title: A trial of a real-time alert for clinical deterioration in patients hospitalized on general medical wards.
abstract: BACKGROUND
  With limited numbers of intensive care unit (ICU) beds available, increasing patient acuity is expected to contribute to episodes of inpatient deterioration on general wards.
  
  
  OBJECTIVE
  To prospectively validate a predictive algorithm for clinical deterioration in general-medical ward patients, and to conduct a trial of real-time alerts based on this algorithm.
  
  
  METHODS
  Randomized, controlled crossover study.
  
  
  METHODS
  Academic center with patients hospitalized on 8 general wards between July 2007 and December 2011.
  
  
  METHODS
  Real-time alerts were generated by an algorithm designed to predict the need for ICU transfer using electronically available data. The alerts were sent by text page to the nurse manager on intervention wards.
  
  
  METHODS
  Intensive care unit transfer, hospital mortality, and hospital length of stay.
  
  
  RESULTS
  Patients meeting the alert threshold were at nearly 5.3-fold greater risk of ICU transfer (95% confidence interval [CI]: 4.6-6.0) than those not satisfying the alert threshold (358 of 2353 [15.2%] vs 512 of 17678 [2.9%]). Patients with alerts were at 8.9-fold greater risk of death (95% CI: 7.4-10.7) than those without alerts (244 of 2353 [10.4%] vs 206 of 17678 [1.2%]). Among patients identified by the early warning system, there were no differences in the proportion of patients who were transferred to the ICU or who died in the intervention group as compared with the control group.
  
  
  CONCLUSIONS
  Real-time alerts were highly specific for clinical deterioration resulting in ICU transfer and death, and were associated with longer hospital length of stay. However, an intervention notifying a nurse of the risk did not result in improvement in these outcomes.
authors: Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO 63110, USA. tbailey@dom.wustl.edu
authors: Thomas C
authors: TC
authors: Bailey
authors: Yixin
authors: Y
authors: Chen
authors: Yi
authors: Y
authors: Mao
authors: Chenyang
authors: C
authors: Lu
authors: Gregory
authors: G
authors: Hackmann
authors: Scott T
authors: ST
authors: Micek
authors: Kevin M
authors: KM
authors: Heard
authors: Kelly M
authors: KM
authors: Faulkner
authors: Marin H
authors: MH
authors: Kollef
mesh_terms: D018806:APACHE; D000046:Academic Medical Centers / Q000639:trends*; D000328:Adult; D000368:Aged; D000465:Algorithms; D003199:Computer Systems / Q000639:trends*; D018592:Cross-Over Studies; D005260:Female; D006748:Hospital Departments / Q000639:trends; D017052:Hospital Mortality / Q000639:trends; D006760:Hospitalization / Q000639:trends*; D006801:Humans; D007362:Intensive Care Units / Q000639:trends*; D007902:Length of Stay / Q000639:trends; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012189:Retrospective Studies