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Author Liu, Nan ♦ Wang, Sijia ♦ Tan, Shu Yun ♦ Lee, Kheng Hock ♦ Thumboo, Julian ♦ Low, Lian Leng
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Hindawi Limited
File Format HTM / HTML
Date Created 2015-12-07
Copyright Year ©2015
Language English
Subject Domain (in LCC) R
Subject Keyword Medicine
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.
ISSN 23146133
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2015-01-01
e-ISSN 23146133
Journal BioMed Research International
Volume Number 2015

Source: Directory of Open Access Journals (DOAJ)