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Title: Predicting 30-day readmissions in an Asian population: Building a predictive model by incorporating markers of hospitalization severity
Authors: Low L.L. 
Liu N. 
Wang S. 
Thumboo J. 
Ong M.E.H. 
Lee K.H. 
Keywords: furosemide
area under the curve
Charlson Comorbidity Index
clinical outcome
cohort analysis
controlled study
diagnostic test accuracy study
disease severity
emergency ward
health care utilization
hospital admission
hospital discharge
hospital patient
hospital readmission
intermethod comparison
LACE index
length of stay
major clinical study
patient acuity
predictive value
receiver operating characteristic
retrospective study
risk assessment
sensitivity and specificity
social determinants of health
tertiary care center
validation study
hospital emergency service
middle aged
risk factor
statistical model
theoretical model
very elderly
Aged, 80 and over
Emergency Service, Hospital
Length of Stay
Logistic Models
Middle Aged
Models, Theoretical
Patient Discharge
Patient Readmission
Retrospective Studies
Risk Factors
ROC Curve
Issue Date: 2016
Publisher: Public Library of Science
Citation: Low L.L., Liu N., Wang S., Thumboo J., Ong M.E.H., Lee K.H. (2016). Predicting 30-day readmissions in an Asian population: Building a predictive model by incorporating markers of hospitalization severity. PLoS ONE 11 (12) : e0167413. ScholarBank@NUS Repository.
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. Method: 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). Conclusion: Several factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity. © 2016 Low et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Source Title: PLoS ONE
ISSN: 19326203
DOI: 10.1371/journal.pone.0167413
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