title: Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity.
abstract: BACKGROUND
  To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months), an established risk stratification tool.
  
  
  METHODS
  This was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital's electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index.
  
  
  RESULTS
  74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5%) were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as 'requiring inpatient dialysis during index admission, and 'treatment with intravenous furosemide 40 milligrams or more' during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77-0.79) versus 0.70 (95% CI: 0.69-0.71).
  
  
  CONCLUSIONS
  Several factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity.
authors.affiliation: Department of Family Medicine & Continuing Care, Singapore General Hospital, Singapore.|Family Medicine Program, Duke-NUS Medical School, Singapore.
authors.forename: Lian Leng
authors.initials: LL
authors.lastname: Low
authors.affiliation: Health Services Research Centre, Singapore Health Services, Singapore.|Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
authors.forename: Nan
authors.initials: N
authors.lastname: Liu
authors.affiliation: Integrated Health Information Systems, Singapore.
authors.forename: Sijia
authors.initials: S
authors.lastname: Wang
authors.affiliation: Health Services Research Centre, Singapore Health Services, Singapore.|Department of Rheumatology and Immunology, Singapore General Hospital, Singapore.
authors.forename: Julian
authors.initials: J
authors.lastname: Thumboo
authors.affiliation: Department of Emergency Medicine, Singapore General Hospital, Singapore.|Health Services and Systems Research, Duke-NUS Medical School, Singapore.
authors.forename: Marcus Eng Hock
authors.initials: ME
authors.lastname: Ong
authors.affiliation: Department of Family Medicine & Continuing Care, Singapore General Hospital, Singapore.|Family Medicine Program, Duke-NUS Medical School, Singapore.
authors.forename: Kheng Hock
authors.initials: KH
authors.lastname: Lee
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D004636:Emergency Service, Hospital; D006760:Hospitalization; D006801:Humans; D007902:Length of Stay; D016015:Logistic Models; D008875:Middle Aged; D008962:Models, Theoretical; D010351:Patient Discharge; D010359:Patient Readmission; D012372:ROC Curve; D012189:Retrospective Studies; D012307:Risk Factors; D012846:Singapore