Please use this identifier to cite or link to this item: https://doi.org/10.1155/2015/169870
Title: Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore
Authors: Low, L.L
Lee, K.H 
Hock Ong, M.E
Wang, S
Tan, S.Y 
Thumboo, J 
Liu, N 
Keywords: C reactive protein
creatinine
serum albumin
sodium
adult
albumin blood level
Article
Charlson Comorbidity Index
comparative study
controlled study
creatinine blood level
emergency ward
female
general practice
health care utilization
hospital readmission
human
ICD-10
internal medicine
lace index
length of stay
leukocyte count
logistic regression analysis
machine learning
major clinical study
male
middle aged
prediction
regression analysis
Singapore
sodium blood level
statistical analysis
Canada
comorbidity
hospital discharge
hospital emergency service
hospital readmission
risk factor
statistical model
statistics and numerical data
theoretical model
time factor
Canada
Comorbidity
Emergency Service, Hospital
Female
Humans
Length of Stay
Logistic Models
Male
Middle Aged
Models, Theoretical
Patient Discharge
Patient Readmission
Risk Factors
Singapore
Time Factors
Issue Date: 2015
Citation: Low, L.L, Lee, K.H, Hock Ong, M.E, Wang, S, Tan, S.Y, Thumboo, J, Liu, N (2015). Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore. BioMed Research International 2015 : 169870. ScholarBank@NUS Repository. https://doi.org/10.1155/2015/169870
Rights: Attribution 4.0 International
Abstract: The LACE index (length of stay, acuity of admission, Charlson comorbidity index, CCI, and number of emergency department visits in preceding 6 months) derived in Canada is simple and may have clinical utility in Singapore to predict readmission risk. We compared the performance of the LACE index with a derived model in identifying 30-day readmissions from a population of general medicine patients in Singapore. Additional variables include patient demographics, comorbidities, clinical and laboratory variables during the index admission, and prior healthcare utilization in the preceding year. 5,862 patients were analysed and 572 patients (9.8%) were readmitted in the 30 days following discharge. Age, CCI, count of surgical procedures during index admission, white cell count, serum albumin, and number of emergency department visits in previous 6 months were significantly associated with 30-day readmission risk. The final logistic regression model had fair discriminative ability c-statistic of 0.650 while the LACE index achieved c-statistic of 0.628 in predicting 30-day readmissions. Our derived model has the advantage of being available early in the admission to identify patients at high risk of readmission for interventions. Additional factors predicting readmission risk and machine learning techniques should be considered to improve model performance. © 2015 Lian Leng Low et al.
Source Title: BioMed Research International
URI: https://scholarbank.nus.edu.sg/handle/10635/183593
ISSN: 23146133
DOI: 10.1155/2015/169870
Rights: Attribution 4.0 International
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