title: Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk.
abstract: BACKGROUND
  This paper explores the importance of electronic medical records (EMR) for predicting 30-day all-cause non-elective readmission risk of patients and presents a comparison of prediction performance of commonly used methods.
  
  
  METHODS
  The data are extracted from eight Advocate Health Care hospitals. Index admissions are excluded from the cohort if they are observation, inpatient admissions for psychiatry, skilled nursing, hospice, rehabilitation, maternal and newborn visits, or if the patient expires during the index admission. Data are randomly and repeatedly divided into fitting and validating sets for cross validations. Approaches including LACE, STEPWISE logistic, LASSO logistic, and AdaBoost, are compared with sample sizes varying from 2,500 to 80,000.
  
  
  RESULTS
  Our results confirm that LACE has moderate discrimination power with the area under receiver operating characteristic curve (AUC) around 0.65-0.66, which can be improved to 0.73-0.74 when additional variables from EMR are considered. These variables include Inpatient in the last six months, Number of emergency room visits or inpatients in the last year, Braden score, Polypharmacy, Employment status, Discharge disposition, Albumin level, and medical condition variables such as Leukemia, Malignancy, Renal failure with hemodialysis, History of alcohol substance abuse, Dementia and Trauma. When sample size is small (≤5000), LASSO is the best; when sample size is large (≥20,000), the predictive performance is similar. The STEPWISE method has a slightly lower AUC (0.734) comparing to LASSO (0.737) and AdaBoost (0.737). More than one half of the selected predictors can be false positives when using a single method and a single division of fitting/validating data.
  
  
  CONCLUSIONS
  True predictors can be identified by repeatedly dividing data into fitting/validating subsets and referring the final model based on summarizing results. LASSO is a better alternative to the STEPWISE logistic regression, especially when sample size is not large. The evidence for adequate sample size can be explored by fitting models on gradually reduced samples. Our model comparison strategy is not only good for 30-day all-cause non-elective readmission risk predictions, but also applicable to other types of predictive models in clinical studies.
authors.affiliation: Advocate Health Care, 3075 Highland Parkway, Downers Grove, IL, 60515, USA. liping.tong@advocatehealth.com.
authors.forename: Liping
authors.initials: L
authors.lastname: Tong
authors.affiliation: Cerner Corporation, World Headquarters, 2800 Rockcreek Parkway, North Kansas City, MO, 64117, USA. Cole.Erdmann@Cerner.com.
authors.forename: Cole
authors.initials: C
authors.lastname: Erdmann
authors.affiliation: Cerner Corporation, World Headquarters, 2800 Rockcreek Parkway, North Kansas City, MO, 64117, USA. Marina.Daldalian@Cerner.com.
authors.forename: Marina
authors.initials: M
authors.lastname: Daldalian
authors.affiliation: Cerner Corporation, World Headquarters, 2800 Rockcreek Parkway, North Kansas City, MO, 64117, USA. Jing.Li@Cerner.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Li
authors.affiliation: Advocate Health Care, 3075 Highland Parkway, Downers Grove, IL, 60515, USA. tina.esposito@advocatehealth.com.
authors.forename: Tina
authors.initials: T
authors.lastname: Esposito
mesh_terms: D019540:Area Under Curve; D002641:Chicago; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D015994:Incidence; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018570:Risk Assessment; D018401:Sample Size; D013997:Time Factors; D014505:Urban Population