title: An electronic medical record-based model to predict 30-day risk of readmission and death among HIV-infected inpatients.
abstract: BACKGROUND
  Readmission after hospitalization is costly, time-consuming, and remains common among HIV-infected individuals. We sought to use data from the Electronic Medical Record (EMR) to create a clinical, robust, multivariable model for predicting readmission risk in hospitalized HIV-infected patients.
  
  
  METHODS
  We extracted clinical and nonclinical data from the EMR of HIV-infected patients admitted to a large urban hospital between March 2006 and November 2008. These data were used to build automated predictive models for 30-day risk of readmission and death.
  
  
  RESULTS
  We identified 2476 index admissions among HIV-infected inpatients who were 73% males, 57% African American, with a mean age of 43 years. One-quarter were readmitted, and 3% died within 30 days of discharge. Those with a primary diagnosis during the index admission of HIV/AIDS accounted for the largest proportion of readmissions (41%), followed by those initially admitted for other infections (10%) or for oncologic (6%), pulmonary (5%), gastrointestinal (4%), and renal (3%) causes. Factors associated with readmission risk include: AIDS defining illness, CD4 ≤ 92, laboratory abnormalities, insurance status, homelessness, distance from the hospital, and prior emergency department visits and hospitalizations (c = 0.72; 95% confidence interval: 0.70 to 0.75). The multivariable predictors of death were CD4 < 132, abnormal liver function tests, creatinine >1.66, and hematocrit <30.8 (c = 0.79; 95% confidence interval: 0.74 to 0.84) for death.
  
  
  CONCLUSIONS
  Readmission rates among HIV-infected patients were high. An automated model composed of factors accessible from the EMR in the first 48 hours of admission performed well in predicting the 30-day risk of readmission among HIV patients. Such a model could be used in real-time to identify HIV patients at highest risk so readmission prevention resources could be targeted most efficiently.
authors.affiliation: Department of Medicine, Division of Infectious Diseases, University of Texas Southwestern Medical Center, Center for Clinical Innovation, Parkland Health and Hospital System, Dallas, TX 75235, USA. ank.nijhawan@utsouthwestern.edu
authors.forename: Ank E
authors.initials: AE
authors.lastname: Nijhawan
authors.forename: Christopher
authors.initials: C
authors.lastname: Clark
authors.forename: Richard
authors.initials: R
authors.lastname: Kaplan
authors.forename: Billy
authors.initials: B
authors.lastname: Moore
authors.forename: Ethan A
authors.initials: EA
authors.lastname: Halm
authors.forename: Ruben
authors.initials: R
authors.lastname: Amarasingham
mesh_terms: D000328:Adult; D057286:Electronic Health Records; D005260:Female; D015658:HIV Infections / Q000401:mortality / Q000628:therapy*; D006786:Hospitals, Urban / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D015233:Models, Statistical; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012307:Risk Factors; D013781:Texas