Optimal timing of transfer out of the intensive care unit

Am J Crit Care. 2013 Sep;22(5):390-7. doi: 10.4037/ajcc2013973.

Abstract

Background: Little other than subjective judgment is available to help clinicians determine when a patient should be transferred out of the intensive care unit.

Objective: To assess whether remaining in the intensive care unit longer than judged to be medically necessary is associated with increased 30-day mortality.

Methods: This prospective, observational cohort study was performed in a 13-bed, closed-model, adult medical intensive care unit of a county-owned, university-affiliated hospital that often has difficulty transferring patients to general care areas because of a lack of available beds. Analysis included all 2401 survivors of intensive care from the study period. Delay in discharge from the intensive care unit was defined as time elapsed between the request for transfer and the actual transfer. Logistic regression was used to assess the association of discharge delay with 30-day mortality, adjusting for demographics, comorbid conditions, type and severity of acute illness, care limitations in the unit, and other potential confounding variables. Nonlinear relationships with continuous variables were modeled with restricted cubic splines.

Results: Overall, 30-day mortality was 10.1%. Mean discharge delay was 9.6 (SD, 11.7) hours; 9.9% had a discharge delay exceeding 24 hours. The relationship of 30-day mortality to discharge delay was statistically significant and U-shaped, with the nadir at 20 hours.

Conclusions: These data indicate an optimal time window for patients to leave the intensive care unit, with increased mortality not only if they leave earlier but also if they leave later than this optimal timing.

Publication types

  • Observational Study

MeSH terms

  • Academic Medical Centers
  • Adult
  • Aged
  • Cohort Studies
  • Female
  • Humans
  • Intensive Care Units / organization & administration*
  • Length of Stay / statistics & numerical data*
  • Logistic Models
  • Male
  • Middle Aged
  • Mortality*
  • Ohio
  • Patient Transfer / organization & administration*
  • Prospective Studies
  • Time Factors