title: Comparing performance of 30-day readmission risk classifiers among hospitalized primary care patients.
abstract: OBJECTIVE
  Hospital readmission within 30 days of discharge occurs in almost 20% of US Medicare patients and may be a marker of poor quality inpatient care, ineffective hospital to home transitions, or disease severity. Within a patient centered medical home, care transition interventions may only be practical from cost and staffing perspectives if targeted at patients with the greatest risk of readmission. Various scoring algorithms attempt to predict patients at risk for 30-day readmission, but head-to-head comparison of performance is lacking. Compare published scoring algorithms which use generally available electronic medical record data on the same set of hospitalized primary care patients.
  
  
  METHODS
  The LACE index, the LACE+ index, the HOSPITAL score, and the readmission risk score were computed on a consecutive cohort of 26,278 hospital admissions. Classifier performance was assessed by plotting receiver operating characteristic curves comparing the computed score with the actual outcome of death or readmission within 30 days. Statistical significance of differences in performance was assessed using bootstrapping techniques.
  
  
  RESULTS
  Correct readmission classification on this cohort was moderate with the following c-statistics: Readmission risk score 0.666; LACE 0.680; LACE+ 0.662; and HOSPITAL 0.675. There was no statistically significant difference in performance between classifiers.
  
  
  CONCLUSIONS
  Logistic regression based classifiers yield only moderate performance when utilized to predict 30-day readmissions. The task is difficult due to the variety of underlying causes for readmission, nonlinearity, and the arbitrary time period of concern. More sophisticated classification techniques may be necessary to increase performance and allow patient centered medical homes to effectively focus efforts to reduce readmissions.
authors.affiliation: Department of Family Medicine, Mayo Clinic, Rochester, USA.
authors.forename: Gregory M
authors.initials: GM
authors.lastname: Garrison
authors.affiliation: Department of Family Medicine, Mayo Clinic, Rochester, USA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Robelia
authors.affiliation: Department of Family Medicine, Mayo Clinic, Rochester, USA.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Pecina
authors.affiliation: Department of Medicine, Mayo Clinic Florida, Jacksonville, USA.
authors.forename: Nancy L
authors.initials: NL
authors.lastname: Dawson
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007902:Length of Stay; D016015:Logistic Models; D008297:Male; D006278:Medicare; D008875:Middle Aged; D010359:Patient Readmission / Q000706:statistics & numerical data*; D018802:Patient-Centered Care / Q000458:organization & administration* / Q000592:standards; D012372:ROC Curve; D012307:Risk Factors; D012720:Severity of Illness Index; D012737:Sex Factors; D012959:Socioeconomic Factors; D014481:United States; D055815:Young Adult
keywords: decision support techniques; hospital medicine; patient readmission; primary health care