title: Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data.
abstract: OBJECTIVE
  Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations.
  
  
  METHODS
  The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation.
  
  
  RESULTS
  The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78; 95% confidence interval (CI) 0.71-0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72; 95% CI 0.66-0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission.
  
  
  CONCLUSIONS
  Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions.
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Locked Bag 20000, Geelong, Vic. 3220, Australia. Email: ; ; ;
authors.forename: Santu
authors.initials: S
authors.lastname: Rana
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Locked Bag 20000, Geelong, Vic. 3220, Australia. Email: ; ; ;
authors.forename: Truyen
authors.initials: T
authors.lastname: Tran
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Locked Bag 20000, Geelong, Vic. 3220, Australia. Email: ; ; ;
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Locked Bag 20000, Geelong, Vic. 3220, Australia. Email: ; ; ;
authors.forename: Dinh
authors.initials: D
authors.lastname: Phung
authors.affiliation: School of Medicine, Deakin University, Locked Bag 20000, Geelong, Vic. 3220, Australia. Email:
authors.forename: Richard L
authors.initials: RL
authors.lastname: Kennedy
authors.affiliation: Centre for Pattern Recognition and Data Analytics, Deakin University, Locked Bag 20000, Geelong, Vic. 3220, Australia. Email: ; ; ;
authors.forename: Svetha
authors.initials: S
authors.lastname: Venkatesh
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016208:Databases, Factual; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D062606:Tertiary Care Centers; D014739:Victoria; D055815:Young Adult