title: A prediction rule for the development of delirium among patients in medical wards: Chi-Square Automatic Interaction Detector (CHAID) decision tree analysis model.
abstract: OBJECTIVE
  To predict development of delirium among patients in medical wards by a Chi-Square Automatic Interaction Detector (CHAID) decision tree model.
  
  
  METHODS
  This was a retrospective cohort study of all adult patients admitted to medical wards at a large community hospital. The subject patients were randomly assigned to either a derivation or validation group (2:1) by computed random number generation. Baseline data and clinically relevant factors were collected from the electronic chart. Primary outcome was the development of delirium during hospitalization. All potential predictors were included in a forward stepwise logistic regression model. CHAID decision tree analysis was also performed to make another prediction model with the same group of patients. Receiver operating characteristic curves were drawn, and the area under the curves (AUCs) were calculated for both models. In the validation group, these receiver operating characteristic curves and AUCs were calculated based on the rules from derivation.
  
  
  RESULTS
  A total of 3,570 patients were admitted: 2,400 patients assigned to the derivation group and 1,170 to the validation group. A total of 91 and 51 patients, respectively, developed delirium. Statistically significant predictors were delirium history, age, underlying malignancy, and activities of daily living impairment in CHAID decision tree model, resulting in six distinctive groups by the level of risk. AUC was 0.82 in derivation and 0.82 in validation with CHAID model and 0.78 in derivation and 0.79 in validation with logistic model.
  
  
  CONCLUSIONS
  We propose a validated CHAID decision tree prediction model to predict the development of delirium among medical patients.
authors.affiliation: Division of General Internal Medicine, Department of Medicine, St. Luke's International Hospital, Tokyo, Japan. Electronic address: daikoba@luke.or.jp.
authors.forename: Daiki
authors.initials: D
authors.lastname: Kobayashi
authors.forename: Osamu
authors.initials: O
authors.lastname: Takahashi
authors.forename: Hiroko
authors.initials: H
authors.lastname: Arioka
authors.forename: Shinichiro
authors.initials: S
authors.lastname: Koga
authors.forename: Tsuguya
authors.initials: T
authors.lastname: Fukui
mesh_terms: D000368:Aged; D016009:Chi-Square Distribution; D003663:Decision Trees; D003693:Delirium / Q000175:diagnosis*; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006764:Hospitals, Community; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010362:Patients' Rooms; D012372:ROC Curve; D012189:Retrospective Studies; D012307:Risk Factors
keywords: CHAID; Delirium; prediction rule