Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2015 (2015), Article ID 169870, 6 pages
http://dx.doi.org/10.1155/2015/169870
Research Article

Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore

1Department of Family Medicine & Continuing Care, Singapore General Hospital, Academia Level 4, 20 College Road, Singapore 169856
2Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore 169608
3Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore
4Integrated Health Information Systems, Singapore
5Department of Rheumatology & Immunology, Singapore General Hospital, Singapore
6Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore

Received 30 August 2015; Accepted 3 November 2015

Academic Editor: Fabrizio Montecucco

Copyright © 2015 Lian Leng Low et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.