title: Real-time automated clinical deterioration alerts predict thirty-day hospital readmission.
abstract: Clinical deterioration alerts (CDAs) are increasingly employed to identify deteriorating patients.
  
  
  
  We performed a retrospective study to determine whether CDAs predict 30-day readmission. Patients admitted to 8 general medicine units were assessed for all-cause 30-day readmission.
  
  
  
  Among 3015 patients, 567 (18.8%) were readmitted within 30 days. Patients triggering a CDA (n = 1141; 34.4%) were more likely to have a 30-day readmission (23.6% vs 15.9%; P < 0.001). Logistic regression identified triggering of a CDA to be independently associated with 30-day readmission (odds ratio [OR]: 1.40; 95% confidence interval [CI]: 1.26-1.55; P = 0.001). Other predictors were: an emergency department visit in the previous 6 months (OR: 1.23; 95% CI:, 1.20-1.26; P < 0.001), increasing age (OR: 1.01; 95% CI: 1.01-1.02; P = 0.003), presence of connective tissue disease (OR: 1.63; 95% CI: 1.34-1.98; P = 0.012), diabetes mellitus with end-organ complications (OR: 1.23; 95% CI: 1.13-1.33; P = 0.010), chronic renal disease (OR: 1.16; 95% CI: 1.08-1.24; P = 0.034), cirrhosis (OR: 1.25; 95% CI: 1.17-1.33; P < 0.001), and metastatic cancer (OR: 1.12; 95% CI: 1.08-1.17; P = 0.002). Addition of the CDA to the other predictors added only modest incremental value for the prediction of hospital readmission.
  
  
  
  Readily identifiable clinical variables can be identified that predict 30-day readmission. It may be important to include these variables in existing prediction tools if pay for performance and across-institution comparisons are to be "fair" to institutions that care for more seriously ill patients. Journal of Hospital Medicine 2016;11:768-772. © 2016 Society of Hospital Medicine.
authors.affiliation: Department of Pharmacy Practice, St. Louis College of Pharmacy, St. Louis, Missouri.
authors.forename: Scott T
authors.initials: ST
authors.lastname: Micek
authors.affiliation: Department of Internal Medicine, Washington University, St. Louis, Missouri.
authors.forename: Maanasi
authors.initials: M
authors.lastname: Samant
authors.affiliation: Division of Infectious Diseases, Washington University School of Medicine, St. Louis, Missouri.
authors.forename: Thomas
authors.initials: T
authors.lastname: Bailey
authors.affiliation: School of Engineering and Applied Sciences, Washington University in St. Louis, St. Louis, Missouri.
authors.forename: Yixin
authors.initials: Y
authors.lastname: Chen
authors.affiliation: School of Engineering and Applied Sciences, Washington University in St. Louis, St. Louis, Missouri.
authors.forename: Chenyang
authors.initials: C
authors.lastname: Lu
authors.affiliation: Center for Clinical Excellence, BJC HealthCare, St. Louis, Missouri.
authors.forename: Kevin
authors.initials: K
authors.lastname: Heard
authors.affiliation: Division of Pulmonary and Critical Care Medicine, Washington University School of Medicine, St. Louis, Missouri. mkollef@dom.wustl.edu.
authors.forename: Marin H
authors.initials: MH
authors.lastname: Kollef
mesh_terms: D000367:Age Factors; D000465:Algorithms; D000075902:Clinical Deterioration; D005260:Female; D006761:Hospitals; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D012307:Risk Factors