measures of ICU strain (census, average acuity, and proportion of new admissions) near the time of ICU admission with mortality.
  
  
  RESULTS
  A total of 36,465 (14%) patients died in the hospital. ICU census on the day of a patient's admission was associated with increased mortality (odds ratio [OR], 1.02 per standardized unit increase; 95% confidence interval [CI]: 1.00, 1.03). This effect was greater among ICUs employing closed (OR, 1.07; 95% CI: 1.02, 1.12) versus open (OR, 1.01; 95% CI: 0.99, 1.03) physician staffing models (interaction P value = 0.02). The relationship between census and mortality was stronger when the census was composed of higher acuity patients (interaction P value < 0.01). Averaging strain over the first 3 days of patients' ICU stays yielded similar results except that the proportion of new admissions was now also associated with mortality (OR, 1.04 for each 10% increase; 95% CI: 1.02, 1.06).
  
  
  CONCLUSIONS
  Several sources of ICU strain are associated with small but potentially important increases in patient mortality, particularly in ICUs employing closed staffing models. Although closed ICUs may promote favorable outcomes under static conditions, they are susceptible to being overwhelmed by patient influxes.
authors.affiliation: 1 Center for Clinical Epidemiology and Biostatistics.
authors.forename: Nicole B
authors.initials: NB
authors.lastname: Gabler
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: Ratcliffe
authors.forename: Jason
authors.initials: J
authors.lastname: Wagner
authors.forename: David A
authors.initials: DA
authors.lastname: Asch
authors.forename: Gordon D
authors.initials: GD
authors.lastname: Rubenfeld
authors.forename: Derek C
authors.initials: DC
authors.lastname: Angus
authors.forename: Scott D
authors.initials: SD
authors.lastname: Halpern
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008505:Medical Staff, Hospital / Q000458:organization & administration* / Q000706:statistics & numerical data; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D014481:United States / Q000453:epidemiology; D000078329:Workforce